diff --git "a/6553.jsonl" "b/6553.jsonl"
new file mode 100644--- /dev/null
+++ "b/6553.jsonl"
@@ -0,0 +1,624 @@
+{"seq_id":"457115308","text":"# Time:  O(n)\n# Space: O(1)\n\n'''\nGiven a binary array, find the maximum number of consecutive 1s in this array if you can flip at most one 0.\n\nExample 1:\n\nInput: [1,0,1,1,0]\nOutput: 4\nExplanation: Flip the first zero will get the the maximum number of consecutive 1s.\n    After flipping, the maximum number of consecutive 1s is 4.\nNote:\n\nThe input array will only contain 0 and 1.\nThe length of input array is a positive integer and will not exceed 10,000\n'''\n\nclass Solution(object):\n    def findMaxConsecutiveOnes(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        result, prev, curr = 0, 0, 0\n        for n in nums:\n            if n == 0:\n                prev, curr = curr, 0\n            else:\n                curr += 1\n            result = max(result, prev + curr + 1)\n        return min(result, len(nums))  # for all 1 array edge case\n","sub_path":"Python/max-consecutive-ones-ii.py","file_name":"max-consecutive-ones-ii.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"19700103","text":"import pandas as pd\nimport walter\nfrom walter import df_frame\n\"\"\"\ncolumns:  'time_stamp', 'action', 'product_credited', 'product_cred_quantity', 'product_cred_case?',\n'product_cred_unit_price', 'product_cred_total', 'debtor_name'\n\"\"\"\n#Study of credits given out\ncredit_df = df_frame[['time_stamp', 'action', 'product_credited', 'product_cred_quantity',\n                               'product_cred_case?', 'product_cred_unit_price', 'product_cred_total',\n                               'debtor_name']]\n\n#analyse the action column where the action is to give out credits\ncred_action = credit_df[credit_df['action'] == 'Ideni'].reset_index(0)\n\n#Change the dtype in total purchase from object to int\ncred_total_col = cred_action['product_cred_total']\ncred_total_col = pd.to_numeric(cred_total_col)\n\n#Get the total business credit on products\n#These are all credits that the business offered to its clients\ntotal_credits = cred_total_col.sum()\nprint('THE TOTAL BUSINESS CREDITS:{}RWF'.format(total_credits))\n","sub_path":"chezwalter/creditout.py","file_name":"creditout.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"529599552","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Dec 23 13:39:39 2017\r\n\r\n@author: shiro\r\n\"\"\"\r\nimport re\r\nimport numpy as np\r\nimport csv\r\n\r\n'''\r\n    functions where we preprocess the data so as to save them in a file corrected\r\n'''\r\ndef clean_str(string, tolower=True):\r\n    \"\"\"\r\n    Tokenization/string cleaning.\r\n    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\r\n    \"\"\"\r\n    # Different regex parts for smiley faces\r\n    happy =\t[':-\\)', ':\\)', '\\(:', '\\(-:' ] \r\n    laugh =\t[':-d', ':d', 'x-d', 'xd', 'xd' ]\r\n    love=\t['<3', ':\\*' ]\r\n    wink = [';-\\)', ';\\)', ';-d', ';d', '\\(;', '\\(-;', ] \r\n    sad  = [':-\\(', ':\\(' , '\\):', '\\)-:', ':,\\(', ':\\'\\(', ':/',  ':\"\\(', ':\\(\\(', 't.t', ':-\\|'] \r\n    surprise = [':o']\r\n\r\n    special = [\",\", \":\", \"\\\"\", \"=\", \"&\", \";\", \"%\", \"$\",\"@\", \"%\", \"^\", \"*\",\r\n               \"(\", \")\", \"{\", \"}\", \"[\", \"]\", \"|\", \"/\", \"\\\\\", \">\", \"<\", \"-\",\r\n               \"!\", \"?\", \".\", \"'\", \"--\", \"---\", \"#\"]\r\n    if tolower:\r\n        string = string.lower()\r\n    # contraction\r\n    string = re.sub(r\"!\", \" ! \", string)\r\n    string = re.sub(r\"\\'s\", \" \\'s\", string)\r\n    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\r\n    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\r\n    string = re.sub(r\"\\'re\", \" \\'re\", string)\r\n    string = re.sub(r\"\\'d\", \" \\'d\", string)\r\n    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\r\n    string = re.sub(r\",\", \" , \", string)\r\n    string = re.sub(r\"\\n\", \"\", string)\r\n    string = re.sub(' +',' ', string)\r\n    # tweet\r\n    string = re.sub(r'\\@[^\\s]+[\\s]?', \"<USER> \", string) # replace user @ by <user>\r\n    string = re.sub(r'\\#[^\\s]+[\\s]?', \"<HASHTAG>\", string) # remove hashtag\r\n    string = re.sub(r\"\\$[\\d]+(\\.)?[\\d]*\", \"<PRICE>\", string) # float and int prince\r\n    string = re.sub(r\"http.?://[^\\s]+[\\s]?\", \"<WEBSITE>\", string) # detect url\r\n    string = re.sub(r\"[\\d]+:[\\d]+\", \"<TIME> \", string) # detect TIME\r\n    string = re.sub(r\"[\\d]+:[\\d]+:[\\d]+\", \"<TIME> \", string) # detect TIME\r\n    string = re.sub(r\"[\\d]+h[\\d]+\", \"<TIME> \", string) # detect TIME\r\n    string = re.sub(r\"[\\d]+\", \"<NUMBER> \", string) # detect number\r\n     # detect emoticonne\r\n    for emo in happy:\r\n        string = re.sub(emo, \"<HAPPY> \", string) # happy\r\n    for emo in laugh:\r\n        string = re.sub(emo, \"<LAUGH> \", string) # laugh\r\n    for emo in love:\r\n        string = re.sub(emo, \"<LOVE> \", string) # love\r\n    for emo in wink:\r\n        string = re.sub(emo, \"<LAUGH> \", string) # wink\r\n    for emo in sad:\r\n        #print(emo)\r\n        string = re.sub(emo, \"<SAD> \", string) # sad\r\n    for emo in surprise:\r\n        string = re.sub(emo, \"<SURPRISE> \", string) # sad\r\n#        \r\n#    # remove special characters\r\n    string = re.sub(r\"[^A-Za-z0-9,!?.\\'\\`]<>\", \" \", string) #()\r\n    #string = re.sub(r\"\\.{1,1}\", \".\", string)\r\n    string = re.sub(r\"\\?\", \" \\? \", string)\r\n    string = re.sub(r\"\\s{2,}\", \" \", string)\r\n    string = re.sub(r\"\\\\\", \" \", string)\r\n    string = re.sub(r\"\\s{2,}\", \" \", string)\r\n    string = re.sub(r\"\\\\\", \" \", string)\r\n    string.replace('...', ' ... ')\r\n    string = re.sub(' +',' ', string)\r\n    \r\n#    \r\n#    string = re.sub(r\"\\(\", \" \\( \", string)\r\n#    string = re.sub(r\"\\)\", \" \\) \", string)\r\n    #for s in special:\r\n    #    string.replace(s, ' ')\r\n    \r\n    return string.strip()\r\n    \r\ndef clean_str2(string, tolower=True):\r\n    \"\"\"\r\n    Tokenization/string cleaning.\r\n    Original taken from https://github.com/yoonkim/CNN_sentence/blob/master/process_data.py\r\n    \"\"\"\r\n    # Different regex parts for smiley faces\r\n    happy =\t[':-\\)', ':\\)', '\\(:', '\\(-:' ] \r\n    laugh =\t[':-d', ':d', 'x-d', 'xd', 'xd' ]\r\n    love=\t['<3', ':\\*' ]\r\n    wink = [';-\\)', ';\\)', ';-d', ';d', '\\(;', '\\(-;', ] \r\n    sad  = [':-\\(', ':\\(' , '\\):', '\\)-:', ':,\\(', ':\\'\\(', ':/',  ':\"\\(', ':\\(\\(', 't.t', ':-\\|'] \r\n    surprise = [':o']\r\n\r\n    special = [\",\", \":\", \"\\\"\", \"=\", \"&\", \";\", \"%\", \"$\",\"@\", \"%\", \"^\", \"*\",\r\n               \"(\", \")\", \"{\", \"}\", \"[\", \"]\", \"|\", \"/\", \"\\\\\", \">\", \"<\", \"-\",\r\n               \"!\", \"?\", \".\", \"'\", \"--\", \"---\", \"#\"]\r\n    \r\n    # contraction\r\n    string = re.sub(r\"!\", \" ! \", string)\r\n    string = re.sub(r\"\\'s\", \" \\'s\", string)\r\n    string = re.sub(r\"\\'ve\", \" \\'ve\", string)\r\n    string = re.sub(r\"n\\'t\", \" n\\'t\", string)\r\n    string = re.sub(r\"\\'re\", \" \\'re\", string)\r\n    string = re.sub(r\"\\'d\", \" \\'d\", string)\r\n    string = re.sub(r\"\\'ll\", \" \\'ll\", string)\r\n    string = re.sub(r\",\", \" , \", string)\r\n    string = re.sub(r\"\\n\", \"\", string)\r\n    string = re.sub('\\.{2,100}',' ...', string) # take in account triple points\r\n    string = re.sub(' +',' ', string)\r\n    \r\n    \r\n    return string.strip()\r\n    \r\ndef loadTextsAndDelete(filename, limit=-1):\r\n    '''\r\n        load dataset and remove feature useless and remove useless \r\n        characters like triple space etc\r\n    '''\r\n    f = open(filename, encoding=\"utf8\")\r\n    dataset=[line.split('\\t') for line in f]\r\n    dataset.pop(0)\r\n    for i, data in enumerate(dataset):\r\n        \r\n        if i%100000==0:\r\n            print(i)\r\n        try:\r\n            data.remove('\\n')\r\n        except:\r\n            pass\r\n        data.pop(0)\r\n        data.pop(1)\r\n        data[1] = clean_str2(data[1])\r\n    return dataset\r\n    \r\ndef loadTexts(filename, limit=-1):\r\n    '''\r\n        load dataset and preprocess it replace # by hashtag etc without save it\r\n    '''\r\n    f = open(filename, encoding=\"utf8\")\r\n    dataset=[line.split('\\t') for line in f]\r\n    for i, data in enumerate(dataset[0:10]):\r\n        \r\n        if i%100000==0:\r\n            print(i)\r\n        try:\r\n            data.remove('\\n')\r\n        except:\r\n            pass\r\n        data[1] = clean_str(data[1])\r\n        print(data)\r\n    return dataset\r\n    \r\ndef loadTextsAndPreprocess(filename, limit=-1):\r\n    '''\r\n        load dataset and preprocess it replace # by hashtag etc and save it\r\n    '''\r\n    f = open(filename, encoding=\"utf8\")\r\n    dataset=[line.split('\\t') for line in f]\r\n    for i, data in enumerate(dataset):\r\n        \r\n        if i%100000==0:\r\n            print(i)\r\n        try:\r\n            data.remove('\\n')\r\n        except:\r\n            pass\r\n        data[1] = clean_str(data[1])\r\n        #print(data)\r\n    \r\n    with open(filename.replace('.txt','_preproces.txt'), 'w', encoding='utf8') as f:\r\n        for d in dataset:\r\n            for wr in d:\r\n                f.write(wr+'\\t')\r\n            f.write('\\n')\r\n    return dataset\r\n    \r\ndef equilibrate_data(filename):\r\n    y = []\r\n    x = []\r\n    f = open(filename, 'r', encoding=\"utf8\")\r\n    dataset=[line.split('\\t') for line in f]\r\n    for data in dataset:\r\n        y.append(data[0])\r\n        x.append(data[1])\r\n    cpt = []\r\n    cpt.append(y.count('0'))\r\n    cpt.append(y.count('1'))\r\n    print(cpt)\r\n    minVal = min(cpt)\r\n    \r\n    f = open(filename.replace('.txt', '_eq.txt'),  'w', encoding=\"utf8\")\r\n    f2 = open(filename.replace('.txt', '_bonus.txt'), 'w', encoding=\"utf8\")\r\n    dic = {}\r\n    dic['0']=0\r\n    dic['1']=0\r\n    for i, label in enumerate(y):\r\n        if dic[label] >= minVal:\r\n            f2.write(label+'\\t' + x[i] + '\\t' + '\\n')\r\n        else:\r\n            f.write(label+'\\t' + x[i] + '\\t' + '\\n')\r\n            dic[label] += 1\r\n    f.close()\r\n    f2.close()\r\n    \r\ndef split_data(filename, ratio=0.8):\r\n    y = []\r\n    x = []\r\n    f = open(filename, 'r', encoding=\"utf8\")\r\n    dataset=[line.split('\\t') for line in f]\r\n    for data in dataset:\r\n        y.append(data[0])\r\n        x.append(data[1])\r\n    \r\n    f = open(filename.replace('.txt', '_train.txt'),  'w', encoding=\"utf8\")\r\n    f2 = open(filename.replace('.txt', '_test.txt'), 'w', encoding=\"utf8\")\r\n\r\n    for i, label in enumerate(y):\r\n        nb = np.random.uniform(0,1)\r\n        if nb <= ratio:\r\n            f.write(label+'\\t' + x[i] + '\\t' + '\\n')\r\n        else:\r\n            f2.write(label+'\\t' + x[i] + '\\t' + '\\n')\r\n            \r\n    f.close()\r\n    f2.close()\r\n    \r\ndef clean_save(dataset, name):\r\n    with open(name, 'w', encoding='utf8') as f:\r\n        for data in dataset:\r\n            f.write(data[0]+'\\t'+data[1]+'\\t\\n')\r\n\r\ndef load_csv(namefile):\r\n    data = []\r\n    with open(namefile, 'r', encoding='utf8') as f:\r\n        reader = csv.reader(f)\r\n        for row in reader:\r\n            data.append(row)\r\n    return data\r\ndef main():\r\n#    data = loadTextsAndDelete('data/Sentiment_Analysis_Dataset.txt')\r\n#    print(data)\r\n    data = loadTextsAndPreprocess('Sentiment_Analysis_Dataset_correct_train.txt')\r\n#    equilibrate_data('Sentiment_Analysis_Dataset_correct.txt')\r\n#    split_data('Sentiment_Analysis_Dataset_correct.txt')\r\n#    with open('Sentiment_Analysis_Dataset_correct.txt', 'w', encoding='utf8') as f:\r\n#        for d in data:\r\n#            for wr in d:\r\n#                f.write(wr+'\\t')\r\n#            f.write('\\n')\r\n#    data = loadTexts('twa.txt')\r\n#    clean_save(data, 'twa_clean.txt')\r\n#    \r\n#    data2 = loadTexts('twa-dev.txt')\r\n#    clean_save(data2, 'twa-dev_clean.txt')\r\n    #data = loadTextsAndPreprocess('data/Sentiment_Analysis_Dataset.txt')\r\n    #clean_save(data, 'Sentiment_Analysis_Datasetv2.txt')\r\nmain()\r\n#test = 'Bonjour !!!!! ! @moi je #connard suis $3 et $3.56 ou $345.6 Ou $354, http://zablo.net/blog/post/twitter-sentiment-analysis-python-scikit-word2vec-nltk-xgboost .'\r\n#print(clean_str(test))","sub_path":"preprocess_csv.py","file_name":"preprocess_csv.py","file_ext":"py","file_size_in_byte":9211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"193054033","text":"__author__ = 'dschkalee'\n\n#import pygtk\n#pygtk.require('2.0')\nimport argparse\nfrom src.misantronic.Main import App\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser(formatter_class=argparse.RawDescriptionHelpFormatter)\n\tparser.add_argument('-d', '--dialog', action='store_true', help='automatically display open-path dialog')\n\tparser.add_argument('-m', '--menu', default='Open path', help='comma-separated menu items')\n\tparser.add_argument('-p', '--path', default='', help='open path')\n\targs = parser.parse_args()\n\n\tApp(args)\n","sub_path":"winpath.py","file_name":"winpath.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"593858210","text":"#!/usr/local/bin/python3\n\n\"\"\"Task \n\nWrite a factorial function that takes a positive integer, N as a parameter and prints the result of N! (N factorial).\n\nNote: If you fail to use recursion or fail to name your recursive function factorial or Factorial, you will get a score of 0.\n\"\"\"\n\n# Complete the factorial function below.\ndef factorial(n):\n\tif n > 0:\n\t\treturn n * factorial(n-1)\n\telse:\n\t\treturn 1\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n    n = int(input())\n    result = factorial(n)\n    fptr.write(str(result) + '\\n')\n    fptr.close()\n","sub_path":"30 Days of Code/Day 9 - Recursion 3/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"637097733","text":"\n# https://drive.google.com/file/d/16kQgkuy-NSdIt5TXjR9dO3mgPB0liuoL/view?usp=sharing\n\n# Пользователь вводит две буквы. Определить, на каких местах алфавита они стоят, и сколько между ними находится букв.\n\nprint('Введи две буквы')\nchar1 = input('Введи первую букву:\\n')\nchar2 = input('Введи вторую букву:\\n')\ncorrection = 96\nposition1 = ord(char1) - correction\nposition2 = ord(char2) - correction\nrange_char = abs((position1 - position2)) - 1\nprint(f\"{char1} находится на позиции {position1}\\n{char2} находится на позиции {position2}\\n\\\nМежду буквами находится {range_char} букв\")","sub_path":"lesson_1/les1_task_5.py","file_name":"les1_task_5.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"369647518","text":"from wtforms import Form, StringField, validators, BooleanField, DecimalField\nfrom wtforms.widgets import HiddenInput\n\nfrom apps.models.shop_model import ShopSellerModel\n\n\nclass ShopForm(Form):\n    shop_name = StringField(label=\"店铺名称\",\n                            validators=[\n                                validators.Length(max=16, message=\"店铺名称最多16位\"),\n                                validators.DataRequired(message=\"必填\")],\n                            render_kw={'class': 'form-control', 'placeholder': '请输入店铺名'}\n                            )\n\n    brand = BooleanField(label=\"是否品牌\", default=True)\n    on_time = BooleanField(label=\"准时送达\", default=False)\n    fengniao = BooleanField(label='配送', default=True)\n    bao = BooleanField(label='保险', default=False)\n    piao = BooleanField(label='发票', default=False)\n    zhun = BooleanField(label='准标识', default=False)\n    start_send = DecimalField(label=\"起送价格\", places=2,\n                              validators=[\n                                  validators.DataRequired(message=\"必填\")],\n                              render_kw={'class': 'form-control', 'placeholder': '请输入起送价格'}\n                              )\n    send_cost = DecimalField(label=\"配送价格\", places=2,\n                             validators=[\n                                 validators.DataRequired(message=\"必填\")],\n                             render_kw={'class': 'form-control', 'placeholder': '请输入配送费用'}\n                             )\n    notice = StringField(label=\"店铺公告\",\n                         validators=[\n                             validators.DataRequired(message=\"必填\"),\n                             validators.Length(max=128, message=\"不得超过128个字符\")\n                         ],\n                         render_kw={'class': 'form-control', 'placeholder': '请输入店铺公告'}\n                         )\n    discount = StringField(label='优惠信息',\n                           validators=[\n                               validators.DataRequired(message='必填'),\n                               validators.Length(max=128, message=\"不得超过128个字符\")\n                           ],\n                           render_kw={'class': 'form-control', 'placeholder': '请输入优惠信息'}\n                           )\n\n    shop_img = StringField(label='店铺logo', id=\"image-input\", widget=HiddenInput())\n\n    def validate_start_send(self, obj):\n        obj.data = float(\"{:.2f}\".format(obj.data))\n\n    def validate_send_cost(self, obj):\n        obj.data = float(\"{:.2f}\".format(obj.data))\n\n\n","sub_path":"apps/forms/shop_form.py","file_name":"shop_form.py","file_ext":"py","file_size_in_byte":2673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"295131076","text":"import numpy as np\n\ndef pelt(cost, length, pen=None):\n    \"\"\" PELT algorithm to compute changepoints in time series\n\n    Ported from:\n        https://github.com/STOR-i/Changepoints.jl\n        https://github.com/rkillick/changepoint/\n    Reference:\n        Killick R, Fearnhead P, Eckley IA (2012) Optimal detection\n            of changepoints with a linear computational cost, JASA\n            107(500), 1590-1598\n\n    Args:\n        cost (function): cost function, with the following signature,\n            (int, int) -> float\n            where the parameters are the start index, and the second\n            the last index of the segment to compute the cost.\n        length (int): Data size\n        pen (float, optional): defaults to log(n)\n    Returns:\n        (:obj:`list` of int): List with the indexes of changepoints\n    \"\"\"\n    if pen is None:\n        pen = np.log(length)\n\n    optimal_costs = np.zeros(length + 1)\n    optimal_costs[0] = -pen\n\n    changepoints = np.zeros(length, dtype=np.int)\n\n    candidates = [0]\n\n    for i in range(1, length):\n        costs = []\n        for candidate in candidates:\n            ccost = cost(candidate, i + 1)\n            costs.append(ccost)\n\n        sel = optimal_costs[np.array(candidates, dtype=np.int)]\n        temp = np.add(sel, costs)\n\n        # Since pen is a constant, we don't need to add it to all\n        # the element of the array, only to the min value\n        min_val = min(temp) + pen\n        min_idx = np.argmin(temp)\n\n        optimal_costs[i+1] = min_val\n        changepoints[i] = candidates[min_idx]\n\n        ineq_prune = [val < min_val for val in temp]\n        _candidates = []\n        for j, val in enumerate(ineq_prune):\n            if val:\n                _candidates.append(candidates[j])\n        candidates = _candidates\n\n        candidates.append(i)\n\n    last = changepoints[-1]\n    backtrack = [last]\n    while last > 0:\n        last = changepoints[last-1]\n        backtrack.append(last)\n\n    return sorted(backtrack)\n","sub_path":"changepy/pelt.py","file_name":"pelt.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"582305050","text":"\"\"\"\n=========================================\nAuthor:薄咊\nTime:2020/9/30  23:55\ncomments:python学习\n==========================================\n\"\"\"\nimport unittest\nfrom library.HTMLTestRunnerNew import HTMLTestRunner\nfrom common.handle_path import REPORT_FILENAME,CASE_DIR\n\n\n# 创建测试套件\nsuite = unittest.TestSuite()\n\n# 加载用例到套件\nloader = unittest.TestLoader()\n# 执行所有测试用例类\nsuite.addTest(loader.discover(CASE_DIR))\n\nrunner = HTMLTestRunner(stream=open(REPORT_FILENAME, \"wb\"),\n                        title=\"测试报告\",\n                        tester=\"bohe\",\n                        description=\"Jenkins测试\"\n                        )\nrunner.run(suite)\n","sub_path":"run_base.py","file_name":"run_base.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"81188124","text":"import math\r\nimport random\r\n\r\ndef find_divisor(n):\r\n    for i in range(2, int(math.sqrt(n))+1):\r\n        if (n // i) * i == n:\r\n            return i\r\n    return None\r\n\r\ndef find_small_divisor(n):\r\n    for i in range(2, min(int(math.sqrt(n))+1, 20)):\r\n        if (n // i) * i == n:\r\n            return i\r\n    return None\r\n\r\ndef interpret(coin, base):\r\n    res = 0\r\n    mult = 1\r\n    while coin:\r\n        if coin[-1] == '1':\r\n            res += mult\r\n        coin = coin[:-1]\r\n        mult *= base\r\n    return res\r\n\r\ndef get_divisors(coin):\r\n    interpretations = [interpret(coin, base) for base in range(2,11)]\r\n    return list(map(find_divisor, interpretations))\r\n\r\ndef get_small_divisors(coin):\r\n    interpretations = [interpret(coin, base) for base in range(2,11)]\r\n    return list(map(find_small_divisor, interpretations))\r\n\r\ndef is_valid(coin):\r\n    divisors = get_divisors(coin)\r\n    return all(map(lambda x: x is not None, divisors))\r\n\r\nn = 16\r\nj = 50\r\ncoins = set()\r\nprint('Case #1:')\r\nwhile len(coins) != j:\r\n    coin = '1'\r\n    for i in range(1,n-1):\r\n        coin += random.choice('01')\r\n    coin += '1'\r\n    # print('(checking {})'.format(coin))\r\n    divisors = get_small_divisors(coin)\r\n    if all(map(lambda x: x is not None, divisors)):\r\n        print('{} {}'.format(coin, ' '.join(map(str, divisors))))\r\n        coins.add(coin)\r\n","sub_path":"solutions_5738606668808192_0/Python/tomerv/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"117887672","text":"from django.http import HttpResponse, JsonResponse\nfrom django.utils import timezone\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom users.controllers import ClientUserControllers\n\n\nclass DefaultResponse(object):\n    @classmethod\n    def api_response(cls, data, status=200):\n        response = HttpResponse(data, content_type='application/json')\n        response.status_code = status\n        return Response(data, status=status)\n\n    @classmethod\n    def api_ok_response(cls, data, messages, status_code=200):\n        response = dict(\n            success=True,\n            messages=messages,\n            data=data,\n            datetime=timezone.now().strftime('%Y-%m-%d %H:%M:%SZ')\n        )\n        return cls.api_response(response, status_code)\n\n    @classmethod\n    def api_fail_response(cls, data, messages, status_code=400):\n        response = dict(\n            success=False,\n            messages=messages,\n            data=data,\n            datetime=timezone.now().strftime('%Y-%m-%d %H:%M:%SZ')\n        )\n        return cls.api_response(response, status_code)\n\n\nclass NoTokenView(APIView, DefaultResponse):\n    pass\n\n\nclass TokenView(APIView, DefaultResponse):\n    user = None\n    user_required = True\n\n    def get_auth_token_from_headers(self, request):\n        if 'HTTP_AUTHORIZATION' not in request.META:\n            return None\n        return request.META['HTTP_AUTHORIZATION']\n    def dispatch(self, request, *args, **kwargs):\n        token = self.get_auth_token_from_headers(request)\n        if token is None:\n            response = self.api_fail_response(None, 'Se requiere token')\n            return JsonResponse(response.data, status=401)\n        data = ClientUserControllers.get_by_token(token)\n        if data is None:\n            response = self.api_fail_response(None, 'Token incorrecto')\n            return JsonResponse(response.data, status=401)\n        self.user = ClientUserControllers.get_by_uuid(data['uuid'])\n        response = super(TokenView, self).dispatch(request, *args, **kwargs)\n        return response","sub_path":"core/view_rest.py","file_name":"view_rest.py","file_ext":"py","file_size_in_byte":2080,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"356818971","text":"from selenium.common.exceptions import NoSuchElementException, NoAlertPresentException, TimeoutException\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom .locators import BasePageLocators, BasketLocators\nimport math\n\nclass BasePage():\n    def __init__( self, browser, url, timeout = 30 ) :\n        self.browser = browser\n        self.url = url\n        self.browser.implicitly_wait( timeout ) #команда неявного ожидания\n\n    def open( self ) :\n        self.browser.get( self.url )\n\n    def is_element_present( self, how, what ):\n        try :\n            WebDriverWait( self.browser, 20 ).until(\n                lambda browser : self.browser.find_element( how, what )\n            )\n           # self.browser.find_element( how, what )\n        except NoSuchElementException :\n            return False\n        return True\n\n    def is_not_element_present( self, how, what ):\n        try:\n            WebDriverWait(self.browser, 4).until(\n                lambda browser: self.browser.find_element(how, what)\n            )\n            # self.browser.find_element( how, what )\n        except TimeoutException:\n            return True\n        return False\n\n    def is_disappear(self, how, what ):\n        try :\n            WebDriverWait( self.browser, 4, 1, TimeoutException ).until_not(\n                lambda browser : self.browser.find_element( how, what ) )\n        except TimeoutException :\n            return False\n        return True\n\n    def solve_quiz_and_get_code( self ) :\n        alert = self.browser.switch_to.alert\n        x = alert.text.split( \" \" )[2]\n        print( x )\n        answer = str( math.log( abs( ( 12 * math.sin( float(x) ) ) ) ) )\n        alert.send_keys( answer )\n        alert.accept()\n        try:\n            alert = self.browser.switch_to.alert\n            alert_text = alert.text\n            print( f\"Your code: {alert_text}\" )\n            alert.accept()\n        except NoAlertPresentException:\n            print( \"No second alert presented\" )\n\n    def go_to_login_page( self ) :\n        link = self.browser.find_element( *BasePageLocators.LOGIN_LINK )\n        link.click()\n\n    def should_be_login_link( self ) :\n        assert self.is_element_present( *BasePageLocators.LOGIN_LINK ), \"Login link is not Presented\"\n\n    def go_to_basket( self ) :\n        link = self.browser.find_element( *BasketLocators.BASKET_LINK )\n        link.click()\n\n    def should_be_authorized_user( self ) :\n        assert self.is_element_present( *BasePageLocators.USER_ICON ), \"You must login\"","sub_path":"page/base_page.py","file_name":"base_page.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"565740301","text":"#!/usr/local/bin/python3\nimport sys\n\ndef forlist(argument):\n    for index in range(len(argument)):\n        print(\"Index \" + str(index) + \" is \" + argument[index])\n\ncatnames = []\nprint(len(catnames))\nwhile True:\n    print(\"Enter the name of the cat \" + str(len(catnames) + 1) + \":\")\n    name = input()\n    if name == \"\":\n        break\n    catnames = catnames + [name]\n\nprint(\"The cates names are: \")\nfor cat in catnames:\n    print(\"\\t\" + cat)\n\n\nsupplies = ['pens', 'stapplers', 'rubber', 'ruler', \"sheets\", 'binder']\n\nfor index in range(len(supplies)):\n    print(\"Index \" + str(index) + \" is \" + supplies[index])\n\nforlist(catnames)","sub_path":"chapter4-list-1.py","file_name":"chapter4-list-1.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"646636953","text":"#\n# @lc app=leetcode id=1122 lang=python3\n#\n# [1122] Relative Sort Array\n#\n\n# @lc code=start\nclass Solution:\n    def relativeSortArray(self, arr1: List[int], arr2: List[int]) -> List[int]:\n        cnt = [0]*1001\n        res = []\n        for num1 in arr1:\n            cnt[num1] += 1\n\n        for num2 in arr2:\n            res.extend([num2]*cnt[num2])\n            cnt[num2] = 0\n\n        for i in range(1001):\n            if cnt[i] != 0:\n                res.extend([i]*cnt[i])\n\n        return res     \n# @lc code=end\n\n","sub_path":"week05HW/1122.relative-sort-array.py","file_name":"1122.relative-sort-array.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"138476249","text":"\"\"\" \n--------------------------------------------------------------------------------\nDescriptive Name     : Parsing tutorial 1\nAuthor               : Kshitij Jain\nContact Info         : jain98@purdue.edu\nDate Written         : 08/02/17\nDescription          : Parsing a site that uses Google Maps API\nCommand to run script: parsingTutorial1.py\nUsage                : N/A\nInput file format    : N/A\nOutput               :\nNote                 :\nOther files required by : N/A\nthis script and where\nlocated\n\n----For Parsing Scripts---------------------------------------------------------\nWebsite Parsed       : <insert url>\nIn database (Y/N)    :\nDate added to Database :\n--------------------------------------------------------------------------------\n\"\"\"\nfrom bs4 import BeautifulSoup\nimport urllib2\nimport re\nimport sys\nimport time\nimport json\nfrom selenium import webdriver\nimport platform\n\ndef nycdot():\n    JSonURL = \"http://dotsignals.org/new-data.php\"  # url to the JSON file containing Map data\n    CameraPopURL = \"http://dotsignals.org/google_popup.php?cid=\"    # url to access the camera popup\n\n    # Open an output file for writing the Camera info obtained from JSonURL\n    with open(\"nycdot_list\", \"w\") as f:\n        # write header\n        f.write(\"description#snapshot_url#latitude#longitude#country#city\\n\")\n\n        # load JSON file into response\n        response = urllib2.urlopen(JSonURL).read()\n        # Parse the JSON file with the json module\n        parsed_json = json.loads(response)\n        # navigate to the markers key which contains all the camera data\n        cameras = parsed_json['markers']\n\n        for camera in cameras:\n            cam_id = camera['id']\n            content = camera['content']\n            latitude = camera['latitude']\n            longitude = camera['longitude']\n            url = CameraPopURL+cam_id\n\n            browser.get(url)\n            soup = BeautifulSoup(browser.page_source)\n            snapshot_url = soup.find('img').get('src')\n            if re.search(r'img/inactive', snapshot_url) == None:\n                snapshot_url = re.search(r'(?P<URL>[\\w\\.\\/:\\\\]*)', snapshot_url).group('URL')\n                f.write(content+\"#\"+str(snapshot_url)+\"#\"+latitude+\"#\"+longitude+\"#\"+\"USA#NY#New York\\n\")\n    return\n\n\n\nif __name__ == '__main__':\n    if platform.system() == 'Windows':\n        PHANTOMJS_PATH = './phantomjs.exe'\n    else:\n        PHANTOMJS_PATH = './phantomjs'\n\n    browser = webdriver.PhantomJS(PHANTOMJS_PATH)\n    nycdot()\n","sub_path":"parsingTutorial1.py","file_name":"parsingTutorial1.py","file_ext":"py","file_size_in_byte":2490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"31501136","text":"import read_states as rs\nimport tkinter as tk\n\n# shiko rastin e rekursioni!!!\nclass NFA:\n    def __init__(self, start_state, final_states, alphabet, filename):\n        # inpute nga perdoruesi\n        self.start_state = start_state\n        self.final_states = final_states.split(\",\")\n        self.alphabet = alphabet.split(\",\")\n        self.filename = filename\n        # variabla instance te klases NFA\n        self.dataset = rs.get_dataset(self.filename)\n        self.tf = self.create_transition_dictionary_for_ENFA()\n        self.nfa_finish_states = set()\n        self.states = rs.get_states(self.filename)\n\n    def create_dictionary(self):\n        diction = {k: [] for k in self.alphabet}\n        return diction\n\n    def create_transition_dictionary_for_ENFA(self):\n        dataset = self.dataset\n        epsilon_alphabet = [\"epsilon\"] + self.alphabet\n        transition_functions = {}\n        i = 1\n        for char in epsilon_alphabet:\n            transition_functions[char] = dataset.iloc[:, i].values\n            i += 1\n        return transition_functions\n\n    def where_to_nfa(self, c, s, tf, seen_states):\n        tmp = set()\n        tmp.add(str(s))\n        if c == \"epsilon\":\n            seen_states_copy = seen_states.copy()\n            seen_states_copy.add(str(s))\n            sq = set(str(tf[c][s]).split(\",\"))\n            for st in sq:\n                if st != str(s) and st not in seen_states:\n\n                    tmp.update(\n                        self.where_to_nfa(\"epsilon\", int(st), tf, seen_states_copy)\n                    )\n            return tmp\n        else:\n            sq = set(str(tf[c][s]).split(\",\"))\n            return sq\n\n    def convert_enfa_to_nfa(self):\n        to_check = {}\n        nfa_dictionary = self.create_dictionary()\n        finale = set()\n        seen_states = set()\n        for state in self.states:\n            for c in self.alphabet:\n                finale.update(set(str(state)))\n                to_check = finale.copy()\n                finale.clear()\n                for st in to_check:\n                    finale.update(\n                        self.where_to_nfa(\"epsilon\", int(st), self.tf, seen_states)\n                    )\n                for i in finale:\n                    if str(i) in self.final_states:\n                        self.nfa_finish_states.add(str(state))\n                        break\n                to_check = finale.copy()\n                finale.clear()\n                for st in to_check:\n                    finale.update(self.where_to_nfa(c, int(st), self.tf, seen_states))\n                to_check = finale.copy()\n                finale.clear()\n                for st in to_check:\n                    finale.update(\n                        self.where_to_nfa(\"epsilon\", int(st), self.tf, seen_states)\n                    )\n                if len(finale) != 1 and \"-1\" in finale:\n                    finale.remove(\"-1\")\n\n                nfa_dictionary[c].append(list(finale))\n                finale.clear()\n        return nfa_dictionary\n\n    def visualize_NFA(self, root, data):\n        window = tk.Tk()\n        state = tk.Label(window, text=\"State\")\n        state.grid(row=0, column=0)\n        alphabet_length = len(self.alphabet)\n        for i in range(0, alphabet_length):\n            char = tk.Label(window, text=self.alphabet[i])\n            char.grid(row=0, column=i + 1)\n        for i in range(0, len(self.states)):\n            s = tk.Label(window, text=self.states[i])\n            s.grid(row=(i + 1), column=0)\n            for j in range(0, alphabet_length):\n                m = self.alphabet[j]\n                w = tk.Label(window, text=str(data[m][i]))\n                w.grid(row=i + 1, column=j + 1)\n        l = tk.Label(window, text=\"NFA Start State: {}\".format(self.start_state))\n        l.grid(row=len(self.states) + 2)\n        l = tk.Label(\n            window, text=\"NFA Finish States: {}\".format(self.nfa_finish_states)\n        )\n        l.grid(row=len(self.states) + 3)\n\n","sub_path":"nfa.py","file_name":"nfa.py","file_ext":"py","file_size_in_byte":3964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"558350968","text":"__author__ = 'hzbc'\n\nfileName = \"readme.txt\"\n\nwith open(fileName) as fn:\n    content = fn.readlines()\nprint(content)\n\noutput = \"output.dat\"\nf = open(output,'a')\nf.write(\"insert word,\")\nf.write(\"two\\n\")\nf.close()","sub_path":"cn/fawzi/first/File.py","file_name":"File.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"84110655","text":"from flask import Flask\nfrom flask_restful import reqparse, abort, Api, Resource\nimport json\nimport pandas as pd\nimport numpy as np\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.externals import joblib\nfrom sklearn.feature_extraction.text import TfidfTransformer\nfrom sklearn.feature_extraction.text import CountVectorizer\n\napp = Flask(__name__)\napi = Api(app)\n\n#model = RandomForestClassifier()\n\nRFmodel_path = 'RFClassiferextrasmall.joblib.pkl'\nwith open(RFmodel_path, 'rb') as f:\n    model = joblib.load(f)\n\ntfIDV_vectorizer_path = 'Transformer.joblib.pkl'\nwith open(tfIDV_vectorizer_path, 'rb') as f:\n    tfidf_transformer = joblib.load(f)\n    \ncount_vectorizer = 'word_count_vector.joblib.pkl'\nwith open(count_vectorizer, 'rb') as f:\n    cv = joblib.load(f)\n# argument parsing\nparser = reqparse.RequestParser()\nparser.add_argument('query')\n\nclass PredictSentiment(Resource):\n    def get(self):\n        # use parser and find the user's query\n        args = parser.parse_args()\n        user_query = args['query']\n        inputvector = []\n        inputvector.append(user_query)\n        countvector = cv.transform(inputvector)\n        tf_idf_vector=tfidf_transformer.transform(countvector)\n\n        output= model.predict(tf_idf_vector )\n        df=pd.DataFrame(output)\n        # create JSON object\n        response = {'prediction': df.to_json()}\n        return response\n\n\n# Setup the Api resource routing here\n# Route the URL to the resource\napi.add_resource(PredictSentiment, '/')\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1554,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"122359484","text":"\"\"\"Find duplicate files inside a directory tree.\"\"\"\n \nfrom os import walk, remove, stat\nfrom os.path import join as joinpath\nfrom pathlib import Path, PurePath, PurePosixPath\nfrom hashlib import md5\nimport argparse\nfrom argparse import ArgumentParser\nimport os\nimport hashlib\nimport shutil\n\naccepted_image_formats = ['.JPG','.PNG']\naccepted_movie_formats = ['.AVI', '.MOV']\naccepted_formats = accepted_image_formats + accepted_movie_formats\nprint(accepted_formats)\n\ndef output_folder(directory):\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n\ndef hashfile(path, blocksize = 65536):\n    afile = open(path, 'rb')\n    hasher = hashlib.md5()\n    buf = afile.read(blocksize)\n    while len(buf) > 0:\n        hasher.update(buf)\n        buf = afile.read(blocksize)\n    afile.close()\n    return hasher.hexdigest()\n \ndef find_duplicates( rootdir, year ):\n    \"\"\"Find duplicate files in directory tree.\"\"\"\n    file_list = []\n\n    outfolder = \"e:\\\\Pictures\\\\final_\" +  year\n\n    if not os.path.exists(outfolder):\n        os.makedirs(outfolder)\n\n    for dirpath, dirnames, filenames in os.walk( rootdir ):\n        for filename in filenames:\n            file_path = os.path.join(dirpath, filename)  \n            if os.path.basename(dirpath).startswith(year):\n                file_extension = PurePosixPath(file_path).suffix\n                if file_extension in accepted_formats:     \n                    file_list.append(file_path)\n                    \n    unique = set()\n    duplicates = []\n\n    with open(\"duplicates\" + year + \".txt\", \"w\") as f:\n        for filepath in file_list:\n            filehash = hashfile(filepath)\n            if filehash not in unique:\n                \n                only_filename = os.path.basename(filepath)\n                # shutil.move(filepath, os.path.join(outfolder, only_filename))\n                unique.add( filehash )\n            else:\n                f.write(filepath + '\\n')\n                duplicates.append( filepath )\n    return duplicates\n \nif __name__ == '__main__':\n    # construct the argument parse and parse the arguments\n    ap = argparse.ArgumentParser()\n    ap.add_argument(\"-f\", \"--folder\", required = True,\n        help = \"path to input folder containing subfolders and images\")\n    ap.add_argument(\"-y\", \"--year\", required = True,\n        help = \"path to input folder containing subfolders and images\")\n    args = vars(ap.parse_args())\n\n    find_duplicates(args['folder'], args['year'])\n","sub_path":"code/findduplicates_v2.py","file_name":"findduplicates_v2.py","file_ext":"py","file_size_in_byte":2454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"648243067","text":"def myMin(aList):\n    low = aList[0]\n    for item in aList:\n        if item < low: low = item\n    print(low)\n    # does not matter if it is printed or returned\n    return low\n\n\ndef myMax(aList):\n    high = aList[0]\n    for item in aList:\n        if item > high: high = item\n    print(high)\n    # does not matter if it is printed or returned\n    return high\n\n\ndef myTotal(aList):\n    total = 0\n    for item in aList:\n        total += item\n    print(total)\n    # does not matter if it is printed or returned\n    return total\n\n\ndef myAvg(aList):\n    sum = myTotal(aList)\n    avg = sum / len(aList)\n    print(avg)\n    return avg\n\n\ndef myCount(aList, thing):\n    count = 0\n    for item in aList:\n        if item == thing:\n            count += 1\n    print(count)\n    return count\n\n\ndef myReplace(aList, old, new):\n    for index, item in enumerate(aList):\n        if item == old:\n            aList = aList[:index] + [new] + aList[index + 2:]\n    print(aList)\n    return aList\n\n\ndef main():\n    myList = [0, 5, 8, 3, 12, 57, 42, 68, 0, 0, 0, 6]\n    myMin(myList)\n    myMax(myList)\n    myTotal(myList)\n    myAvg(myList)\n    myCount(myList, 0)\n    myReplace(myList, 12, 9000)\n\n\nmain()\n","sub_path":"Lab/lab 10 (1).py","file_name":"lab 10 (1).py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"651790157","text":"import numpy as np\r\nimport pyqtgraph as pg\r\nfrom pyqtgraph.Qt import QtGui\r\n#import cmdLineArgument as cla\r\n\r\nanticipation_time = '300'\r\ncoordinate = 'pitch' \r\nmode = 'movement'\r\n\r\n# initiate the Windows of real time plot\r\napp = QtGui.QApplication([])                                # Create the application\r\npg.setConfigOption('background', 'k')\r\n\r\nif mode == \"movement\":\r\n\tplot_title = 'Real Time Plot'\r\n\tsubtitle = 'Orientation'\r\nelif mode == \"error\":\r\n\tplot_title = 'Real Time Error Plot'\r\n\tsubtitle = 'Error'\r\n\t\r\n\r\nwin = pg.GraphicsWindow(title=plot_title)# Create empty space in the window\r\np = win.addPlot(title= subtitle + '(' + coordinate + '), Anticipation Time: ' + str(anticipation_time) + ' ms') \t\t\t# Add the plot to be drawn\r\n\r\nif coordinate == \"yaw\":\r\n\tp.setYRange(-100,100,padding = 0)\r\nelse:\r\n\tp.setYRange(-90,90,padding = 0)\r\n\r\np.addLegend()\r\np.setLabel('bottom', text='time(s)')\r\np.setLabel('left', text='angles(deg)')\r\nstream_0 = p.plot(pen=(255, 255, 255), name='actual')                \t # plot color\r\nstream_1 = p.plot(pen=(255,0, 0), name='crp')\r\nstream_2 = p.plot(pen=(0,255,0), name='cap')\r\nstream_3 = p.plot(pen=(255,0,255), name='nop')\r\nstream_4 = p.plot(pen=(0,255,255), name='ann')\r\n\r\nwindow_width = 200\r\nstream0 = np.zeros(shape=(window_width, 2))\r\nstream1 = np.zeros(shape=(window_width, 2))\r\nstream2 = np.zeros(shape=(window_width, 2))\r\nstream3 = np.zeros(shape=(window_width, 2))\r\nstream4 = np.zeros(shape=(window_width, 2))\r\n\t\r\ndef RealTimePlot(x, y):\r\n\t# this part is for \"sliding\" the plot\r\n\tstream0[:-1] = stream0[1:]\r\n\tstream1[:-1] = stream1[1:]\r\n\tstream2[:-1] = stream2[1:]\r\n\tstream3[:-1] = stream3[1:]\r\n\tstream4[:-1] = stream4[1:]\t\r\n\t#this part is for \"filling the gap\" left by sliding\r\n\tstream0[-1] = np.array([x,y[0]])\r\n\tstream1[-1] = np.array([x,y[1]])\r\n\tstream2[-1] = np.array([x,y[2]])\r\n\tstream3[-1] = np.array([x,y[3]])\r\n\tstream4[-1] = np.array([x,y[4]])\r\n\t\r\n\t# this part is for \"real time plotting\"\r\n\t# print(stream0)\r\n\tstream_0.setData(stream0)\r\n\tstream_1.setData(stream1)\r\n\tstream_2.setData(stream2)\r\n\tstream_3.setData(stream3)\r\n\tstream_4.setData(stream4)\r\n\tQtGui.QApplication.processEvents()","sub_path":"RealTimePlot.py","file_name":"RealTimePlot.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"468922367","text":"import random\r\n\r\nboard = []\r\n\r\nfor i in range(0,5):\r\n  board.append([\"o\"] * 5)\r\n\r\nfor row in board:\r\n    print (\" \".join(row))\r\nprint(\"Let's play!\")\r\nprint(\"This is a 2 player game\")\r\n\r\nplayer_1 = input(\"Enter first name: \")\r\nplayer_2 = input(\"Enter second name: \")\r\nplayers = [player_1, player_2]\r\n\r\nfor i in range(0,5):\r\n  row = random.randint(0,len(board)-1)\r\n  col = random.randint(0,len(board[0])-1)\r\n  print(\" \".join(board[i]))\r\n\r\nif random.choice(players) == player_1:\r\n  print(player_1, \"starts the game.\")\r\nelse:\r\n  print(player_2, \"starts the game.\")\r\n\r\nship_row_1 = random.randint(0,len(board)-1)\r\nship_col_1 = random.randint(0,len(board[0])-1)\r\n\r\nship_row_2 = random.randint(0,len(board)-1)\r\nship_col_2 = random.randint(0,len(board[0])-1)\r\n\r\nfor i in range(5):\r\n  print(\" \".join(board[i]))\r\n\r\nplayer_start = random.choice(players)\r\n\r\nhit_count = 0\r\n\r\nfor turn in range(4):\r\n     guess_row = int(input(\"Guess Row: (allowed values: 0-4) \"))\r\n     guess_col = int(input(\"Guess Col: (allowed values: 0-4) \"))\r\n\r\n     if (guess_row == ship_row_1 and guess_col == ship_col_1) or (guess_row == ship_row_2 and guess_col == ship_col_2):\r\n            hit_count = hit_count + 1\r\n            board[guess_row][guess_col] = \"*\"\r\n            print (\"Congratulations! \")\r\n            if hit_count == 1:\r\n                   print(\"You sunk first battleship!\") \r\n            elif hit_count == 2:\r\n                   print(\"You sunk second battleship! You win!\")\r\n                   print(\" \".join(board[5]))\r\n                   break\r\n     else:\r\n            if (guess_row < 0 or guess_row > 4)  or (guess_col < 0 or guess_col > 4):\r\n                   print (\"Oops, you miss the board.\")\r\n            elif(board[guess_row][guess_col] == \"X\"):\r\n                   print (\"You guessed that one already.\")\r\n            else:\r\n                 print (\"You missed my battleship!\")\r\n                 board[guess_row][guess_col] = \"X\"\r\n            print (turn + 1, \"turn\")\r\n     for i in range(5):      \r\n      print(\" \".join(board[i]))\r\nprint (\"Ship 1 is hidden:\")    \r\nprint (ship_row_1)\r\nprint (ship_col_1)\r\n\r\nprint (\"Ship 2 is hidden:\")    \r\nprint (ship_row_2)\r\nprint (ship_col_2)","sub_path":"battleship_v2.py","file_name":"battleship_v2.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"78866516","text":"# Training reconstruction with temporal output with v5. This is almost the same as v1_trainer except the\n# dynamic part\n\nimport torch\nimport torch.nn as nn\n\n# Prepare paths\nimport os\nimport pickle\nimport time\n\nfrom ignite.engine import Engine, Events\nfrom ignite.handlers import Checkpoint, DiskSaver, EarlyStopping, global_step_from_engine\nfrom ignite.metrics import RunningAverage\nfrom torch.optim.lr_scheduler import MultiStepLR\nfrom ignite.contrib.handlers.param_scheduler import LRScheduler\nfrom torch.utils.data import random_split, DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\nfrom data_utilities.data_paths import DataPath\nfrom reconstruction_models.linear_subject import HeatDiffusionKernel\nfrom reconstruction_trainer.load_ckp import load_checkpoint\nfrom utils.argument_parser import get_argparser\n# Prepare paths\nfrom data_utilities.amy_dataset import Datasetv5\nfrom data_utilities.prepare_data import prepare_data_qced\nimport json\n\n\nparser = get_argparser()\nargs = parser.parse_args()\nprint(args)\n\n\nFILE_PREFIX = DataPath.PREFIX\nfolder_name = args.write_dir\nlog_dir = os.path.join(FILE_PREFIX, \"Diffusion_u_net/log/\" + folder_name)\ntb_log_dir = log_dir + \"/tb\"\nmodel_log_dir = log_dir + \"/model\"\nresult_log_dir = log_dir + \"/result\"\ntb_log = SummaryWriter(tb_log_dir)\nlabel_all = json.load(open(\"classes_all.json\"))\n\n# Prepare data\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\nprint(\"Device: {}\".format(device))\n\nx, y, dx, g, T = prepare_data_qced(label_all)\nx = x.to(device)\ny = y.to(device)\ndx = dx.to(device)\ng = g.to(device)\n# T = torch.cat([torch.zeros(T.shape[0], 1), T], dim=-1)\nT = T.to(device)\nweight = []\nfor i in range(len(set(label_all.values()))):\n    weight.append(1000/(dx == i).sum())\nweight = torch.FloatTensor(weight).to(device)\nprint(\"Weight:\", weight)\ntotal = dx.shape[0]\ntrain_split = int(total * args.train_r)\nvalid_split = int(total * args.valid_r)\ninit_dataset = Datasetv5(x=x, y=y, z=dx, a=g, t=T)\nlengths = [train_split, valid_split, len(init_dataset) - train_split - valid_split]\nprint(\"Split length: \", lengths)\ntrain, test, eval = random_split(init_dataset, lengths, generator=torch.Generator().manual_seed(4))\ntrain_loader = DataLoader(train, batch_size=args.batch_size)\nvalid_loader = DataLoader(test, batch_size=valid_split)\neval_loader = DataLoader(eval, batch_size=len(init_dataset) - train_split - valid_split)\nprint(\"Data Loaded !!!\")\nprint(\"Data Loaded !!!\")\n\n# Prepare model\ndropout = args.dropout\nact = nn.ELU()\n\n\ndef init_weights(m):\n    if type(m) == nn.Linear:\n        torch.nn.init.xavier_uniform_(m.weight)\n        m.bias.data.fill_(0.01)\n\n\nwith open(DataPath.PREFIX + \"/Diffusion_u_net/data_utilities/avg_data.p\", \"rb\") as f:\n    data = pickle.load(f)\n\nload_model = args.load\ntrain_model = args.train\nmodel_name = \"saved_model\"\ncn_net = torch.FloatTensor(data[\"net\"][\"CN\"]).to(device)\ncn_net = (cn_net + cn_net.t()) * 0.5\nl = torch.diag_embed(cn_net.sum(dim=-1)) - cn_net\nV, U = torch.symeig(l, eigenvectors=True)\nif load_model:\n    if args.model_ckp_path == \"saved_model\":\n        with open(os.path.join(model_log_dir, model_name), \"rb\") as f:\n            model = torch.load(f, map_location=torch.device(device)).to(device)\n    else:\n        model = HeatDiffusionKernel(in_feat=2, dim=args.indim,\n                                    n_modes=args.modes, act=nn.ELU(), ks=args.ks, n_class=len(set(label_all.values())),\n                                    k=args.n_neigh, drop_p=args.dropout, device=device).to(device)\n        ckp = load_checkpoint(model, os.path.join(model_log_dir, args.model_ckp_path))\n        model.load_state_dict(ckp[\"model\"])\n        model.device = device\nelse:\n    with open(os.path.join(log_dir, \"Arguments\"), \"w\") as f:\n        f.write(str(args))\n        f.write(\"Labels: {}\".format(label_all))\n    model = HeatDiffusionKernel(in_feat=2, dim=args.indim, n_class=len(set(label_all.values())),\n                                n_modes=args.modes, act=nn.ELU(), ks=args.ks,\n                                k=args.n_neigh, drop_p=args.dropout, device=device).to(device)\n    model.apply(init_weights)\nprint(model)\n\noptimizer = torch.optim.Adam(model.parameters(), lr=args.lr) if args.opt == \"Adam\" else torch.optim.SGD(model.parameters(), lr=args.lr)\ncriterion_recon = torch.nn.MSELoss()\ncriterion_cls = torch.nn.CrossEntropyLoss(weight)\nstep_scheduler = MultiStepLR(optimizer, milestones=[args.split], gamma=0.1)\nlr_scheduler = LRScheduler(step_scheduler)\n\n\ndef compute_loss(y_pred, y):\n    loss_temp = torch.mul((y_pred - y) ** 2, (y != 0)).sum(dim=1).mean()\n    return loss_temp\n\n\n# Prepare trainer\ndef process_function(engine, batch):\n    model.train()\n    optimizer.zero_grad()\n    x, y, dx, g, t = batch\n    y_pred = model(x[:, :, 0:2], g, t)\n    loss = compute_loss(y_pred, y)\n    loss.backward()\n    optimizer.step()\n    return {\"prediction\": y_pred, \"true\": y, \"loss\": loss.item()}\n\n\ndef evaluate_function(engine, batch):\n    model.eval()\n    with torch.no_grad():\n        x, y, dx, g, t = batch\n        y_pred = model(x[:, :, 0:2], g, t)\n        loss = compute_loss(y_pred, y)\n        return {\"prediction\": y_pred, \"true\": y, \"loss\": loss.item()}\n\n\ndef print_logs(engine, dataloader, mode, history_dict):\n    metrics = [\"loss\"]\n    evaluator.run(dataloader, max_epochs=1)\n    print_line = mode + \": \" + str(engine.state.epoch) + \": \"\n    for m in metrics:\n        v = evaluator.state.output[m]\n        tb_log.add_scalar(\"{}/{}\".format(mode, m), v, engine.state.epoch)\n        print_line += \"{}: {:.4f}, \".format(m, v)\n        if m not in history_dict.keys():\n            history_dict[m] = [v]\n        else:\n            history_dict[m].append(v)\n    print(print_line)\n\n\nif train_model:\n    trainer = Engine(process_function)\n    evaluator = Engine(evaluate_function)\n    metric_names = [\"loss\"]\n    training_history = {}\n    validation_history = {}\n    evaluation_history = {}\n    for m in metric_names:\n        RunningAverage(output_transform=lambda x: x[m]).attach(trainer, m)\n        RunningAverage(output_transform=lambda x: x[m]).attach(evaluator, m)\n    to_save = {\n        'model': model,\n        'optimizer': optimizer,\n        'trainer': trainer\n    }\n\n    def score_function(engine):\n        val_loss = evaluator.state.output['loss']\n        return -val_loss\n\n    handler = Checkpoint(to_save,\n                         DiskSaver(model_log_dir, create_dir=True, require_empty=False),\n                         score_name=\"val_r_loss\",\n                         filename_prefix='best',\n                         score_function=score_function,\n                         global_step_transform=global_step_from_engine(trainer),\n                         n_saved=10)\n\n    trainer.add_event_handler(Events.EPOCH_COMPLETED, print_logs, train_loader, 'Training', training_history)\n    trainer.add_event_handler(Events.EPOCH_COMPLETED, print_logs, valid_loader, 'Validation', validation_history)\n    trainer.add_event_handler(Events.EPOCH_COMPLETED(every=10), handler)\n    trainer.add_event_handler(Events.EPOCH_STARTED, lr_scheduler)\n    start_time = time.time()\n    trainer.run(train_loader, max_epochs=args.max_epoch)\n    with open(os.path.join(model_log_dir, model_name), \"wb\") as f:\n        torch.save(model, f)\n    elapsed = time.time() - start_time\n    print(\"Time elapsed: {}h {}m\".format(elapsed // 3600, (elapsed % 3600) // 60))\n","sub_path":"reconstruction_trainer/linear_subject_trainer.py","file_name":"linear_subject_trainer.py","file_ext":"py","file_size_in_byte":7360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"186834700","text":"from gensim.models import Word2Vec\nimport multiprocessing\nimport os\n\ninput_dir = \"./out_list/\"\nfile_list = []\ntext = []\nlength = 3000\nreplace_list = [\",\",\"'\",'\"',\"/\",\"?\",\".\",\"!\",\":\"]\n\n\nfor files in os.listdir(input_dir):\n\tlength = length - 1\n\tflag = 0\n\tfilename = files\n\t#print(filename)\n\tif filename != \".DS_Store\":\n\t\tfilename = input_dir + str(filename)\n\t\tfile_list.append(filename)\n\t\tloadf = open(filename,'r')\n\t\tloadfile = loadf.read()\n\t\tloadfile_sep = loadfile.split(\".\")\n\t\tfor sep in loadfile_sep:\n\t\t\tsep_rep = sep\n\t\t\tfor rep in replace_list:\n\t\t\t\tsep_rep = sep_rep.replace(rep,\"\")\n\t\t\t#loadfile = loadfile.lower\n\t\t\t#text.append(loadfile)\n\t\t\ttext.append(sep_rep)\n\tif length < 0:\n\t\tbreak\n\n#text = [\"Drinking enough water is vital to health and good bodily functioning. However, some research suggests that the temperature of water when a person drinks it is also important. Here, we discuss whether cold water can be bad for health and if there are any risks or benefits of drinking cold water vs. warm water.\",\"Previous studies from our lab have suggested that at least 50% of our metabolism is circadian, and 50% of the metabolites in our body oscillate based on the circadian cycle. It makes sense that exercise would be one of the things that's impacted, says Sassone-Corsi.\",\"Gad Asher, who works in the Department of Biomolecular Sciences at the Weizmann Institute of Science in Rehovot, Israel, is senior author of the first study, while Paolo Sassone-Corsi of the Center for Epigenetics and Metabolism at the University of California (UC), Irvine, is senior author of the second.\"]\n\n#text = [\"Welcome to the planetarium where the stars are waiting for you\", \"Drink water is good for your health\"]\nsentences = []\nfor line in text:\n\tsentences.append(line.split())\n\nEMB_DIM = 500\nprint(sentences)\n\nmodel = Word2Vec.load(\"point2.model\")\n\n#w2v = Word2Vec(sentences, size=EMB_DIM, window=20, min_count=3, iter=100, workers=multiprocessing.cpu_count())\nmodel.train(sentences,total_examples = len(sentences),epochs = 10)\n\nmodel.save(\"point2_new.model\")\n\n\nword_vectors = model.wv\n\nresult = word_vectors.similar_by_word(\"cancer\")\nprint(result)","sub_path":"new/model_sample.py","file_name":"model_sample.py","file_ext":"py","file_size_in_byte":2144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"527764127","text":"\"\"\"\nDependency Management System\n\"\"\"\n\nimport argparse\nimport logging\nimport os\nimport pathlib\nimport sys\n\nimport yaml\n\nfrom cache import Cache\nfrom install import Installer\nfrom platform_introspection import get_platforms\n\n# Set up logging and handler\nlogger = logging.getLogger('cbdep')\nlogger.setLevel(logging.DEBUG)\n\nch = logging.StreamHandler()\nlogger.addHandler(ch)\n\nclass Cbdep:\n    def __init__(self):\n        cachedir = pathlib.Path.home() / \".cbdepcache\"\n        self.cache = Cache(str(cachedir))\n\n    def do_cache(self, args):\n        \"\"\"\n        Cache a URL\n        \"\"\"\n\n        self.cache.get(args.url)\n\n    def do_platform(self, args):\n        \"\"\"\n        Display introspected platform information\n        \"\"\"\n\n        logger.debug(\"Determining platform...\")\n        print (get_platforms())\n\n    def do_install(self, args):\n        \"\"\"\n        Install a package based on a descriptor YAML\n        \"\"\"\n\n        yamlfile = args.config_file\n        if yamlfile is None:\n            if getattr( sys, 'frozen', False ) :\n                # running in a bundle\n                mydir = pathlib.Path(sys._MEIPASS)\n            else :\n                # running live\n                mydir = pathlib.Path.home()\n            yamlfile = str(mydir / \"cbdep.config\")\n\n        installdir = args.dir\n        if installdir is None:\n            # QQQ\n            installdir = \"install\"\n        installdir = str(pathlib.Path(installdir).resolve())\n\n        installer = Installer.fromYaml(yamlfile, self.cache, args.platform)\n        installer.install(args.package, args.version, args.x32, installdir)\n\ndef main():\n    \"\"\"\n    \"\"\"\n\n    # PyInstaller binaries get LD_LIBRARY_PATH set for them, and that\n    # can have unwanted side-effects for our own subprocesses. Remove\n    # that here - it can still be set by an env: entry in cbdep.config\n    # for an install directive.\n    # This needs to be done very early - even the call to get_platforms()\n    # below indirectly shells out to lsb_release.\n    os.environ.pop(\"LD_LIBRARY_PATH\", None)\n\n    parser = argparse.ArgumentParser(\n        description='Dependency Management System'\n    )\n    parser.add_argument('-d', '--debug', action='store_true',\n                        help='Enable debugging output')\n    parser.add_argument('-p', '--platform', type=str,\n                        default=get_platforms(),\n                        help=\"Override detected platform\")\n    subparsers = parser.add_subparsers()\n\n    cache_parser = subparsers.add_parser(\n        \"cache\", help=\"Add downloaded URL to local cache\"\n    )\n    cache_parser.add_argument(\"url\", type=str, help=\"URL to cache\")\n    cache_parser.set_defaults(func=Cbdep.do_cache)\n\n    install_parser = subparsers.add_parser(\n        \"install\", help=\"Install a package\"\n    )\n    install_parser.add_argument(\"package\", type=str,\n        help=\"Package to install\")\n    install_parser.add_argument(\"version\", type=str,\n        help=\"Version to install\")\n    install_parser.add_argument('-3', '--x32', action=\"store_true\",\n        help=\"Download 32-bit package (default false; only works on a few packages)\")\n    install_parser.add_argument(\"-c\", \"--config-file\",\n        type=str, help=\"YAML file descriptor\")\n    install_parser.add_argument(\"-d\", \"--dir\",\n        type=str, help=\"Directory to unpack into (not applicable for all packages)\")\n    install_parser.set_defaults(func=Cbdep.do_install)\n\n    platform_parser = subparsers.add_parser(\n        \"platform\", help=\"Dump introspected platform information\"\n    )\n    platform_parser.set_defaults(func=Cbdep.do_platform)\n\n    args = parser.parse_args()\n\n    # Set logging to debug level on stream handler if --debug was set\n    if args.debug:\n        ch.setLevel(logging.DEBUG)\n\n    # Check that a command was specified\n    if \"func\" not in args:\n        parser.print_help()\n        sys.exit(1)\n\n    cbdep = Cbdep()\n    args.func(cbdep, args)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"cbdep/scripts/cbdep.py","file_name":"cbdep.py","file_ext":"py","file_size_in_byte":3927,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"107289019","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 28 16:39:40 2018\n\n@author: ttt\n\"\"\"\n\n#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 18 12:56:47 2018\n\n@author: ttt\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nimport time\nfrom datetime import datetime\nimport model2 as model\nimport globals as g_\nimport h5py\n#import os\n#import hickle as hkl\n#import os.path as osp\n#from glob import glob\n#import sklearn.metrics as metrics\n#from input import Dataset\n\n\n\n#currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\n#parentdir = os.path.dirname(currentdir)\n#sys.path.append(parentdir)\n\n\nFLAGS = tf.app.flags.FLAGS\ntf.app.flags.DEFINE_string('train_dir', '/media/ttt/Elements/TanYinghui/lung1/train1.hdf5',\n                           \"\"\"h5file of trian data \"\"\"\n                           \"\"\"and checkpoint.\"\"\")\ntf.app.flags.DEFINE_boolean('log_device_placement', False,\n                            \"\"\"Whether to log device placement.\"\"\")\ntf.app.flags.DEFINE_integer('epoch', 300,\n                            \"\"\"Number of training epoch.\"\"\")\n#tf.app.flags.DEFINE_string('val_dir', '/home/ttt/hdf/hdf5/train17.hdf5', \n#                            \"\"\"h5file of val data\"\"\")\ntf.app.flags.DEFINE_string('model', '', \n                            \"\"\"finetune with a model converted by tensorflow\"\"\")\n\nnp.set_printoptions(precision=3)\n\n\ndef train(trainpath,  ckptfile=''):\n    print ('train() called')\n    is_finetune = bool(ckptfile)\n    batch_size = 200\n    data_size = get_datasize(trainpath)\n    print ('training size:', data_size)\n\n\n    with tf.Graph().as_default():\n        startstep = 0 if not is_finetune else int(ckptfile.split('-')[-1])\n        global_step = tf.Variable(startstep, trainable=False)\n         \n        # placeholders for graph input\n        view_ = tf.placeholder('float32', shape=(None, 3, 3, 21, 21), name='im0')\n        y_ = tf.placeholder('int64', shape=(None), name='y')\n        keep_prob_ = tf.placeholder('float32')\n\n        # graph outputs\n        fc8 = model.inference_multiview(view_, g_.NUM_CLASSES, keep_prob_)\n#        pr=tf.nn.softmax(fc8)\n        loss = model.loss(fc8, y_)\n        train_op = model.train(loss, global_step, data_size)\n        prediction = model.classify(fc8)\n\n        # build the summary operation based on the F colection of Summaries\n        summary_op = tf.summary.merge_all()\n\n\n        # must be after merge_all_summaries\n#        validation_loss = tf.placeholder('float32', shape=(), name='validation_loss')\n#        validation_summary = tf.summary.scalar('validation_loss', validation_loss)\n#        validation_acc = tf.placeholder('float32', shape=(), name='validation_accuracy')\n#        validation_acc_summary = tf.summary.scalar('validation_accuracy', validation_acc)\n\n        saver = tf.train.Saver(tf.all_variables(), max_to_keep=1000)\n\n        init_op = tf.global_variables_initializer()\n        sess = tf.Session(config=tf.ConfigProto(log_device_placement=FLAGS.log_device_placement))\n        \n        if is_finetune:\n            # load checkpoint file\n            saver.restore(sess, ckptfile)\n            print ('restore variables done')\n#        elif caffemodel:\n#            # load caffemodel generated with caffe-tensorflow\n#            sess.run(init_op)\n#            model.load_alexnet_to_mvcnn(sess, caffemodel)\n#            print ('loaded pretrained caffemodel:', caffemodel)\n        else:\n            # from scratch\n            sess.run(init_op)\n            print ('init_op done')\n\n        summary_writer = tf.summary.FileWriter(FLAGS.train_dir,\n                                               graph=sess.graph) \n\n        step = startstep\n        for epoch in range(FLAGS.epoch):\n            print ('epoch:', epoch)\n            imfile = h5py.File(FLAGS.train_dir, 'a')\n            \n            nimg = len([k for k in imfile.keys() if k != 'cache'])\n            trainidx = [i for i in range(nimg)]\n            for i, idx in enumerate(trainidx):\n                print('== <<<<trainning on  image %d/%d' %(i+1, len(trainidx)))\n                x, y, c = select_patches_from(imfile,idx)\n                num, nrotate, nscale, kernelsx, kernelsy, n_views = x.shape\n#                num =len(x)\n                print('the number of the image is %d' % num)\n            \n                batch_start = 0 \n                batch_end = 0 \n                while batch_start < num:\n                    step += 1\n                    batch_end = batch_start + batch_size \n                    if batch_end <= num :\n                        \n                        nsample_each = batch_end - batch_start\n                        batch_x = np.zeros((nsample_each, nscale, kernelsx, kernelsy, n_views))\n#                        xuhao = np.arange(batch_x.shape[0])\n#                        np.random.shuffle(xuhao)\n                        batch_y = np.zeros((nsample_each, 1))\n                        print(batch_y.shape)\n    #                    coords = np.zeros((nsample_each, 3))             \n                        batch_x[:, :, :, :, :] = x[batch_start:batch_end,0,:,:,:,:]\n                        batch_x = batch_x.transpose((0,4,1,2,3))\n                        batch_y = np.squeeze(y[batch_start:batch_end,:])\n                        print(batch_y.shape)\n                        batch_y = batch_y.astype('int64')\n#                        batch_x = batch_x[xuhao,:,:,:,:]\n#                        batch_y = batch_y[xuhao]\n                        print(batch_y)\n                        \n                        start_time = time.time()\n                        feed_dict = {view_: batch_x,\n                                     y_ : batch_y,\n                                     keep_prob_: 0.5 }\n        #                feed_dict_1 = {view_: batch_x,\n        #                             keep_prob_: 0.5 }\n        #                p_fc,p_softmax = sess.run(\n        #                        [fc8,pr],\n        #                        feed_dict=feed_dict_1)\n        #                print(p_fc,p_softmax)\n                        \n                        _, pred, loss_value,ffc = sess.run(\n                                [train_op, prediction,  loss,fc8],\n                                feed_dict=feed_dict)\n        #                print(ffc)\n                        print(pred)\n        #                print(loss_value)\n        \n                        duration = time.time() - start_time\n        \n                        assert not np.isnan(loss_value), 'Model diverged with loss = NaN'\n        \n                        # print training information\n                        if step % 10 == 0 or step - startstep <= 30:\n                            sec_per_batch = float(duration)\n                            print ('%s: step %d, loss=%.2f (%.1f examples/sec; %.3f sec/batch)' \\\n                                 % (datetime.now(), step, loss_value,\n                                            FLAGS.batch_size/duration, sec_per_batch))\n        \n        \n        \n                        if step % 100 == 0:\n                            # print 'running summary'\n                            summary_str = sess.run(summary_op, feed_dict=feed_dict)\n                            summary_writer.add_summary(summary_str, step)\n                            summary_writer.flush()\n        \n                        if step % g_.SAVE_PERIOD == 0 and step > startstep:\n        #                    checkpoint_path = os.path.join(FLAGS.train_dir, 'model.ckpt')\n                            saver.save(sess, '/media/ttt/Elements/TanYinghui/lung1/model2d/model2.cpkt', global_step=step)\n                    batch_start = batch_end\n#                    predictions = np.array([])\n#                    val_y = []\n#        val_batch_start = 0\n#        val_batch_end = val_x.shape[0] + 1\n##                    while batch_start < dataset_train.shape[0]:\n##                        step += 1\n##                        batch_end = batch_start + batch_size if batch_end <= dataset_train.shape[0] else dataset_train.shape[0]\n#                                \n#        val_batch_x = val_x[val_batch_start:val_batch_end,:,:,:,:]\n##                        val_batch_y = np.squeeze(val_y[batch_start:batch_end,:])\n##                        batch_start = batch_end\n#        val_feed_dict = {view_: val_batch_x,\n#                         keep_prob_: 1.0 }\n#        pred = sess.run( prediction, feed_dict=val_feed_dict)\n#        predictions = np.hstack((predictions, pred))\n#        p_n = np.argwhere(predictions>0)\n#        print(p_n.shape[0])\n\n\n\ndef main():\n    st = time.time() \n    print ('start loading data')\n\n#    listfiles_train, labels_train = read_lists(g_.TRAIN_LOL)\n#    listfiles_val, labels_val = read_lists(g_.VAL_LOL)\n#    dataset_train = Dataset(listfiles_train, labels_train, subtract_mean=False, V=g_.NUM_VIEWS)\n#    dataset_val = Dataset(listfiles_val, labels_val, subtract_mean=False, V=g_.NUM_VIEWS)\n    trainpath = FLAGS.train_dir\n#    valpath = FLAGS.val_dir\n#    train_x,train_y = loaddata(trainpath)\n    \n#    val_x,val_y = loaddata(valpath)\n    \n    \n#    train_y = np.squeeze(train_y[random_idx, :])\n#    print(train_y.shape)\n\n    print ('done loading data, time=', time.time() - st)\n\n    train(trainpath, FLAGS.model)\n\n#def loaddata(path):\n#    imfile = h5py.File(path, 'a')\n#    nimg = len([k for k in imfile.keys() if k != 'cache'])\n#    trainidx = [i for i in range(nimg)]\n##    trainidx = [i for i in trainidx if i not in testidx]\n#\n#    if nimg==0:\n#        raise Exception('There are one or more test idx out of bound')\n#\n#    train_x = []\n#    train_y = []\n#\n#    for i, idx in enumerate(trainidx):\n#        print('== Collect patches from image %d/%d' %(i, len(trainidx)))\n#        x, y, c = select_patches_from(imfile,idx)\n#        train_x.append(x)\n#        train_y.append(y)\n#    imfile.close()\n#    print('== All the patches collected')\n#\n#    train_x = np.concatenate(train_x, axis=0)\n#    train_y = np.concatenate(train_y, axis=0)\n#\n##    print('nsample_each:%d\\ttrain_x:%d' %\n##                  (nsample_each, train_x.shape[0]))\n#\n#            # Shuffle the indices\n#    random_idx = np.arange(train_y.shape[0])\n#    np.random.shuffle(random_idx)\n#    train_x = train_x[random_idx, :, :, :, :]\n#    train_x = train_x.transpose((0,4,2,3,1))\n#    \n#    train_y = train_y[random_idx, :]\n#    return train_x,train_y\ndef get_datasize(path):\n    size = 0\n    f=h5py.File(path, \"r\")\n    img_names = [k for k in f['/'].keys() if k != 'cache']\n    for i in range(len(img_names)):\n        x = f[img_names[i]]['data']['x']\n        n, nrotate, nscale, kernelsz, _, _ = x.shape\n        size = size + n\n    f.close\n    return size\ndef select_patches_from(imfile,idx):\n    \n    img_names = [k for k in imfile['/'].keys() if k != 'cache']\n    x = imfile[img_names[idx]]['data']['x']\n\n#    if binary:\n    y = imfile[img_names[idx]]['data']['label']\n#    else:\n#        y = imfile[img_names[idx]]['data']['dist']\n    c = imfile[img_names[idx]]['data']['c']  # Total number of locations\n#    n, nrotate, nscale, kernelsz, _, _ = x.shape\n#\n#    # Sample half with zeros\n#    y_np = np.squeeze(np.array(y))\n#    nsample_each = len(y_np)\n#    # Convert y to numpy array\n#    zero_idx = np.argwhere(y_np == 0)\n#    nonzero_idx = np.argwhere(y_np > 0)\n#    np.random.shuffle(zero_idx)\n#    np.random.shuffle(nonzero_idx)\n#\n##    if binary:\n#    nsample_each_cls = int(np.floor(nsample_each / 2))\n#    sample_idx = np.concatenate(\n#            (zero_idx[:nsample_each_cls if zero_idx.size > nsample_each_cls\n#                          else zero_idx.size],\n#                 nonzero_idx[:nsample_each_cls if zero_idx.size >\n#                             nsample_each_cls else zero_idx.size]))\n##    else:\n##        nsample_nonzero = int(np.floor(nsample_each * 1 / 4))\n##        nsample_zero = int(np.floor(nsample_each * 3 / 4))\n##        sample_idx = np.concatenate(\n##            (zero_idx[:nsample_zero\n##                      if zero_idx.size > nsample_zero else zero_idx.size],\n##                 nonzero_idx[:nsample_nonzero if zero_idx.size >\n##                             nsample_nonzero else zero_idx.size]))\n#\n#    # np.random.shuffle(sample_idx)\n#    sample_idx = np.squeeze(sample_idx)\n#    nsample_each = sample_idx.size\n#    print('sample_idx', sample_idx.shape)\n#\n#    # Determine the depth of the block\n#    #    if self._patch_type == '25d':\n#    #        block_depth = 3\n#    #    elif self._patch_type == 'nov':\n#    #        block_depth = 9\n#    #    elif self._patch_type == '3d':\n#    #        block_depth = 3\n#    \n#    block_depth = g_.NUM_VIEWS\n#\n#    # Claim memory for the patches\n#    patches = np.zeros(\n#        (nsample_each, 1, kernelsz, kernelsz, block_depth))\n#    groundtruth = np.zeros((nsample_each, 1))\n#    coords = np.zeros((nsample_each, 3))\n#\n#    # for i, idx in enumerate(tqdm(sample_idx)):\n#    sample_idx = np.sort(sample_idx)\n#    patch_idx = np.arange(len(sample_idx))\n#    patches[patch_idx, :, :, :, :] = x[sample_idx, 0, :, :, :, :]\n#    groundtruth[patch_idx, :] = y[sample_idx, :]\n#    coords[patch_idx] = c[sample_idx, :]\n#\n#    print('Nonzeros: %d/%d' % (np.count_nonzero(groundtruth), groundtruth.size))\n#\n    return x, y, c\n\n\n#def read_lists(list_of_lists_file):\n#    listfile_labels = np.loadtxt(list_of_lists_file, dtype=str).tolist()\n#    listfiles, labels  = zip(*[(l[0], int(l[1])) for l in listfile_labels])\n#    return listfiles, labels\n    \n\n\nif __name__ == '__main__':\n    main()\n\n\n","sub_path":"python/trainmv_1.py","file_name":"trainmv_1.py","file_ext":"py","file_size_in_byte":13460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"478319732","text":"import time\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport tensorflow as tf\n\nclass Dataset(object):\n    def __init__(self, x, y):\n        self.__x = x\n        self.__y = y\n\n    def get_x(self, type):\n        return self.__x[type]\n\n    def get_y(self, type):\n        return self.__y[type]\n\n    def get_data(self, type):\n        return (self.__x[type], self.__y[type])\n\n    def get_len(self, type):\n        return len(self.__x[type])\n\n\n\ndef data_gen(file_path, n_node, train_pct=0.8, val_pct=0.1, shuffle=True):\n    '''\n    Source file load and dataset generation.\n    :param file_path: str, the file path of data source.\n    :param n_node: int, the number of nodes in the graph.\n    :return: dict, dataset that contains training, validation and test.\n    '''\n    # generate training, validation and test data\n    try:\n        data = pickle.load(open(file_path,'rb'))\n        # data[0] # snapshots a list of #len_seq iterations\n        # data[1] # source node index [len_seq, 1]\n    except FileNotFoundError:\n        print(f'ERROR: input file was not found in {file_path}.')\n\n    n_tot = len(data[0])\n\n    ind_list = list(range(n_tot))\n\n    if shuffle:\n        np.random.shuffle(ind_list)\n\n    n_train = int(n_tot * train_pct)\n    n_val = int(n_tot * val_pct)\n\n    x_train = np.array(data[0])[ind_list[:n_train]]\n    y_train = np.array(data[1])[ind_list[:n_train]]\n\n    x_val = np.array(data[0])[ind_list[n_train:(n_train+n_val)]]\n    y_val = np.array(data[1])[ind_list[n_train:(n_train+n_val)]]\n\n    x_test = np.array(data[0])[ind_list[(n_train+n_val):]]\n    y_test = np.array(data[1])[ind_list[(n_train+n_val):]]\n\n\n    x_data = {'train': x_train, 'val': x_val, 'test': x_test}\n    y_data = {'train': y_train, 'val': y_val, 'test': y_test}\n\n    dataset = Dataset(x_data, y_data)\n\n    return dataset\n\n\ndef gen_xy_batch(inputs, batch_size, dynamic_batch=False, shuffle=False):\n    '''\n    Data iterator in batch.\n    :param inputs: (x,y)\n    :x: np.ndarray, [len_seq, n_frame, n_node, n_feat]\n    :y: np.ndarray, [len_seq, 1]\n    :param batch_size: int, the size of batch.\n    :param dynamic_batch: bool, whether changes the batch size in the last batch if its length is less than the default.\n    :param shuffle: bool, whether shuffle the batches.\n    '''\n    x = inputs[0]\n    y = inputs[1]\n\n    len_inputs = len(x)\n\n    if shuffle:\n        idx = np.arange(len_inputs)\n        np.random.shuffle(idx)\n\n    for start_idx in range(0, len_inputs, batch_size):\n        end_idx = start_idx + batch_size\n        if end_idx > len_inputs:\n            if dynamic_batch:\n                end_idx = len_inputs\n            else:\n                break\n        if shuffle:\n            slide = idx[start_idx:end_idx]\n        else:\n            slide = slice(start_idx, end_idx)\n\n        yield (x[slide], y[slide])\n\n\n\n# x_batch: len_seq sparse_matrix\n# sparse_matrix has shape [n_frame, N]\n# return: dense array, [len_seq, n_frame, N]\n#def sparse2dense(x_batch):\n#    return np.array([a.todense().tolist() for a in x_batch])\n\n\n# sample one snapshot at ind\ndef sample_at(iterations, ind):\n    n = len(iterations)\n    if ind > n:\n        raise Exception('step must be < len(iterations)')\n\n    m = min(ind+1,n)\n\n    g = iterations[0]['status'].copy()\n    N = len(g)\n    for step in range(1,m):\n        s = iterations[step]['status']\n        if len(s) > 0:\n            for k in s:\n                g[k] = s[k]\n\n    snapshot = [g[k] for k in range(N)]\n\n    return snapshot\n\n\n# sample one snapshot from an iteration\ndef sample_one_snapshot(iteration, start, end=-1, random=True, ind=None):\n    if end==-1:\n        end = len(iteration)\n    else:\n        end = min(end, len(iteration))\n\n    if random:\n        ind = np.random.choice(range(start,end))\n    elif ind == None:\n        raise Exception('Must provide ind if random is False')\n    elif ind < start or (end > 0 and ind > end):\n        raise Exception('ind must be > start and < end')\n\n    res = sample_at(iteration, ind)\n\n    return res\n\n\n\n# sample one snapshot per iteration in a batch\ndef iteration2snapshot(iterations, start, end, random=True, ind=None):\n    return np.array([sample_one_snapshot(x, start, end, random, ind) for x in iterations])\n\n\ndef onehot(a, n, axis=-1, dtype=int):\n    pos = axis if axis >= 0 else a.ndim + axis + 1\n    shape = list(a.shape)\n    #shape.insert(pos, a.max() + 1)\n    shape.insert(pos, n)\n    out = np.zeros(shape, dtype)\n    ind = list(np.indices(a.shape, sparse=True))\n    ind.insert(pos, a)\n    out[tuple(ind)] = True\n    return out\n\n\n# ------------------------\n# for real world cases\n# -------------------------\n\ndef single_data_gen(file_path):\n    '''\n    Source file load and dataset generation.\n    :param file_path: str, the file path of data source.\n    :return: dict, dataset that contains training, validation and test.\n    '''\n    # generate training, validation and test data\n    try:\n        data = pickle.load(open(file_path,'rb'))\n    except FileNotFoundError:\n        print(f'ERROR: input file was not found in {file_path}.')\n\n    x_data = {'test': data[0]}\n    y_data = {'test': data[1]}\n    #y_data = {'test': [0]}\n\n    dataset = Dataset(x_data, y_data)\n\n    return dataset\n\n\n\n\nif __name__=='__main__':\n    sfile ='../data/ER/SIR/SIR_Rzero1-15_gamma0.1-0.4_ls2000_nf16_N1000_p0.02_g0_entire.pickle'\n    n = 1000\n    train_pct, val_pct = 0.8, 0.1\n    batch_size = 16\n    random = True\n    n_channel = 3\n\n    start = 0\n    end = -1\n\n    dataset = data_gen(sfile, n, train_pct, val_pct)\n\n    for x_batch, y_batch in gen_xy_batch(dataset.get_data('train'), batch_size, dynamic_batch=True,\\\n    shuffle=True):\n        x_batch_ = onehot(iteration2snapshot(x_batch, start=start, end=end, random=random),\\\n        n_channel)\n\n        print(x_batch_)\n        print(y_batch)\n\n\n\n\n\n\n","sub_path":"baselines/finding_zero/pygcn/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":5770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"110459395","text":"\nfrom . import towers, field, monster, painter\n\nBOARD_LENGTH = 100\nTOTAL_REACTOR_LOOPS = 160 * 5   # 5 x path length\nTOTAL_MONSTERS = 100\n\n\ndef go(bootstrap_info, drawing):\n    \"\"\"Receive a dict with positions as keys, and tower type as values, return the score.\"\"\"\n    # bootstrap, set up the towers\n    game_towers = [towers.get_tower(pos, kind) for pos, kind in bootstrap_info.items()]\n    field_board = field.get_board()\n    painter.draw_field(field_board, game_towers)\n\n    score = 0  # negative, every time a monster finished, +1\n    monsters = []\n    remaining_monsters = TOTAL_MONSTERS\n    initial_monster_position = field.get_monster_entrance()\n\n    for step in range(TOTAL_REACTOR_LOOPS):\n        # update the monsters, remove the dead ones\n        for m in monsters:\n            m.update()\n\n        # remove the dead monsters\n        for i in range(len(monsters) - 1, -1, -1):\n            if monsters[i].life <= 0:\n                del monsters[i]\n\n        # add one more monster to the board, if any remaining\n        if remaining_monsters:\n            monsters.append(monster.Monster(initial_monster_position))\n            remaining_monsters -= 1\n\n        # if no monsters left, exit\n        if not monsters and not remaining_monsters:\n            break\n\n        # tell the field to move the monsters\n        fallen = field.move(monsters)\n\n        # draw!\n        painter.draw(monsters, score)\n\n        # remove the fallen monsters (and update the score, they finished!!)\n        score += len(fallen)\n        for m in fallen:\n            monsters.remove(m)\n\n        # if all finished, exit\n        if not monsters and not remaining_monsters:\n            break\n\n        # tell the towers to shoot\n        for t in [t for t in towers if t.pre_shoot()]:\n            in_range_monsters = []\n            for m in monsters:\n                distance = abs(t.position[0] - m.position[0]) + abs(t.position[1] - m.position[1])\n                if t.shooting_range >= distance:\n                    in_range_monsters.append((m, distance))\n            # get monsters for this tower\n            t.shoot(in_range_monsters)\n\n    painter.draw(monsters, score)\n    return score\n","sub_path":"core/reactor.py","file_name":"reactor.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"124215403","text":"from keras import Sequential\nfrom keras.layers import CuDNNLSTM, Conv2D, MaxPooling2D, Dropout, Flatten, Dense, ConvLSTM2D, LSTM, TimeDistributed, \\\n    GlobalAveragePooling3D, Conv3D, MaxPooling3D, AveragePooling3D\n\n\nclass RnnCnnModel:\n    def __init__(self, n_classes):\n        self.n_classes = n_classes\n\n    def rnnCnnModel(self, input_shape):\n        model = Sequential()\n\n        model.add(Conv3D(32, kernel_size=(4, 4, 4), input_shape=(33, input_shape[0], input_shape[1], input_shape[2])))\n        model.add(Dropout(0.1))\n        model.add(AveragePooling3D((2, 2, 2)))\n        model.add(Conv3D(32, (4, 4, 4), activation='relu'))\n        model.add(AveragePooling3D((2, 2, 2)))\n        model.add(Dropout(0.1))\n\n        model.add(ConvLSTM2D(64, (3, 3), return_sequences=True))\n\n        model.add(Conv3D(1, (3, 3, 3), activation='sigmoid', data_format='channels_last'))\n        #model.add(GlobalAveragePooling3D())\n        model.add(Flatten())\n        model.add(Dense(100))\n        model.add(Dense(8, activation=\"softmax\"))\n\n        return model\n","sub_path":"RNN/RNN_model.py","file_name":"RNN_model.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"541135896","text":"import operator\n\nfrom ..helpers.resource_matcher import ResourceMatcher\n\n\ndef process_resource(rows, conditions):\n    for row in rows:\n        if any(func(row[k], v) for func, k, v in conditions):\n            yield row\n\n\ndef filter_rows(equals=tuple(), not_equals=tuple(), resources=None):\n\n    matcher = ResourceMatcher(resources)\n\n    conditions = [\n        (operator.eq, k, v)\n        for o in equals\n        for k, v in o.items()\n    ] + [\n        (operator.ne, k, v)\n        for o in not_equals\n        for k, v in o.items()\n    ]\n\n    def func(rows):\n        if matcher.match(rows.res.name):\n            return process_resource(rows, conditions)\n        else:\n            return rows\n\n    return func\n","sub_path":"dataflows/processors/filter_rows.py","file_name":"filter_rows.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"361885774","text":"from django.conf.urls import url\n\nfrom .views import *\n\n\nurlpatterns = [\n    url(r'^science/$', ScienceItemListView.as_view(), name='science'),\n    url(r'^technology/$', TechnologyItemListView.as_view(), name='technology'),\n    url(r'^entertainment/$', EntertainmentItemListView.as_view(), name='entertainment'),\n    url(r'^music/$', MusicItemListView.as_view(), name='music'),\n    url(r'^$', HomeTemplateView.as_view(), name='home'),\n]\n","sub_path":"web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"21583967","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jun 15 10:00:45 2020\n\n@author: David Palecek\n\n- Testing people's ability to fit some kind of TA data\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import solve_ivp\nfrom scipy.special import erf\n\n\n\n## RHS used for the model\ndef rhs01(s, v, p0, p1, p2, p3): \n    return [-v[0]/p0 - v[0]/p1 + v[1]/p3,\n            -v[1]/p2 + v[0]/p1 - v[1]/p3]\n\n## control without the back transfer component\n## Used by Victor\ndef rhs02(s, v, p0, p1, p2): \n    return [-v[0]/p0 - v[0]/p1,\n            -v[1]/p2 + v[0]/p1]\n\n# component 1, pentacene\nspectra = np.loadtxt('TAS.txt')\nwl = spectra[10:-30,0]\nsp0 = spectra[10:-30,2]*1000\n\n# component 2, p3ht fibers\nsp1 = np.loadtxt('test_data.txt')[20,:]\nwl1 = np.linspace(wl[0], wl[-1], len(sp1))\nsp2 = np.interp(wl, wl1, sp1) \n\nt = np.linspace(-49.9,500, 166)\nres0, res1 = np.zeros((len(t), )), np.zeros((len(t), ))\n\nplt.plot(wl, sp0, label = 'comp 0')\nplt.plot(wl, sp2, label = 'comp 1')\nplt.title('Model spectral components')\nplt.legend()\nplt.show()\n\n## solve ODE\npar = (200,60,300,150)\n\nres = solve_ivp(rhs01, (0.001,500),[1,0.3],args=par,t_eval=t[15:])\nres_control = solve_ivp(rhs02, (0.001,500),[1,0.3],args=par[:-1],t_eval=t[15:])\nres0[15:] = res.y[0]\nres1[15:] = res.y[1]\nres0conv = res0 * erf(t/5)\nres1conv = res1 * erf(t/5)\n\n## control plot, if the backtransfer is possible to get out\nplt.plot(res.y[0], label = 'model')\nplt.plot(res_control.y[0], label= 'no back transf.')\nplt.title('Kin, comp 0')\nplt.legend()\nplt.show()\n\nplt.plot(res.y[1], label = 'model')\nplt.plot(res_control.y[1], label= 'no back transf.')\nplt.title('Kin, comp 0')\nplt.title('Kin, comp 1')\nplt.legend()\nplt.show()\n\n\n###############\nplt.plot(t, res0, ':', color = 'C0', label = 'kin 0')\nplt.plot(t, res1, ':', color = 'C1', label = 'kin 1')\nplt.plot(t, res0conv, color = 'C0', label = 'kin 0 + IRF')\nplt.plot(t, res1conv, color = 'C1', label = 'kin 1 + IRF')\nplt.xlim([-20,50])\nplt.title('IRF convolution')\nplt.legend()\nplt.show()\n\ncomp0 = np.outer(sp0, res0conv)\ncomp1 = np.outer(sp2, res1conv)\nnoise = np.random.normal(0, 0.01, len(wl)*len(t)).reshape(comp0.shape)\nfull = comp0 + comp1 + noise\n\nplt.contourf(t,wl,comp0, 31)\nplt.colorbar()\nplt.show()\n\nplt.contourf(t,wl,comp1, 31)\nplt.colorbar()\nplt.show()\n\nplt.contourf(t,wl,full, 51)\nplt.colorbar()\nplt.show()\n\n# resulting and separate spectra\nplt.plot(wl, sp0, label = 'sol1')\nplt.plot(wl, sp2, label = 'sol2')\nplt.plot(wl, full[:,17], label= 'early')\nplt.plot(wl, full[:,-10], label= 'late')\nplt.legend()\nplt.show()\n\nnp.savetxt('dtt2fit.txt', full, fmt='%10.5f')\nnp.savetxt('wl.txt', wl, fmt='%10.5f')\nnp.savetxt('t.txt', t, fmt='%10.5f')\n\n# ----------------------------------\n#### test victor components \n# ------------------------------------\ncomp = np.loadtxt('components_victor.csv',\n                  delimiter = ',')\nprint(comp.shape)\nres_victor0, res_victor1 = np.zeros((len(t), )), np.zeros((len(t), ))\npar_in = (84,62,264)\nres_victor = solve_ivp(rhs02, (0.001,500),\n                        [1,0.0],args=par_in,\n                        t_eval=t[15:])\n\nres_victor0[15:] = res_victor.y[0]\nres_victor1[15:] = res_victor.y[1]\nres_victor0conv = res_victor0 * erf(t/5)\nres_victor1conv = res_victor1 * erf(t/5)\n\n## control plot, if the backtransfer is possible to get out\nplt.plot(res0conv, label = 'kin0 model')\nplt.plot(res_victor0conv, label = 'kin0 Victor')\nplt.title('Kin0 comparison')\nplt.legend()\nplt.savefig('kin0.png')\nplt.show()\n\nplt.plot(res1conv, label = 'kin1 model')\nplt.plot(res_victor1conv, label = 'kin1 Victor')\nplt.title('Kin1 comparison')\nplt.legend()\nplt.savefig('kin1.png')\nplt.show()\n\n\n## spectra ##\nplt.plot(wl, sp0, label = 'sol1')\nplt.plot(wl, comp[:,1], label = 'Victor 1')\nplt.title('Comp 1 comparison')\nplt.legend()\nplt.savefig('spe0.png')\nplt.show()\n\nplt.plot(wl, sp2, label = 'sol2')\nplt.plot(wl, comp[:,2], label = 'Victor 2')\nplt.title('Comp 2 comparison')\nplt.legend()\nplt.savefig('spe1.png')\nplt.show()\n\n\n## Victor whole map ##\nfull_victor = (np.outer(comp[:,1], res_victor0conv) +\n               np.outer(comp[:,2], res_victor1conv))\n\nplt.contourf(t,wl,full_victor, 51)\nplt.colorbar()\nplt.title('Full TA, Victor')\nplt.show()\n\nplt.contourf(t,wl,full - full_victor, 51)\nplt.colorbar()\nplt.title('Residuals, Model-Victor')\nplt.savefig('residuals.png')\nplt.show()\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"analysis/tests/test_ta_fitting.py","file_name":"test_ta_fitting.py","file_ext":"py","file_size_in_byte":4345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"247730829","text":"#!/usr/local/bin/python\n\nimport RPi.GPIO as GPIO\nimport time\n\n# Definindo os pinos onde estao conectados cada sensor\n# de luminosidade\npin_to_circuit_cerca = 11\npin_to_circuit_alarme = 9\n\ndef getStatus(pin_to_check):\n    GPIO.setmode(GPIO.BCM)\n    exec_count = 0\n    piscou_count = 0\n  \n    #Output on the pin for \n    GPIO.setup(pin_to_check, GPIO.OUT)\n    GPIO.output(pin_to_check, GPIO.LOW)\n    time.sleep(0.2)\n\n    #Change the pin back to input\n    GPIO.setup(pin_to_check, GPIO.IN)\n  \n    #Count until the pin goes low\n    while (exec_count < 140000 ):\n        if (GPIO.input(pin_to_check) == GPIO.LOW):\n            piscou_count += 1\n        exec_count +=1\n    return piscou_count\n\ndef getStatusCercaAlarme():\n     #Catch when script is interrupted, cleanup correctly\n     try:\n          # Lendo quantas vezes o sensor detectou luz no LED do alarme\n          piscou_alarme = getStatus(pin_to_circuit_alarme)\n          if (piscou_alarme > 130000):\n              # Se o LED do alarme estiver constantemente aceso, ou seja, sem piscar,\n              # entao ambos Cerca e Alarme estao ligados.\n              return \"CercaON/AlarmeON:%s\" % piscou_alarme\n          if (piscou_alarme == 0):\n              # Se o LED do alarme nao estiver sequer piscando, entao apenas a \n              # cerca esta ligada, ou ambos estao desligados. Por isso, precisa\n              # checar se o LED da cerca esta piscando\n              piscou_cerca = getStatus(pin_to_circuit_cerca)\n              if (piscou_cerca > 0):\n                   # O LED da cerca esta piscando, entao ela esta ligada. Como ja vimos que o LED\n                   # do alarme nao esta, entao apenas a cerca esta ligada\n                   return \"CercaON:%s/AlarmeOFF:%s\" %(piscou_cerca, piscou_alarme)\n              # Ambos cerca e alarme nao estao piscando. Portanto, ambos estao desligados\n              return \"CercaOFF/AlarmeOFF:%s\" % piscou_alarme\n          \n          # Se o LED do alarme esta piscando, mas nao aceso continuamente, entao ou o alarme\n          # ou a cerca estao ligados.\n          if (piscou_alarme > 0 and piscou_alarme < 130000):\n              # Checando se o LED da cerca pisca.\n              piscou_cerca = getStatus(pin_to_circuit_cerca)\n              if (piscou_cerca > 0):\n                  # Cerca esta piscando, e LED do alarme tambem esta piscando\n                  return \"CercaON:%s/AlarmeOFF:%s\" %(piscou_cerca, piscou_alarme)\n              if (piscou_cerca == 0):\n                  # Cerca nao esta piscando, mas o alarme sim.\n                  return \"CercaOFF:%s/AlarmeON:%s\" %(piscou_cerca, piscou_alarme)\n\n     except KeyboardInterrupt:\n         pass\n    # finally:\n    #     GPIO.cleanup()\n     \n","sub_path":"board_ldr.py","file_name":"board_ldr.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"572781101","text":"# -*- coding: utf-8 -*-\n\"\"\"Defines fixtures available to all tests.\"\"\"\nimport pytest\nfrom faker import Faker\nfrom webtest import TestApp\n\nfrom apps.app import create_app\nfrom apps.database import db as _db\n\nfrom .utils import TestClient\n\nfake = Faker()\n\npytest_plugins = [\"tests.fixtures.sample\"]\n\n\n@pytest.fixture\ndef app():\n    \"\"\"Create application for the tests.\"\"\"\n    _app = create_app(\"tests.settings\")\n    ctx = _app.test_request_context()\n    ctx.push()\n    yield _app\n\n    ctx.pop()\n\n\n@pytest.fixture\ndef testapp(app):\n    \"\"\"Create Webtest app.\"\"\"\n    return TestApp(app)\n\n\n@pytest.fixture\ndef db(app):\n    \"\"\"Create database for the tests.\"\"\"\n    _db.app = app\n    with app.app_context():\n        _db.create_all()\n\n    yield _db\n\n    # Explicitly close DB connection\n    _db.session.close()\n    _db.drop_all()\n\n\n@pytest.fixture(scope=\"function\")\ndef client(app):\n    \"\"\"\n    Setup an app client, this gets executed for each test function.\n    :param app: Pytest fixture\n    :return: Flask app client\n    \"\"\"\n    app.test_client_class = TestClient\n    client = app.test_client()\n    yield client\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"531056001","text":"from django.shortcuts import render\nfrom django.http import JsonResponse\nfrom .models import Article, Folder\n\n\ndef new_story(request):\n    return render(request, 'stories/new_story.html', locals())\n\n\ndef update_story(request, id):\n    article = Article.objects.get(id=id)\n    return render(request, 'stories/update_story.html', locals())\n\n\ndef ajax_save_story(request):\n\n    title = request.GET.get(\"title\")\n    content = request.GET.get(\"content\")\n    folder = request.GET.get(\"folder\")\n\n    print(\"1......\", title)\n    print(\"2......\", content)\n    print(\"3......\", folder)\n\n    article = Article.objects.get(id=1)\n\n    if folder == \"\":\n        article.title = title\n        article.content = content\n    else:\n        print(\"a....\", folder)\n        folder = Folder.objects.get(name=folder)\n        print(\"b....\", folder)\n        article.folder = folder\n\n    article.save()\n\n    response = {}\n\n    return JsonResponse(response)\n","sub_path":"apps/stories/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"205712916","text":"import requests\nimport json\n\nurl = \"https://tianqiapi.com/api\"\nparams_dic = {'appid': '23845672', 'appsecret': 'NhFIJ1tG', 'version': 'v6'}\nheaders = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:77.0) Gecko/20100101 Firefox/77.0'}\n\n\nasync def get_weather_of_city(city: str) -> str:\n    params_dic['city'] = city\n    params_dic['version']='v6'\n    r = requests.get(url, headers=headers, params=params_dic)\n    weather_dic = json.loads(r.content.decode(), encoding='utf8')\n    if city != '北京' and weather_dic['city'] == '北京':\n        return f'喵，{city}的天气暂时查不到喵QAQ'\n    else:\n        return '喵，指挥官所查询的城市{}当前天气情况为{},最低气温{},最高气温{},{}{},空气质量{}\\n' \\\n               '茗喵小提示：{}'.format(weather_dic['city'], weather_dic['wea'], weather_dic['tem'],\n                                 weather_dic['tem1'], weather_dic['win'], weather_dic['win_speed'],\n                                 weather_dic['air_level'], weather_dic['air_tips'])\n\n\nasync def get_report(city: str) -> str:\n    params_dic['version'] = 'v2'\n    params_dic['city'] = city\n    r = requests.get(url, headers=headers, params=params_dic)\n    weather_dic = json.loads(r.content.decode(), encoding='utf8')\n    if city != '北京' and weather_dic['city'] == '北京':\n        return f'喵，{city}的天气暂时查不到喵QAQ'\n    else:\n        report = f'以下是{city}地区未来三天的天气预报喵~\\n'\n        for i in range(3):\n            data_dict = weather_dic['data'][i + 1]\n            report += '{}{},预计天气情况为{},其中白天为{},夜间为{},最高气温为{}°，最低气温为{}°\\n'. \\\n                format(data_dict['date'], data_dict['week'], data_dict['wea'], data_dict['wea_day'],\n                       data_dict['wea_night'], data_dict['tem1'], data_dict['tem2'])\n        return report\n\n\nif __name__ == '__main__':\n    url = \"https://tianqiapi.com/api\"\n    params_dic = {'appid': '23845672', 'appsecret': 'NhFIJ1tG', 'version': 'v2'}\n    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:77.0) Gecko/20100101 Firefox/77.0'}\n    r = requests.get(url, headers=headers, params=params_dic)\n    print(json.loads(r.content.decode(), encoding='utf8'))\n","sub_path":"pcr/plugins/weather/data_source.py","file_name":"data_source.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"423587046","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Nov 23 21:51:11 2018\r\n\r\n@author: MAGESHWARI\r\n\"\"\"\r\nimport os\r\nfrom tkinter import * \r\nfrom tkinter import filedialog\r\nimport re\r\nimport csv\r\n\r\n\r\ndef center_window(w=200, h=500):\r\n    # get screen width and height\r\n    ws = root.winfo_screenwidth()\r\n    hs = root.winfo_screenheight()\r\n    # calculate position x, y\r\n    x = (ws/2) - (w/2)    \r\n    y = (hs/2) - (h/2)\r\n    root.geometry('%dx%d+%d+%d' % (w, h, x, y))\r\n\r\n\r\ndef browse():\r\n    global directory\r\n    global filename\r\n    global string_lst\r\n    global contents\r\n    global total\r\n    total = 0\r\n    filepath =  filedialog.askopenfilename(initialdir = \"/\",title = \"Select file\",filetypes = ((\"MDL files\",\"*.mdl\"),(\"all files\",\"*.*\")))\r\n    select_file_field.insert(0,filepath) # insert the path in textbox\r\n    directory = os.path.dirname(filepath)\r\n    filename_ext=os.path.basename(filepath)\r\n    #print (filename_ext)\r\n    filename=os.path.splitext(filename_ext)[0]\r\n    #print(filename)\r\n    file = open(filepath,'r')  # open the selected file\r\n    contents = file.read()\r\n    string_lst = ['BusSelector','DiscretePulseGenerator','TransferFcn','SignalGenerator','Scope','Reference']\r\n    for word in string_lst:\r\n        count = sum(1 for match in re.findall(r\"\\s\"+word+\"\\s\",contents))\r\n        total+=count\r\n    print (\"Total : \"+str(total))\r\n    \r\ndef submit():\r\n    #total_block_field.set(total)\r\n    total_block.configure(text=\"Total Blocks: \"+str(total))\r\n    txt_file= open(os.path.join(directory,filename+\".txt\"),'w')\r\n    for word in string_lst:\r\n        count = sum(1 for match in re.findall(r\"\\s\"+word+\"\\s\",contents))\r\n        txt_file.write(word)\r\n        txt_file.write(\":\")\r\n        txt_file.write(str(count))\r\n        txt_file.write(\"\\n\")\r\n    txt_file.close()\r\n    \r\n    with open(os.path.join(directory,filename+\".txt\"),'r') as in_file:\r\n        stripped = (line.strip() for line in in_file)\r\n        lines=(line.split(\":\") for line in stripped if line)\r\n        with open(os.path.join(directory,filename+\".csv\"),'w') as out_file:\r\n            writer=csv.writer(out_file)\r\n            writer.writerow((\"WORD\",\"COUNT\"))\r\n            writer.writerows(lines)\r\n        \r\ndef reset():\r\n    select_file_field.delete(0, END)\r\n    total_block.destroy()\r\n    make_label()\r\n    \r\ndef make_label():\r\n    global total_block\r\n    total_block = Label(root,text=\" \",font=(\"Times New Roman\",12))\r\n    total_block.grid(row=2,column=1,sticky='w')\r\n    \r\nif __name__==\"__main__\":\r\n    \r\n    root = Tk()\r\n    root.resizable(0,0) # to disable the maximize button\r\n    root.title(\"DATA COUNT TOOL\")\r\n    root.configure(pady=60)\r\n\r\n    select_file = Label(root, text=\"Select the File : \",width=12)\r\n    select_file.grid(column=0, row=0,sticky='w')\r\n    \r\n    select_file_field = Entry(root,width=50)\r\n    select_file_field.grid(column=1, row=0)\r\n    \r\n    browse = Button(root, text=\"....\",width=5, command=browse)\r\n    browse.grid(column=2, row=0,sticky='w')\r\n    submit = Button(root, text=\"Submit\",width=16, command=submit)\r\n    submit.grid(column=1, row=1,sticky='w',padx=5,pady=5)\r\n    reset = Button(root, text=\"Reset\",width=16, command=reset)\r\n    reset.grid(column=1, row=1,sticky='e',padx=5,pady=5)    \r\n    \r\n    total_block = Label(root,text=\" \",font=(\"Times New Roman\",12))\r\n    total_block.grid(row=2,column=1,sticky='w')\r\n    \r\n    #total_block_field = IntVar()\r\n    #Label(root,textvariable=total_block_field)\r\n    #total_block_field.grid(row=2,column=1,sticky='e')\r\n    \r\n    root.iconbitmap(r\"C:\\Users\\MAGESHWARI\\Desktop\\images.ico\")\r\n    center_window(500,200)  # to make the application window centered\r\n    root.mainloop()","sub_path":"data_count_tool.py","file_name":"data_count_tool.py","file_ext":"py","file_size_in_byte":3643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"593956925","text":"from django.conf.urls import include, url\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.CategoryListView.as_view(), name='categories'),\n    url(r'^recently_added$', views.RecentProductListView.as_view(), name='recent_products'),\n    url(r'^(?P<category_slug>[-\\w\\d]+)$', views.CategoryDetailView.as_view(), name='products'),\n    url(r'^(?P<category_slug>[-\\w\\d]+)/(?P<product_slug>[-\\w\\d]+)$', views.ProductDetailView.as_view(),\n        name='product_detail'),\n\n]\n","sub_path":"project/product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"298398895","text":"# This file provides functions to calculate the interests on a trustline off-chain\n\nSECONDS_PER_YEAR = 60 * 60 * 24 * 365\nINTERESTS_DECIMALS = 2\nDELTA_TIME_MINIMAL_ALLOWED_VALUE = -60\n\n\ndef _ensure_non_negative_delta_time(delta_time):\n    \"\"\"make sure the delta_time - used for computation of interests - is mostly\n    positive. Every value passed in that's less than\n    DELTA_TIME_MINIMAL_ALLOWED_VALUE will trigger a ValueError.\n\n    see https://github.com/trustlines-protocol/relay/issues/279\n    \"\"\"\n    if delta_time < DELTA_TIME_MINIMAL_ALLOWED_VALUE:\n        raise ValueError(\"delta_time out of bounds\")\n    return max(delta_time, 0)\n\n\ndef calculate_interests(\n    balance: int,\n    internal_interest_rate: int,\n    delta_time_in_seconds: int,\n    highest_order: int = 15,\n) -> int:\n    delta_time_in_seconds = _ensure_non_negative_delta_time(delta_time_in_seconds)\n    intermediate_order = balance\n    interests = 0\n    # Calculate compound interests using taylor approximation\n    for order in range(1, highest_order + 1):\n        intermediate_order = int(\n            intermediate_order\n            * internal_interest_rate\n            * delta_time_in_seconds\n            / (SECONDS_PER_YEAR * 100 * 10 ** INTERESTS_DECIMALS * order)\n        )\n\n        if intermediate_order == 0:\n            break\n        interests += intermediate_order\n\n    return interests\n\n\ndef balance_with_interests(\n    balance: int,\n    internal_interest_rate_positive_balance: int,\n    internal_interest_rate_negative_balance: int,\n    delta_time_in_seconds: int,\n) -> int:\n    delta_time_in_seconds = _ensure_non_negative_delta_time(delta_time_in_seconds)\n    if balance > 0:\n        interest = calculate_interests(\n            balance, internal_interest_rate_positive_balance, delta_time_in_seconds\n        )\n    else:\n        interest = calculate_interests(\n            balance, internal_interest_rate_negative_balance, delta_time_in_seconds\n        )\n    total = balance + interest\n    assert isinstance(total, int)\n    return total\n","sub_path":"py-deploy/tldeploy/interests.py","file_name":"interests.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"504722837","text":"import cc_data\nimport json\n\n\n\ndef make_level_from_json(json_file):\n\n    cc_level = cc_data.CCLevel()\n    cc_level.level_number = json_file[\"level_number\"]\n    #cc_level.optional_fields = make_optional_fields_from_json(json_datap[\"optional fields\"])\n    return cc_level\n\ndef make_cc_data_from_json(json_file):\n    cc_data_file = cc_data.CCDataFile()\n    with open(json_file, 'r') as reader:\n        level_data = json.load(reader)\n        #level_data and json_file are NOT the same\n        for json_level in level_data:\n            cc_level = make_level_from_json(json_level)\n            cc_data_file.add_level(cc_level)\n    return cc_data_file","sub_path":"cc_json_utils.py","file_name":"cc_json_utils.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"228124916","text":"from numpy import *\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\nimport pylab\nfrom numpy import genfromtxt\n\n# Load the data set (wine). Variable data stores the final data (178 x 13)\nmy_data = [[-1, -1], [0, 0], [1, 1]]\ndata = my_data\n\nM = mean(data, 0)\nC = data - M\nprint(C)\nW = dot(C.T, C)\nprint(W)\neigval, eigvec = linalg.eig(W) #compute eigenvalues and eigenvectors of covariance matrix\nidx = eigval.argsort()[::-1] #sorting of eigenvalues\neigvec = eigvec[:,idx] #sort eigenvetors according to eigenvalues\n\nnewData2 = dot(C,real(eigvec[:,:1])) # Project the data to the new space (2-D)\n\nprint(eigval)\nprint(eigvec)\nprint(newData2)\n","sub_path":"Individual Lab/Lab 1/PCA/untitled.py","file_name":"untitled.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"583726295","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/xaralis/Workspace/elladev/ella/test_ella/settings/config.py\n# Compiled at: 2013-09-22 07:03:20\nADMINS = ()\nMANAGERS = ADMINS\nDEBUG = False\nTEMPLATE_DEBUG = DEBUG\nDISABLE_CACHE_TEMPLATE = DEBUG\nDATABASES = {'default': {'ENGINE': 'django.db.backends.sqlite3', \n               'NAME': ':memory:'}}\nTEST_CORE_REDIS = PHOTOS_REDIS = {'db': 15}\nTIME_ZONE = 'Europe/Prague'\nLANGUAGE_CODE = 'en-us'\nSITE_ID = 1\nSECRET_KEY = '88b-01f^x4lh$-s5-hdccnicekg07)niir2g6)93!0#k(=mfv$'\nCACHES = {'default': {'BACKEND': 'django.core.cache.backends.dummy.DummyCache'}}\nUSE_PRIORITIES = True","sub_path":"pycfiles/ella-3.0.13.tar/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"350971436","text":"# -*- coding:utf-8 -*-\nimport torch\nimport torch.nn as nn\n\n\nclass Convolution(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size, stride, padding=0):\n        super().__init__()\n        self.conv = nn.Sequential(\n                                    nn.Conv1d(in_channels=in_channels, out_channels=out_channels,\n                                              kernel_size=kernel_size, stride=stride, padding=padding),\n                                    nn.InstanceNorm1d(out_channels),\n                                    nn.ReLU(),\n                                    )\n    def forward(self, x):\n        return self.conv(x)\n\n\nclass SignNet(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.conv = nn.Sequential(\n                                    Convolution(1, 16, 3, 1),\n                                    Convolution(16, 32, 3, 1),\n                                    Convolution(32, 64, 3, 1),\n                                    Convolution(64, 128, 3, 1),\n                                    )\n        self.linear = nn.Sequential(\n                                    nn.Linear(128, 1),\n                                    nn.Sigmoid(),\n                                    )\n\n    def forward(self, x):\n        x = x.reshape(shape=(1, 1, -1))\n        output_conv = self.conv(x)\n        input_linear = output_conv.reshape(shape=(1, -1))\n        output_linear = self.linear(input_linear)\n        return output_linear\n\n\nif __name__ == '__main__':\n    net = SignNet()\n    x = [i for i in range(9)]\n    print(net(x))","sub_path":"1DConv_Signal/net.py","file_name":"net.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"417738016","text":"import sys\nfrom zope.app.pagetemplate.simpleviewclass import simple as SimplePTPage\nfrom zope.app.publisher.browser.viewmeta import simple as SimplePage\nfrom zope.publisher.browser import BrowserPage\nfrom zope.viewlet import viewlet, manager\nfrom z3c.pagelet.browser import BrowserPagelet\n\nfrom z3c.reload import BLATHER\n\nsimple_view_classes = (\n    SimplePTPage, SimplePage, BrowserPage,\n    viewlet.ViewletBase, manager.ViewletManagerBase,\n    BrowserPagelet)\n\n\nclass Reloader(object):\n    \"\"\"A mixin to be used on SimpleViewClass instances.\n\n    These SimpleViewClass instances are in fact dynamically constructed types\n    with either zope.app.pagetemplate.simpleviewclass.simple or\n    zope.app.publisher,browser.viewmeta as one of the base classes.\n\n    This mixin must be the first superclass, because its __init__ must be\n    called on instantiation.\n    \"\"\"\n\n    def __init__(self, *args, **kw):\n        bases = self.__class__.__bases__\n        assert len(bases) >= 2\n        reloader2, real_view = bases[:2]\n        assert reloader2 is Reloader\n\n        rest = bases[2:]\n\n        clsname = real_view.__name__\n        modname = real_view.__module__\n        module = sys.modules[modname]\n        if hasattr(module, clsname):\n            reload(module)\n            new_view = getattr(module, clsname)\n            self.__class__.__bases__ = (Reloader, new_view) + rest\n        else:\n            # If the module does not have such an attribute, chances are that\n            # the class was dynamically constructed.  In this case reloading is\n            # likely to break so we don't do it.\n            new_view = real_view # just use the old view\n\n        self.__sanitize_bases(new_view)\n        # This is generally not the right thing to do, but should work in this\n        # limited case.\n        for base in self.__class__.__bases__[1:]:\n            try:\n                base.__init__(self, *args, **kw)\n            except TypeError:\n                # Needed as object.__init__() does not accept any arguments in py26.\n                base.__init__(self)\n\n    def __sanitize_bases(self, cls):\n        \"\"\"Make sure that the bases of a class are in the scope.\n\n        This works around the problem when a class bases do not correspond\n        to the same-named classes in the scope, which could happen after a\n        reload.  This causes errors when invoking base clases\n        \"\"\"\n        modname = cls.__module__\n        module = sys.modules[modname]\n\n        bases = cls.__bases__\n        new_bases = []\n        for b in bases:\n            bc = getattr(module, b.__name__, None)\n            if bc is not None and bc.__module__ != modname:\n                # Make sure that the base class comes from a different module.\n                new_base = getattr(module, b.__name__)\n                self.__sanitize_bases(new_base)\n                new_bases.append(new_base)\n            else:\n                # Couldn't find class in local scope, abort.\n                break\n        else:\n            # All bases checked successfully.\n            cls.__bases__ = tuple(new_bases)\n\n\n\ndef install_reloader(view_class):\n    \"\"\"Install the Reloader mixin on view_class.\"\"\"\n    if BLATHER:\n        print >> sys.stderr, 'Reloader installed for', view_class.__bases__[0]\n    view_class.__bases__ = (Reloader, ) + view_class.__bases__\n","sub_path":"z3c.reload/trunk/src/z3c/reload/reload.py","file_name":"reload.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"570418066","text":"import numpy as np\n#Linear Ordering Based On Spectral Clustering\n#Ding, C., & He, X. (2004). Linearized Cluster Assignment via Spectral Ordering.\n# In Proceedings of the 21st International Conference on Machine Learning.\n\ndef sqrtm(mat):\n    '''\n    Returns a square root of a matrix\n    :param mat: 2 dimensional numpy array or an equivalent list of lists. It should represent a postitive semidefinite matrix\n    :return: 2 dimensional numpy array of the same shape as the input\n    '''\n\n    sh = np.shape(mat)\n\n    assert sh[0] == sh[1]\n    assert (mat == mat.T).all()\n\n    n = sh[0]\n\n    u, evals, v = np.linalg.svd(mat) #SVD (Singular Value Decomposition)\n\n    assert all(evals >= 0)\n\n    sqEvals = np.diagflat(np.sqrt(evals))\n\n    return np.dot(u, np.dot(sqEvals, v))\n\n\ndef linearOrdering(similiarityMatrix):\n    '''\n    Uses spectral clustering algorithm to return a linear ordering\n    :param similiarityMatrix: square numpy array for similarities\n    :return: numpy array containing same number of elements as rows as the input.\n    '''\n\n    matW = similiarityMatrix\n    sh = np.shape(matW)\n\n    assert sh[0] == sh[1]\n    assert (matW == matW.T).all()\n\n    matD = np.diagflat(matW.sum(axis=1)) #matD = D\n\n    rootMatD = sqrtm(matD) #rootmatD = D^1/2\n\n    invRootMatD = np.linalg.inv(rootMatD) #invRootMatD = D^-1/2\n\n    matA = np.dot(invRootMatD, np.dot(matW, invRootMatD)) #matA = D^-1/2*W*D^-1/2\n\n    w, v = np.linalg.eigh(matA) #w = eigenvalues, v = matrix with eigenvectors (columnwise)\n\n    z1 = v[:, w.argsort()[-2]] #z1 = first eigenvector\n\n    q1 = np.dot(invRootMatD, z1) #q1 = D^-1/2*z1\n\n    ordering = np.argsort(q1) #indices that sort q1\n\n    matW_rowOrdered = matW[ordering, :]\n\n    matW_ordered = matW_rowOrdered[:, ordering]\n\n    return ordering, matW_ordered\n\ndef linearOrdering_con(similiarityMatrix):\n    '''\n    Uses spectral clustering algorithm to return a linear ordering\n    :param similiarityMatrix: square numpy array for similarities\n    :return: numpy array containing same number of elements as rows as the input.\n    '''\n\n    matW = similiarityMatrix\n    sh = np.shape(matW)\n\n    assert sh[0] == sh[1]\n    assert (matW == matW.T).all()\n\n    matD = np.diagflat(matW.sum(axis=1)) # matD = D\n\n    rootMatD = sqrtm(matD) # rootmatD = D^1/2\n\n    invRootMatD = np.linalg.inv(rootMatD) # invRootMatD = D^-1/2\n\n    matA = np.dot(invRootMatD, np.dot(matW, invRootMatD)) # matA = D^-1/2 * W * D^-1/2\n\n    matB = np.dot(rootMatD, np.dot(matA, rootMatD)) # matB = D^1/2 * D^-1/2*W*D^-1/2 * D^1/2\n\n    w, v = np.linalg.eigh(matB) # w = eigenvalues, v = matrix with eigenvectors (columnwise)\n\n    z1 = v[:, w.argsort()[-2]] # z1 = first eigenvector\n\n    q1 = np.dot(invRootMatD, z1) # q1 = D^-1/2*z1\n\n    ordering = np.argsort(q1) # indices that sort q1\n\n    matW_rowOrdered = matW[ordering, :]\n\n    matW_ordered = matW_rowOrdered[:, ordering]\n\n    return ordering, matW_ordered\n\n\n\ndef calcClusterCrossing(matW, m):\n    '''\n    Calculates the cluster crossing\n    :param matW: square numpy array representing the similarity matrix\n    :param m: bandwidth along the antidiagonals to use.\n    :return: a list of (matW.shape - 1) elements containing cluster crossings.\n    '''\n\n    sh = np.shape(matW)\n\n    assert sh[0] == sh[1]\n    assert (matW == matW.T).all()\n\n    n = sh[0]\n\n    matWRev = matW[:, ::-1]\n\n    crossings = []\n\n    for offset in np.arange(n - 2, -(n - 1), -2):\n\n        dia = np.diagonal(matWRev, offset)\n        #len(dia) is even\n        mid = len(dia) / 2\n        sigi = sum(dia[mid: min(mid + m, len(dia) - 1)])\n        sigim = 0\n        sigip = 0\n        if offset > -(n - 2):\n            diam = np.diagonal(matWRev, offset - 1)\n            #len(diam) is odd; need to exclude the element on the diagonal of matW (antidiagonal of matWRev)\n            mid = len(diam) / 2 + 1\n            sigim = sum(diam[mid: min(mid + m, len(dia) - 1)])\n\n        if offset < n - 2:\n            diap = np.diagonal(matWRev, offset + 1)\n            #len(diap) is odd; need to exclude the element on the diagonal of matW (antidiagonal of matWRev)\n            mid = len(diap) / 2 + 1\n            sigip = sum(diap[mid: min(mid + m, len(dia) - 1)])\n\n        crossing = 0.5 * sigi + 0.25 * sigim + 0.25 * sigip\n\n        if len(dia) <= 2 * m:\n\n            crossing *= 2 * m / float(len(dia))\n\n        crossings.append(crossing)\n\n    return crossings\n\n\n\n\n\n\n\n","sub_path":"LOBOSC.py","file_name":"LOBOSC.py","file_ext":"py","file_size_in_byte":4372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"375147484","text":"import RPi.GPIO as GPIO\nfrom time import sleep\n\n# \"Constants\"\nIO_RIGHT = 3\nIO_LEFT = 5\nIO_FORWARD = 8\nIO_BACK = 10\n\nbuttons = [IO_RIGHT, IO_LEFT, IO_FORWARD, IO_BACK]\n\nduration = 0.1\n\nGPIO.setmode(GPIO.BOARD) # pin 1 top left of header\n\nGPIO.setup(IO_RIGHT, GPIO.OUT) # right\nGPIO.setup(IO_LEFT, GPIO.OUT) # left\nGPIO.setup(IO_FORWARD, GPIO.OUT) # forward\nGPIO.setup(IO_BACK, GPIO.OUT) # back\n\npattern = [[\"FS\", 0.25],\n           [\"NS\", 0.1],\n           [\"FS\", 0.15],           \n           [\"NS\", 0.1],\n           [\"FL\", 0.1],\n           [\"FS\", 0.15],\n           [\"FS\", 0.1],\n           [\"FL\", 0.1],\n           [\"FS\", 0.15],\n#           [\"S\", duration],\n#           [\"N\", duration]\n           ]\n\ndef dirWithDur(direction = 0, duration = 0):\n    # pass in 0 to 2 in alphabet of (FNB)(RSL)\n    # if called with no args, unpress all buttons\n    print(str(direction) + \" \" + str(duration))\n    if (direction == 0):\n        for b in buttons:\n            GPIO.output(b, GPIO.HIGH) # button off\n        sleep(duration)\n        return\n    if \"S\" in direction: # straight, no turning\n        GPIO.output(IO_FORWARD, GPIO.HIGH)\n        GPIO.output(IO_BACK, GPIO.HIGH)\n    if \"N\" in direction: # neutral motion\n        GPIO.output(IO_LEFT, GPIO.HIGH)\n        GPIO.output(IO_RIGHT, GPIO.HIGH)\n    if \"F\" in direction: # and \"B\" not in direction:\n        GPIO.output(IO_FORWARD, GPIO.LOW)\n        GPIO.output(IO_BACK, GPIO.HIGH)\n    if \"B\" in direction: # and \"F\" not in direction:\n        GPIO.output(IO_BACK, GPIO.LOW)\n        GPIO.output(IO_FORWARD, GPIO.HIGH)\n    if \"R\" in direction: # and \"L\" not in direction:\n        GPIO.output(IO_RIGHT, GPIO.LOW)\n        GPIO.output(IO_LEFT, GPIO.HIGH)\n    if \"L\" in direction: # and \"R\" not in direction:\n        GPIO.output(IO_LEFT, GPIO.LOW)\n        GPIO.output(IO_RIGHT, GPIO.HIGH)\n    sleep(duration)\n\ntry:\n    for (d, dur) in pattern:\n        dirWithDur(d, dur)\n\nfinally:\n    GPIO.cleanup()\n","sub_path":"testing_scripts/control_test/fractional_speed_test.py","file_name":"fractional_speed_test.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"367151436","text":"import matplotlib.pyplot as plt \n\ndecisionNode=dict(boxstyle=\"sawtooth\",fc=\"0.8\")#形状\nleafNode=dict(boxstyle=\"round4\",fc=\"0.8\")\narrow_args=dict(arrowstyle=\"<-\")\n\ndef plotNode(nodeTxt,conterPt,parentPt,nodeType):\n    createPlot.ax1.annotate(nodeTxt,xy=parentPt,xycoords='axes fraction',\\\n        xytext=conterPt,textcoords='axes fraction',va=\"center\",ha=\"center\",bbox=nodeType,arrowprops=arrow_args)\n\ndef createPlot():\n    fig=plt.figure(1,facecolor='white')\n    fig.clf()\n    createPlot.ax1=plt.subplot(111,frameon=False)\n    plotNode(U'决策节点',(0.5,0.1),(0.1,0.5),decisionNode)#名称，末端，起始端\n    plotNode(U'；叶节点',(0.8,0.1),(0.3,0.8),leafNode)\n    plt.show()","sub_path":"2-Tree/treePlotter.py","file_name":"treePlotter.py","file_ext":"py","file_size_in_byte":689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"472954689","text":"##========================================================================================\nimport numpy as np\nfrom scipy import linalg\nfrom sklearn.preprocessing import OneHotEncoder\n\nclass ExpectationReflection(object):\n\n    def __init__(self, niter_max, l2):\n        self.niter_max = niter_max\n        self.l2 = l2\n\n    \"\"\" ----------------------------------------------------------------------------------\n    fit h0 and w based on Expectation Reflection\n    input: features x[l,n], target: y[l] (y = {0,1})\n     output: h0, w[n]\n    \"\"\"\n    def fit(self,x,y):\n        niter_max = self.niter_max\n        l2 = self.l2\n        \n        n_unique_y = len(np.unique(y))\n        if n_unique_y == 1:    \n            print('The training data set is USELESS because it contains only 1 class')\n            \n        elif n_unique_y == 2:  # binary        \n            # convert 0,1 to -1, 1\n            y1 = 2*y - 1.\n           \n            #print(niter_max)    \n            n = x.shape[1]\n            \n            x_av = np.mean(x,axis=0)\n            dx = x - x_av\n            c = np.cov(dx,rowvar=False,bias=True)\n\n            # 2019.07.16:  \n            c += l2*np.identity(n) / (2*len(y))\n            c_inv = linalg.pinvh(c)\n\n            # initial values\n            h0 = 0.\n            w = np.random.normal(0.0,1./np.sqrt(n),size=(n))\n            \n            cost = np.full(niter_max,100.)\n            for iloop in range(niter_max):\n                h = h0 + x.dot(w)\n                y1_model = np.tanh(h/2.)    \n\n                # stopping criterion\n                p = 1/(1+np.exp(-h))                \n                cost[iloop] = ((p-y)**2).mean()\n\n                #cost[iloop] = ((y1[:]-y1_model[:])**2).mean()\n                # 2019.07.12: lost function                \n                #cost[iloop] = (-y[:]*np.log(p) - (1-y)*np.log(1-p)).mean()\n\n                if iloop>0 and cost[iloop] >= cost[iloop-1]: break\n\n                # update local field\n                t = h!=0    \n                h[t] *= y1[t]/y1_model[t]\n                h[~t] = 2*y1[~t]\n\n                # find w from h    \n                h_av = h.mean()\n                dh = h - h_av \n                dhdx = dh[:,np.newaxis]*dx[:,:]\n\n                dhdx_av = dhdx.mean(axis=0)\n                w = c_inv.dot(dhdx_av)\n                h0 = h_av - x_av.dot(w)\n            \n            self.h0 = h0\n            self.w = w\n            self.classtype = 'binary'\n\n        else:  # multiple classes\n            \"\"\" -----------------------------------------------------------------------\n            2019.06.14: fit h0 and w based on Expectation Reflection\n            input: features x[l,n], target: y[l,m] (y = +/-1)\n             output: h0[m], w[n,m]\n            \"\"\"\n            #def fit_multi(self,x,y,niter_max=500,l2=0.001):        \n            onehot_encoder = OneHotEncoder(sparse=False,categories='auto')\n            y_onehot = onehot_encoder.fit_transform(y.reshape(-1,1))  # y[t,:] = {0,1,...,0}\n\n            #print(niter_max)        \n            n = x.shape[1]\n            m = y_onehot.shape[1] # number of categories\n            \n            x_av = np.mean(x,axis=0)\n            dx = x - x_av\n            c = np.cov(dx,rowvar=False,bias=True)\n\n            # 2019.07.16:  l2 = lamda/(2L)\n            c += l2*np.identity(n) / (2*len(y))\n            c_inv = linalg.pinvh(c)\n\n            H0 = np.zeros(m)\n            W = np.zeros((n,m))\n\n            for i in range(m):\n                y = y_onehot[:,i]  # y = {0,1}\n                y1 = 2*y - 1       # y1 = {-1,1}\n                # initial values\n                h0 = 0.\n                w = np.random.normal(0.0,1./np.sqrt(n),size=(n))\n                \n                cost = np.full(niter_max,100.)\n                for iloop in range(niter_max):\n                    h = h0 + x.dot(w)\n                    y1_model = np.tanh(h/2.)    \n\n                    # stopping criterion\n                    p = 1/(1+np.exp(-h))\n\n                    cost[iloop] = ((p-y)**2).mean()\n\n                    # 2019.07.12: lost function\n                    #cost[iloop] = ((y1[:]-y1_model[:])**2).mean()\n                    #cost[iloop] = (-y[:]*np.log(p) - (1-y)*np.log(1-p)).mean()\n\n                    if iloop > 0 and cost[iloop] >= cost[iloop-1] : break\n\n                    # update local field\n                    t = h!=0    \n                    h[t] *= y1[t]/y1_model[t]\n                    h[~t] = 2*y1[~t]\n\n                    # find w from h    \n                    h_av = h.mean()\n                    dh = h - h_av \n                    dhdx = dh[:,np.newaxis]*dx[:,:]\n\n                    dhdx_av = dhdx.mean(axis=0)\n                    w = c_inv.dot(dhdx_av)\n                    h0 = h_av - x_av.dot(w)\n\n                H0[i] = h0\n                W[:,i] = w\n            \n            self.h0 = H0\n            self.w = W\n            self.classtype = 'multi'    \n            #return H0,W\n    #=====================================================================================                   \n    def predict(self,x):\n        classtype = self.classtype        \n        h0 = self.h0\n        w = self.w\n        \n        if classtype == 'binary':\n            \"\"\" --------------------------------------------------------------------------\n            calculate probability p based on x,h0, and w\n            input: x[l,n], w[n], h0\n            output: p[l]\n            \"\"\"\n            h = h0 + x.dot(w)\n            p = 1/(1+np.exp(-h))        \n            y = np.sign(p-0.5) # -1, 1\n            y = (y+1)/2        # 0, 1\n            \n        elif classtype == 'multi': \n            \"\"\" --------------------------------------------------------------------------\n            2019.06.12: calculate probability p based on x,h0, and w\n            input: x[l,n], w[n,my], h0\n            output: p[l]\n            \"\"\"\n            h = h0[np.newaxis,:] + x.dot(w)\n            p = 1/(1+np.exp(-h))\n            p /= p.sum(axis=1)[:,np.newaxis]\n            y = np.argmax(p,axis=1)\n        else:\n            print('Cannot define the classtype, not binary nor multi')\n                          \n        return y\n","sub_path":"261July19_pypackage_multiclass/expectation_reflection_2019.07.23.py","file_name":"expectation_reflection_2019.07.23.py","file_ext":"py","file_size_in_byte":6116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"476683735","text":"#!/usr/bin/env python3\n\"\"\" Batch normalization with tensorflow \"\"\"\nimport tensorflow as tf\n\n\ndef create_batch_norm_layer(prev, n, activation):\n    \"\"\" creates a batch normalization layer for a neural network in tensorflow\n        @prev: is the activated output of the previous layer\n        @n: is the number of nodes in the layer to be created\n        @activation: is the activation function that should be used on the\n                     output of the layer\n        - you should use the tf.layers.Dense layer as the base layer with\n          kernal initializer tf.contrib.layers.variance_scaling_initializer\n          (mode=\"FAN_AVG\")\n        - your layer should incorporate two trainable parameters, gamma and\n          beta, initialized as vectors of 1 and 0 respectively\n        - you should use an epsilon of 1e-8\n        Returns: a tensor of the activated output for the layer\n    \"\"\"\n    # Kernel initializer\n    w = tf.contrib.layers.variance_scaling_initializer(mode=\"FAN_AVG\")\n    # Create the layer\n    layer = tf.layers.Dense(units=n, kernel_initializer=w, name='layer')\n    # Add input to layer\n    x = layer(prev)\n    # Mean and variance\n    mean, variance = tf.nn.moments(x, axes=[0])\n    # Hyperparameters gamma and beta\n    gamma = tf.Variable(tf.constant(1.0, shape=[n]), trainable=True)\n    beta = tf.Variable(tf.constant(0.0, shape=[n]), trainable=True)\n    # Implement batch normalization\n    norm = tf.nn.batch_normalization(x, mean, variance, beta, gamma, 1e-8)\n    # Activate the output\n    act = activation(norm)\n    return act\n","sub_path":"supervised_learning/0x03-optimization/14-batch_norm.py","file_name":"14-batch_norm.py","file_ext":"py","file_size_in_byte":1555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"471709674","text":"from django.shortcuts import render\r\nfrom user import models\r\nfrom django.http import JsonResponse, HttpResponse\r\n\r\n\r\ndef t_show(request):\r\n    \"\"\"教师界面\"\"\"\r\n    return render(request, \"root/show.html\")\r\n\r\n\r\ndef equipmentlist(request):\r\n    \"\"\"设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all()\r\n    return render(request, \"root/equipmentlist.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef kindjsj(request):\r\n    \"\"\"计算机类设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eKind=\"计算机类\")\r\n    return render(request, \"root/kindjsj.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef kindsw(request):\r\n    \"\"\"生物类设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eKind=\"生物类\")\r\n    return render(request, \"root/kindsw.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef kindhx(request):\r\n    \"\"\"化学类设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eKind=\"化学类\")\r\n    return render(request, \"root/kindhx.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef kindwl(request):\r\n    \"\"\"物理类设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eKind=\"物理类\")\r\n    return render(request, \"root/kindwl.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef kindjx(request):\r\n    \"\"\"机械类设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eKind=\"机械类\")\r\n    return render(request, \"root/kindjx.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef kindqt(request):\r\n    \"\"\"其它类设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eKind=\"其它类\")\r\n    return render(request, \"root/kindqt.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef stateable(request):\r\n    \"\"\"可借状态设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eState=\"可借\")\r\n    return render(request, \"root/stateable.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef stateunable(request):\r\n    \"\"\"借出状态设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eState=\"借出\")\r\n    return render(request, \"root/stateunable.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef statefailure(request):\r\n    \"\"\"故障状态设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eState=\"故障\")\r\n    return render(request, \"root/statefailure.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef service(request):\r\n    \"\"\"维修状态设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eState=\"维修\")\r\n    return render(request, \"root/equipmentlist.html\", {\"equipments\": equipments})\r\n\r\n\r\ndef broken(request):\r\n    \"\"\"维修状态设备列表\"\"\"\r\n    equipments = models.Equipment.objects.all().filter(eState=\"报销\")\r\n    return render(request, \"root/equipmentlist.html\", {\"equipments\": equipments})\r\n\r\n\r\n\r\ndef edit(request):\r\n    \"\"\"编辑设备信息\"\"\"\r\n    eid = request.GET[\"eid\"]\r\n    equipment = models.Equipment.objects.get(eNum=int(eid))\r\n    return render(request, \"root/edit.html\", {\"equipment\": equipment})\r\n\r\n\r\ndef update(request):\r\n    \"\"\"更新设备信息\"\"\"\r\n    eid = request.GET[\"eid\"]\r\n    e = models.Equipment.objects.get(eNum=eid)\r\n    e.eName = request.POST[\"eName\"]\r\n    e.eKind = request.POST[\"eKind\"]\r\n    e.eRoom = request.POST[\"eRoom\"]\r\n    e.eCost = request.POST[\"eCost\"]\r\n    e.eManufacture = request.POST[\"eManufacture\"]\r\n    t = models.Teacher.objects.get(tName=e.eTeacher.tName)\r\n    t.tName = request.POST[\"tName\"]\r\n    t.tPhone = request.POST[\"tPhone\"]\r\n    try:\r\n        e.save()\r\n        t.save()\r\n    except Exception:\r\n        return JsonResponse({\"code\": 1})\r\n    else:\r\n        return JsonResponse({\"code\": 2})\r\n\r\n\r\ndef delete(request):\r\n    \"\"\"删除设备信息\"\"\"\r\n    eid = request.GET[\"eid\"]\r\n    equipment = models.Equipment.objects.filter(eNum=eid)\r\n    try:\r\n        equipment.delete()\r\n    except Exception:\r\n        return HttpResponse(\"删除失败\")\r\n    else:\r\n        return HttpResponse(\"删除成功\")\r\n\r\n\r\ndef addequipment(request):\r\n    \"\"\"添加设备\"\"\"\r\n    teachers = models.Teacher.objects.all()\r\n    return render(request, \"root/addequipment.html\", {\"teachers\": teachers})\r\n\r\n\r\ndef save(request):\r\n    \"\"\"保存添加的内容\"\"\"\r\n    eNum = request.POST[\"eNum\"]\r\n    eName = request.POST[\"eName\"]\r\n    eCost = request.POST[\"eCost\"]\r\n    eKind = request.POST[\"eKind\"]\r\n    eRoom = request.POST[\"eRoom\"]\r\n    eManufacture = request.POST[\"eManufacture\"]\r\n    etName = request.POST[\"tName\"]\r\n    teacher = models.Teacher.objects.get(tName=etName)\r\n    try:\r\n        models.Equipment.objects.create(eNum=eNum, eKind=eKind, eName=eName, eCost=eCost,\r\n                                        eManufacture=eManufacture, eRoom=eRoom, eTeacher=teacher)\r\n    except Exception:\r\n        return JsonResponse({\"code\": 1})\r\n    else:\r\n        return JsonResponse({\"code\": 2})\r\n\r\n\r\ndef userlist(request):\r\n    users = models.User.objects.all()\r\n    return render(request, \"root/userlist.html\", {\"users\": users})\r\n\r\n\r\ndef deleteuser(request):\r\n    \"\"\"删除用户信息\"\"\"\r\n    uid = request.GET[\"uid\"]\r\n    user = models.User.objects.filter(id=uid)\r\n    try:\r\n        user.delete()\r\n    except Exception:\r\n        return HttpResponse(\"删除失败\")\r\n    else:\r\n        return HttpResponse(\"删除成功\")\r\n\r\n\r\ndef main(request):\r\n    \"\"\"申请维修\"\"\"\r\n    eid = request.GET[\"eid\"]\r\n    equipment = models.Equipment.objects.get(eNum=int(eid))\r\n    return render(request, \"root/main.html\", {\"equipment\": equipment})\r\n\r\n\r\ndef mainupdate(request):\r\n    \"\"\"设置故障状态\"\"\"\r\n    eid = request.GET[\"eid\"]\r\n    e = models.Equipment.objects.get(eNum=eid)\r\n    e.eState = \"故障\"\r\n    BreakEquipmentNum = request.POST[\"BreakEquipmentNum\"]\r\n    BreakEquipmentName = request.POST[\"BreakEquipmentNum\"]\r\n    BreakTxt = request.POST[\"BreakTxt\"]\r\n    DealPerson = request.POST[\"Dealperson\"]\r\n    try:\r\n        e.save()\r\n        models.BreakEquipment.objects.create(BreakTxt=BreakTxt, eNum=e, BreakEquipmentNum=BreakEquipmentNum,\r\n                                             BreakEquipmentName=BreakEquipmentName, DealPerson=DealPerson)\r\n    except Exception:\r\n        return JsonResponse({\"code\": 1})\r\n    else:\r\n        return JsonResponse({\"code\": 2})\r\n\r\n\r\ndef search(request):\r\n    \"\"\"设备搜索\"\"\"\r\n    sear = request.GET[\"sear\"]\r\n    try:\r\n        int(sear)\r\n    except Exception:\r\n        equipments = models.Equipment.objects.all().filter(eName=sear)\r\n        content = {'equipments': equipments}\r\n        return render(request, \"root/search.html\", content)\r\n    else:\r\n        equipments = models.Equipment.objects.all().filter(eNum=int(sear))\r\n        content = {'equipments': equipments}\r\n        return render(request, \"root/search.html\", content)\r\n","sub_path":"root/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"633747990","text":"__author__ = \"Dohoon Lee\"\r\n__copyright__ = \"Copyright 2019, Dohoon Lee\"\r\n__email__ = \"dohlee.bioinfo@gmail.com\"\r\n__license__ = \"MIT\"\r\n\r\nfrom snakemake.shell import shell\r\n\r\n# Extract log.\r\nlog = snakemake.log_fmt_shell(stdout=False, stderr=True)\r\n\r\n# Extract parameters.\r\noutput = snakemake.output[0]\r\nif output.endswith('.gz'):\r\n    wget_output = output\r\n    gunzip_command = ':'\r\nelse:\r\n    wget_output = output + '.gz'\r\n    gunzip_command = 'gunzip %s' % wget_output\r\n\r\nurl = 'https://dohlee-bioinfo.sgp1.digitaloceanspaces.com/test-data/rrbs/se/test.fastq.gz'\r\n\r\n# Execute shell command.\r\nshell(\r\n    \"(\"\r\n    \"wget {url} -qO {wget_output} && \"\r\n    \"{gunzip_command}\"\r\n    \")\"\r\n)\r\n","sub_path":"test/rrbs/se/wrapper.py","file_name":"wrapper.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"266381397","text":"from PIL import ImageGrab\r\nfrom io import BytesIO\r\nimport time\r\n\r\n\r\ndef take_screenshot():\r\n    img = ImageGrab.grab()\r\n    buffer = BytesIO()\r\n    img.save(buffer, \"PNG\", quality=10)\r\n\r\ndef func_speed(func_name):\r\n    start = time.time()\r\n    eval(func_name+\"()\")\r\n    return time.time() - start\r\n\r\ndef benchmark(timeout=1, display=True):\r\n    count = 0\r\n    end_loop = time.time() + (timeout)\r\n    while time.time() < end_loop:\r\n        take_screenshot()\r\n        count += 1\r\n\r\n    if display == True:\r\n        print(\"Result:\",count,\"frames in\",timeout,\"seconds!\")\r\n        print(\"Average:\",count/timeout,\"frames per second!\")\r\n\r\n    return [timeout, count, count/timeout]\r\n\r\nprint(benchmark())\r\n\r\n# FPS drops from 30 to 9 (due to the compression)\r\n# image compression will need to be done on another thread\r\n","sub_path":"Python/!PYTHON/py/screen recorder/backup/tests/compress_image_in_memory.py","file_name":"compress_image_in_memory.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"479228499","text":"from silvereye_wps_demo.models.ecomeasure import EcoMeasure\n\n\nclass Rainfall(EcoMeasure):\n    def __init__(self):\n        EcoMeasure.__init__(\n            self,\n            'http://dapds00.nci.org.au/thredds/dodsC/rr9/eMAST_data/ANUClimate/ANUClimate_v1-0_rainfall_daily_0-01deg_1970-2014',\n            'lwe_thickness_of_precipitation_amount',\n            'Rainfall'\n        )\n","sub_path":"src/silvereye_wps_demo/models/rainfall.py","file_name":"rainfall.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"145599448","text":"import pandas as pd\nfrom PyQt5.QtCore import QAbstractTableModel, QModelIndex, Qt, QVariant\nfrom PyQt5.QtGui import QFont, QBrush, QColor\nfrom PyQt5.QtWidgets import QPushButton\nfrom package.classes.protocole import TitrationProtocole\n\nclass DataFrameModel(QAbstractTableModel):\n\n    def __init__(self, parent=None, editable = False):\n        super().__init__(parent)\n\n        self._df = pd.DataFrame()\n        self.editable = editable\n        \n        \n    @property\n    def df(self):\n        return self._df\n\n    def load_csv(self, filePath, header):\n        with open(filePath, \"r\") as fh:\n            self._df = pd.read_table(fh, sep='\\\\s+', header=header)\n            self.modelReset.emit()\n\n    def rowCount(self, parent=QModelIndex()):\n        return len(self.df.index)\n\n    def columnCount(self, parent=QModelIndex()):\n        return len(self.df.columns.values)\n\n    def headerData(self, section, orientation, role=Qt.DisplayRole):\n        if role != Qt.DisplayRole:\n            return QVariant()\n\n        if orientation == Qt.Horizontal:\n            try:\n                return str(self.df.columns.tolist()[section])\n            except (IndexError, ):\n                return QVariant()\n        elif orientation == Qt.Vertical:\n            try:\n                # return self.df.index.tolist()\n                return str(self.df.index.tolist()[section])\n            except (IndexError, ):\n                return QVariant()\n\n    \n\n    def data(self, index, role=Qt.DisplayRole):\n\n        row = self.df.index[index.row()]\n        col = self.df.columns[index.column()]\n\n        if not index.isValid():\n            return None\n        if role == Qt.DisplayRole:\n            return QVariant(str(self.df.ix[row, col]))\n        return None\n\n    def sort(self, column, order):\n        colname = self.df.columns.tolist()[column]\n        self.layoutAboutToBeChanged.emit()\n        self.df.sort_values(colname, ascending= order == Qt.AscendingOrder, inplace=True)\n        self.df.reset_index(inplace=True, drop=True)\n        self.layoutChanged.emit()\n\n    def flags(self,index):\n        #return super().flags(index)\n        flags = super().flags(index)\n        if self.editable : flags |= Qt.ItemIsEditable\n        return flags\n\n    def setData(self, index, value, role=Qt.DisplayRole):\n        \"\"\"Set the value to the index position depending on Qt::ItemDataRole and data type of the column\n        Args:\n            index (QModelIndex): Index to define column and row.\n            value (object): new value.\n            role (Qt::ItemDataRole): Use this role to specify what you want to do.\n        Raises:\n            TypeError: If the value could not be converted to a known datatype.\n        Returns:\n            True if value is changed. Calls layoutChanged after update.\n            False if value is not different from original value.\n        \"\"\"\n        if not index.isValid():\n            return False\n\n        if value != index.data(role):\n\n            self.layoutAboutToBeChanged.emit()\n\n            row = self.df.index[index.row()]\n            col = self.df.columns[index.column()]\n            #print 'before change: ', index.data().toUTC(), self.df.iloc[row][col]\n            self.df.set_value(row, col, value)\n            #print 'after change: ', value, self._dataFrame.iloc[row][col]\n            self.layoutChanged.emit()\n            return True\n        else:\n            return False\n\n\nclass ProtocoleModel(DataFrameModel):\n\n    def __init__(self, protocole, parent = None, editable = True):\n        super().__init__(parent, editable)\n        self.protocole = protocole\n        self.headers = self.df.columns.tolist()\n\n    @property\n    def df(self):\n        return self.protocole.df\n\n    def flags(self,index):\n        #return super().flags(index)\n        flags = Qt.ItemIsSelectable\n        # if index.row() != 0:\n        flags |= Qt.ItemIsEnabled\n        if self.editable and index.column() == 0 and index.row() != 0:\n            flags |= Qt.ItemIsEditable\n        return flags\n\n\n    def data(self, index, role=Qt.DisplayRole):\n\n        if not index.isValid():\n            return None\n\n\n        row = self.df.index[index.row()]\n        col = self.df.columns[index.column()]\n\n        \n        if role == Qt.DisplayRole:\n            return QVariant(float(self.df.ix[row, col]))\n        elif role == Qt.EditRole:\n            return QVariant(float(self.df.ix[row,col]))\n        elif role == Qt.FontRole:\n            if index.column() == 0 and index.row() != 0:\n                font = QFont()\n                font.setBold(True)\n                return QVariant(font)\n        elif role == Qt.TextAlignmentRole:\n            if index.column() == 0 and index.row() != 0:\n                return Qt.AlignCenter\n        elif role == Qt.BackgroundRole:\n            if index.column() == 0 and index.row() != 0:\n                return QBrush(QColor(100, 200, 255, 100))\n        elif role == Qt.ForegroundRole:\n            if index.column() == 0 and index.row() != 0:\n                return QBrush(QColor(100, 200, 50))\n        return None\n\n    def headerData(self, section, orientation, role=Qt.DisplayRole):\n\n        if role == Qt.StatusTipRole:\n            return QVariant(\"KANAR\")\n        if role != Qt.DisplayRole:\n            return QVariant()\n\n        if orientation == Qt.Horizontal:\n            try:\n                return str(self.headers[section])\n            except (IndexError, ):\n                return QVariant()\n        elif orientation == Qt.Vertical:\n            try:\n                # return self.df.index.tolist()\n                return \"Step {}\".format(self.df.index.tolist()[section])\n            except (IndexError, ):\n                return QVariant()\n\n    def setHeaderData(self, section, orientation, value, role= Qt.EditRole):\n        if orientation == Qt.Horizontal:\n            self.headers[section] = value\n            self.headerDataChanged.emit(Qt.Horizontal, section, section )\n            return True\n        return False\n\n    def setData(self, index, value, role=Qt.DisplayRole):\n        \"\"\"Set the value to the index position depending on Qt::ItemDataRole and data type of the column\n        Args:\n            index (QModelIndex): Index to define column and row.\n            value (object): new value.\n            role (Qt::ItemDataRole): Use this role to specify what you want to do.\n        Raises:\n            TypeError: If the value could not be converted to a known datatype.\n        Returns:\n            True if value is changed. Calls layoutChanged after update.\n            False if value is not different from original value.\n        \"\"\"\n        if not index.isValid():\n            return False\n\n        if value != index.data(role):\n\n            self.modelAboutToBeReset.emit()\n\n            row = self.df.index[index.row()]\n            col = self.df.columns[index.column()]\n            #print 'before change: ', index.data().toUTC(), self.df.iloc[row][col]\n            self.protocole.update_volumes({row: value})\n            self.protocole.update()\n            #print 'after change: ', value, self._dataFrame.iloc[row][col]\n            self.modelReset.emit()\n            return True\n        \n        return False\n\n    \n    def insertRows(self, row, count, parent=QModelIndex()):\n        self.beginInsertRows(parent, self.rowCount(), self.rowCount())\n        self.protocole.add_volume(self.protocole.volumes[-1] or 1)\n        self.endInsertRows()\n        return True","sub_path":"package/models/DataFrameModel.py","file_name":"DataFrameModel.py","file_ext":"py","file_size_in_byte":7410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"275575322","text":"from pyqt4_framework_two_views import *\n\nclass PyQt4DrawSelective(Pyqt4Draw):\n\n    def ManualDraw(self):\n        \"\"\"\n        This implements code normally present in the C++ version,\n        which calls the callbacks that you see in this class (DrawSegment,\n        DrawSolidCircle, etc.).\n        \n        This is implemented in Python as an example of how to do it, and also\n        a test.\n        \"\"\"\n        \"\"\"\n        we re-implement this method so that we can, for example, selectively display\n        the manipulandum.\n        \"\"\"\n        colors = {\n            'active'    : b2Color(0.5, 0.5, 0.3),\n            'static'    : b2Color(0.5, 0.9, 0.5), \n            'kinematic' : b2Color(0.5, 0.5, 0.9), \n            'asleep'    : b2Color(0.6, 0.6, 0.6), \n            'default'   : b2Color(0.9, 0.7, 0.7), \n        }\n\n        settings=self.test.settings\n        world=self.test.world\n        if self.test.selected_shapebody:\n            sel_shape, sel_body=self.test.selected_shapebody\n        else:\n            sel_shape=None\n\n        if settings.drawShapes:\n            for body in world.bodies:\n                if (settings.hideManipulandum and \n                    body == self.test.dynamics.manipuland_body):\n                    continue #don't plot \n\n                transform=body.transform\n                for fixture in body.fixtures:\n                    shape=fixture.shape\n\n                    if not body.active: color=colors['active']\n                    elif body.type==b2_staticBody: color=colors['static']\n                    elif body.type==b2_kinematicBody: color=colors['kinematic']\n                    elif not body.awake: color=colors['asleep']\n                    else: color=colors['default']\n                    \n                    self.DrawShape(fixture.shape, transform, color, (sel_shape==shape))\n\n\n        if settings.drawJoints:\n            for joint in world.joints:\n                self.DrawJoint(joint)\n\n        # if settings.drawPairs\n        #   pass\n\n        if settings.drawAABBs:\n            color=b2Color(0.9, 0.3, 0.9)\n            cm=world.contactManager\n            for body in world.bodies:\n                if not body.active:\n                    continue\n                transform=body.transform\n                for fixture in body.fixtures:\n                    shape=fixture.shape\n                    for childIndex in range(shape.childCount):\n                        self.DrawAABB(shape.getAABB(transform, childIndex), color)\n","sub_path":"pyqt4_draw_selective_two_views.py","file_name":"pyqt4_draw_selective_two_views.py","file_ext":"py","file_size_in_byte":2477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"145537677","text":"from connected_components.connected_components import run_write_proposals\nimport click\n\n\n@click.command()\n@click.argument(\"img_dir\")\n@click.argument(\"out_dir\")\n@click.argument(\"procs\")\ndef cli(img_dir, out_dir, procs):\n    run_write_proposals(img_dir=img_dir, output_dir=out_dir, procs=int(procs))\n\n\nif __name__ == \"__main__\":\n    cli()\n","sub_path":"cosmos/propose.py","file_name":"propose.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"53127990","text":"#!/usr/bin/python3.2\n# -*- coding: cp1252 -*-\n#\n# Author:  Mario S. Könz <mskoenz@gmx.net>\n# Date:    17.01.2012 10:53:33 CET\n# File:    rubber_band_select.py\n\nimport sys\nfrom qt_import import *\nfrom modules import *\n#=======================================================================================================================\ndef map_to_grid(point, grid_size):\n    x = int(point.x()/grid_size)*grid_size;\n    y = int(point.y()/grid_size)*grid_size;\n    return QPoint(x, y);\n    \n#=======================================================================================================================\nclass Example(QWidget):\n    def __init__(self):\n        super(Example, self).__init__();\n        self.setWindowTitle(\"Click or Move\");\n        self.setGeometry(300, 300, 300, 300);\n        self.setAcceptDrops(True);\n        \n        self.objects = [moveablePushButton('1', self)];\n        self.objects.append(moveablePushButton('2', self));\n        self.objects.append(moveablePushButton('3', self));\n        self.objects[0].setGeometry(10,20,20,20);\n        self.objects[1].setGeometry(100,20,60,60);\n        self.objects[2].setGeometry(150,20,20,20);\n        \n        \n        self.reso_list = resource_line(-100, 2000);\n        for item in self.objects:\n            self.reso_list.add_item(item, item.pos().x(), item.size().height());\n        \n        self.rubber = SRubberBand(self);\n        self.selection = selection_manager();\n        self.shiftPressed = False;\n        \n    def keyPressEvent(self, e):\n        if e.isAutoRepeat() == False and e.key() == Qt.Key_Shift:\n            self.shiftPressed = True;\n            self.setWindowTitle(\"shift pressed\");\n    def keyReleaseEvent(self, e):\n        if e.isAutoRepeat() == False and e.key() == Qt.Key_Shift:\n            self.shiftPressed = False;\n            self.setWindowTitle(\"shift not pressed\");\n    \n    def move_selected_to(self, item, pos):\n        self.selection.happend = True;\n        for it in self.selection:\n            loc = map_to_grid(it.pos()+pos, 10);\n            it.move(loc);\n            self.reso_list.move_item_to(it, loc.x());\n        self.repaint();\n            \n    def mousePressEvent(self, e):\n        self.rubber.start(e);\n        \n    def mouseMoveEvent(self, e):\n        self.rubber.change(e);\n        \n    def mouseReleaseEvent(self, e):\n        sel = self.rubber.get_selection(self.objects, e);\n        if self.shiftPressed and not self.rubber.empty():\n            self.selection.select_list_add(sel);\n        if not self.shiftPressed:\n            self.selection.select_list(sel);\n        \n        self.rubber.set_to_zero();\n        \n    def paintEvent(self, event):\n        self.reso_list.paint(self, \"Green\", 4400);\n#=======================================================================================================================\ndef main():\n    app = QApplication(sys.argv);\n    app.setStyle(\"plastique\")\n    ex = Example();\n    ex.show();\n    app.exec_();\n\nif __name__ == '__main__':\n    main();\n","sub_path":"04 frist_modules/reso_line_and_select.py","file_name":"reso_line_and_select.py","file_ext":"py","file_size_in_byte":3009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"325213238","text":"from django.conf.urls import patterns, url\nfrom topka.yulankor.views import MainView, StatsView\n\n\nurlpatterns = patterns('topka.yulankor.views',\n    url(r'^$', MainView.as_view(), name='yulankor'),\n    url(r'^add/$', 'add_tasks', name='add_tasks'),\n    url(r'^get/$', 'get_tasks', name='get_tasks'),\n    url(r'^stats/$', StatsView.as_view(), name='stats'),\n    url(r'^stats/add/$', 'add_stats', name='add_stats'),\n    #url(r'^stats/get/$', 'get_tasks', name='get_stats'),\n)","sub_path":"topka/yulankor/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"377070601","text":"import sys\n\n\nprint(\"Case #1:\")\n\n\ndef factor(n):\n    for i in range(2, min(int(n ** 0.5) + 1, 1000)):\n        if n % i == 0: return i\n    return None\n\n\ndef solve(n, j):\n    n = int(n)\n    j = int(j)\n    results = []\n    for i in range(1 << (n - 2)):\n        num = \"1\" + bin(i)[2:].zfill(n - 2) + \"1\"\n        factors = []\n        for base in range(2, 11):\n            factors.append(factor(int(num, base)))\n            if factors[-1] is None: break\n\n        if None not in factors:\n            results.append((num, factors))\n            print(\" \".join(map(str, [num] + factors)))\n            if len(results) == j: return\n\n\nif __name__ == '__main__':\n    solve(32, 500)\n","sub_path":"codes/CodeJamCrawler/16_0_3/MattStark/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"443049108","text":"from itertools import permutations\nfrom math import sqrt, factorial, pow\nimport random\n\n\ndef get_data_from_file(file_name: str) -> dict:\n    f = open(file_name)\n    n = f.readline()\n    result = {}\n    for line in f.readlines():\n        tmp = line.split()\n        result[tmp[0]] = (int(tmp[1]), int(tmp[2]))\n    return result\n\n\ndef convert_to_dict_matrix(my_dict: dict) -> dict:\n    result = {}\n    for k1, v1 in my_dict.items():\n        result[k1] = {}\n        for k2, v2 in my_dict.items():\n            result[k1][k2] = sqrt(pow(v1[0] - v2[0], 2) + pow(v1[1] - v2[1], 2))\n        del result[k1][k1]\n    return result\n\n\ndef get_length_of_way(way, values: dict) -> float:\n    result = 0\n    for i in range(len(way)-1):\n        result += values[way[i]][way[i+1]]\n    return result\n\n\ndef get_random_way(table: dict) -> list:\n    n = len(table.keys())\n    tmp_keys = list(table.keys())\n    tmp_way = []\n    for i in range(n):\n        tmp_way.append(tmp_keys.pop(random.randrange(n-i)))\n    tmp_way.append(tmp_way[0])\n    return tmp_way\n\n\ndef distance(a, b):\n    \"\"\"Calculates the Levenshtein distance between a and b.\"\"\"\n    n, m = len(a), len(b)\n    if n > m:\n        # Make sure n <= m, to use O(min(n, m)) space\n        a, b = b, a\n        n, m = m, n\n\n    current_row = range(n + 1)  # Keep current and previous row, not entire matrix\n    for i in range(1, m + 1):\n        previous_row, current_row = current_row, [i] + [0] * n\n        for j in range(1, n + 1):\n            add, delete, change = previous_row[j] + 1, current_row[j - 1] + 1, previous_row[j - 1]\n            if a[j - 1] != b[i - 1]:\n                change += 1\n            current_row[j] = min(add, delete, change)\n\n    return current_row[n]\n\n","sub_path":"IteratedLocalSearch/gready/support.py","file_name":"support.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"398208601","text":"# -*- coding: utf-8 -*-\n# @Time     : 2020/3/23 21:22\n# @Author   : Zhou_Chao\n# @Email    : 1031529989@qq.com\n# @File     : test_invest.py\n# @Software : 投资\n\"\"\"\n约束条件：已登陆 有标 有钱 标有余\n\"\"\"\n\nimport unittest\n\nfrom selenium import webdriver\n\nfrom class11_pytest_fixture.datas import login_data,bid_data\nfrom class11_pytest_fixture.pages.bid_page import BidPage\nfrom class11_pytest_fixture.pages.index_page import IndexPage\nfrom class11_pytest_fixture.pages.login_page import LoginPage\n\n\n\n\nclass TestInvest(unittest.TestCase):\n\n    def setUp(self):\n        self.driver = webdriver.Chrome()\n        self.driver.get('http://120.78.128.25:8765/Index/login.html')\n        self.driver.maximize_window()\n        self.login_page = LoginPage(self.driver)\n        self.login_page.submit_userinfo(\n            login_data.user_correct['phone'],login_data.user_correct['password']\n        )\n        self.bid_page = BidPage(self.driver)\n\n    def tearDown(self):\n        self.driver.quit()\n\n\n    # def test_bid_send10_popup(self):\n    #     # 访问登录页面\n    #     self.login_page.submit_userinfo(data['phone'], data['password'])\n    #     # 断言\n    #     self.assertTrue(data['expected'] == self.login_page.alert_info().text)\n\n    # def test_bid_send1_wrong(self):\n\n    def test_bid_0_success(self):\n        # 首页点击投标\n        IndexPage(self.driver).bid()\n        # 输入投标金额\n        self.bid_page.send_bid(bid_data.bid_correct['money'])\n        # 点击投标\n        self.bid_page.submit_bid()\n\n        # 断言1.弹出框有投标成功！\n        self.assertTrue(self.bid_page.bid_success_popup.text == bid_data.bid_correct['expected'])\n        # 断言2.比对余额\n        # 断言3.投资记录\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n\n\n","sub_path":"class11_pytest_fixture/test_invest.py","file_name":"test_invest.py","file_ext":"py","file_size_in_byte":1794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"557816614","text":"#=-*-coding:utf-8 -*-\n'''\n利用tracert程序获取域名或者IP的路由跟踪信息-管理端\n'''\n\nimport json\nfrom .utils import *\nimport time\nimport datetime\nimport subprocess\nimport chardet\nimport shlex\n\nclass RouteTracerManager:\n    '''\n    路由跟踪实现类，目前仅支持Windows\n    任务执行管理端，主要负责结果的解析\n    '''\n    def __init__(self):\n        pass\n\n    def _result_parse_json_Windows(self, resultText):\n        '''\n        解析Windows版本Tracert结果\n        :param resultText: Windows版本的Tracert结果，为Json字符串，如下：\n         ----------------------\n            {\n                \"target\":\"baidu.com\",                #需要路由跟踪的IP或者域名目标\n                \"raw\":\"[...]\",                       #路由跟踪的原始结果\n                \"state\":\"\",                          #需要传递的额外内容，用户指定\n                \"max_hops\":30,                       #路由跟踪最大跳数（TTL）值\n                \"timeout\":1,                         #路由跟踪超时时间，单位为秒\n                \"time\":\"2017-01-12 02:15:37.166689\", #路由跟踪完成时间（UTC）\n                \"os\":\"Linux\",                        #执行路由跟踪命令的操作系统类型(Windows/Linux)\n                \"uid\":-1,                            #被路由跟踪的IP或者域名的唯一标记，用户指定\n                \"task_id\":-1                         #任务ID\n            }\n       ----------------------\n        :return: 解析后的结果的Json字符串，如下：\n        ---------------------\n        {\n            \"domain\": \"\",                            #路由跟踪的目标的域名\n            \"task_id\":-1                             #任务ID\n            \"uid\": -1,                               #被路由跟踪的IP或者域名的唯一标记，用户指定\n            \"trace\": [                               #路由跟踪结果集合\n                {\n                    \"index\": 1,                      #路由跟踪跳数\n                    \"ip\": \"192.168.1.254\",           #当前跳的IP\n                    \"time1\": 140,                    #第1个数据包返回时间ms\n                    \"time2\": 123,                    #第2个数据包返回时间ms\n                    \"time3\": 115,                    #第3个数据包返回时间ms\n                    \"host\": \"\",                      #当前跳的IP对应的域名\n                    \"timeout\": false,                #是否超时，超时：true，未超时：false\n                    \"average_time\":126,              #平均时延ms\n                },\n                {\n                    ...\n                }\n            ],\n            \"ttl\": 30,                               #路由跟踪实际跳数（TTL）值\n            \"ip\": \"154.63.25.8\",                     #路由跟踪的目标的IP\n            \"error\": 0,                              #路由跟踪结果状态码,0-成功  非零-失败（1-目标域名无法解析 2-解析结果失败 1231-传输错误）\n            \"state\": \"\",                             #需要传递的额外内容，用户指定\n            \"timeout\": 1,                            #路由跟踪超时时间，单位为秒\n            \"time\": \"2017-01-12 06:46:04.519000\",    #路由跟踪完成时间（UTC）\n            \"max_ttl\": 30,                           #路由跟踪最大跳数（TTL）值\n            \"os\": \"Windows\",                         #执行路由跟踪命令的操作系统类型(Windows/Linux)\n            \"error_msg\": \"OK\",                       #路由跟踪结果状态码附加说明信息\n            \"target\": \"154.63.25.8\",                 #需要路由跟踪的IP或者域名目标\n            \"is_reached\":true                        #是否到达目标IP,是-true,否-false\n        }\n        ---------------------\n        '''\n        resultJson = json.loads(resultText, encoding='utf-8')\n        taskId = resultJson['task_id']\n        target = resultJson['target'].encode('utf-8')\n        raw = resultJson['raw'].encode('utf-8')\n        state = resultJson['state'].encode('utf-8')\n        max_hops = resultJson['max_hops']\n        timeout = resultJson['timeout']\n        record_time = resultJson['time'].encode('utf-8')\n        os_type = resultJson['os'].encode('utf-8')\n        uid = resultJson['uid']\n        lines = raw.split('\\n')\n        root = {}\n        root['error'] = 0\n        root['error_msg'] = 'OK'\n        root['os'] = os_type\n        root['time'] = record_time\n        root['task_id'] = taskId\n        root['uid'] = uid\n        root['state'] = state\n        root['target'] = target\n        root['timeout'] = timeout\n\n        result = []\n        maxTTL = max_hops\n        TTL = 0\n        targetIP = ''\n        targetDomain = ''\n        isReachDestination=False\n        root['is_reached'] =isReachDestination\n        try:\n            for line in lines:\n                line = line.lower()\n                if line == '' or line.strip() == '':\n                    continue\n                line = line.strip()\n                '''\n                ############################################################\n                Unable to resolve target system name fdsfdsfd.cnn.ccc.\n                ############################################################\n                无法解析目标系统名称 fsfdsfds.ccn.ccn。\n                ############################################################\n                '''\n                if \"无法解析目标系统名称\".lower() in line or \"Unable to resolve target system name\".lower() in line:\n                    root['error'] = 1\n                    root['error_msg'] = 'Unable to resolve target system name %s' % (target)\n                    return json.dumps(root, indent=4)\n                if 'Transmit error'.lower() in line:\n                    root['error'] = 1231\n                    root['error_msg'] = 'Transmit error: code 1231'\n                    return json.dumps(root, indent=4)\n                if 'ms' not in line and '*' not in line:\n                    '''\n                    ############################################################\n                    Tracing route to 123.125.114.144 over a maximum of 30 hops\n\n                    1     7 ms    17 ms    14 ms  192.168.79.1\n                    2     *        *        *     Request timed out.\n                    3    78 ms    64 ms    37 ms  120.80.62.53\n                    4    91 ms    48 ms    66 ms  120.80.62.102\n                    ############################################################\n                    通过最多 30 个跃点跟踪到 23.91.97.24 的路由\n\n                    1     *       93 ms   102 ms  bogon [192.168.1.1]\n                    2    96 ms   101 ms   112 ms  2-171-100-101.myrepublic.com.sg [101.100.171.2]\n                    3   100 ms    88 ms    89 ms  103-6-148-49.myrepublic.com.sg [103.6.148.49]\n                    ############################################################\n                    Tracing route to sina.com [66.102.251.33]\n                    over a maximum of 30 hops:\n\n                      1     5 ms    16 ms    11 ms  192.168.79.1\n                      2     *        *        *     Request timed out.\n                      3    56 ms    44 ms    81 ms  120.80.62.49\n                      4    51 ms    29 ms    47 ms  120.80.62.94\n                      5    68 ms    44 ms    40 ms  120.80.62.89\n                      6    47 ms     *        *     120.80.62.105\n                    ############################################################\n                    通过最多 30 个跃点跟踪\n                    到 baidu.com [111.13.101.208] 的路由:\n\n                      1   107 ms    92 ms   121 ms  bogon [192.168.1.1]\n                      2    98 ms   109 ms   110 ms  2-171-100-101.myrepublic.com.sg [101.100.171.2]\n                      3   115 ms   101 ms   107 ms  103-6-148-49.myrepublic.com.sg [103.6.148.49]\n                      4   123 ms   120 ms   121 ms  snge-b1-link.telia.net [213.248.72.21]\n                      5   342 ms   278 ms   317 ms  las-b21-link.telia.net [62.115.134.43]\n                      6   293 ms   290 ms   286 ms  chinamobile-ic-319993-las-b21.c.telia.net [213.248.92.171]\n                    ############################################################\n                    '''\n\n                    if line.startswith('Tracing route to'.lower()) and line.endswith('hops'.lower()):\n                        line = line.replace('Tracing route to'.lower(), '').replace('over a maximum of'.lower(), '') \\\n                            .replace('hops'.lower(), '').strip()\n                        datas = split_and_remove_empty(line, sep=' ')\n                        targetIP = datas[0]\n                        maxTTL = int(datas[1])\n                    elif line.startswith('通过最多'.lower()) and line.endswith('的路由'.lower()):\n                        line = line.replace('通过最多'.lower(), '').replace('个跃点跟踪到'.lower(), '').replace('的路由'.lower(),\n                                                                                                      '').strip()\n                        datas = split_and_remove_empty(line, sep=' ')\n                        targetIP = datas[1]\n                        maxTTL = int(datas[0])\n                    elif line.startswith('Tracing route to'.lower()) and line.endswith(']'.lower()):\n                        line = line.replace('Tracing route to'.lower(), '').replace('['.lower(), '') \\\n                            .replace(']'.lower(), '').strip()\n                        datas = split_and_remove_empty(line, sep=' ')\n                        targetDomain = datas[0]\n                        targetIP = datas[1]\n                    elif line.startswith('通过最多'.lower()) and line.endswith('个跃点跟踪'.lower()):\n                        line = line.replace('通过最多'.lower(), '').replace('个跃点跟踪'.lower(), '').strip()\n                        datas = split_and_remove_empty(line, sep=' ')\n                        maxTTL = int(datas[0])\n                    elif line.startswith('over a maximum of'.lower()) and line.endswith('hops:'.lower()):\n                        line = line.replace('over a maximum of'.lower(), '').replace('hops:'.lower(), '').strip()\n                        datas = split_and_remove_empty(line, sep=' ')\n                        maxTTL = int(datas[0])\n                    elif line.startswith('到'.lower()) and line.endswith('的路由:'.lower()):\n                        line = line.replace('到'.lower(), '').replace('['.lower(), '') \\\n                            .replace(']'.lower(), '').replace('的路由:'.lower(), '').strip()\n                        datas = split_and_remove_empty(line, sep=' ')\n                        targetDomain = datas[0]\n                        targetIP = datas[1]\n                    continue\n\n                temp = line.split(' ')\n                datas = []\n                for t in temp:\n                    if t == '' or t.strip() == '' or t.strip() == 'ms':\n                        continue\n                    t = t.strip()\n                    if t == \"*\":\n                        t = '0'\n                    datas.append(t)\n                totalLen = len(datas)\n                index = int(datas[0])\n                time1 = int(datas[1])\n                time2 = int(datas[2])\n                time3 = int(datas[3])\n                if totalLen == 6:\n                    host = datas[4]\n                    ip = datas[5]\n                else:\n                    if \"请求超时\".lower() in line or \"timed out\".lower() in line:\n                        host = ''\n                        ip = ''\n                    else:\n                        host = ''\n                        ip = datas[4]\n                ip=ip.replace('[', '').replace(']', '')\n                #到达目的跳，不记录\n                if ip==targetIP:\n                    #到达最后一跳\n                    isReachDestination=True\n                    continue\n                ret = {}\n                ret['index'] = index\n                ret['time1'] = time1\n                ret['time2'] = time2\n                ret['time3'] = time3\n                ret['average_time']=(time1+time2+time3)/3\n                ret['host'] = host\n                ret['ip'] = ip\n                # 是否超时\n                ret['timeout'] = (time1 == time2 == time3 == 0)\n                result.append(ret)\n                TTL = index  # 记录实际的TTL\n        except Exception as e:\n            root['error'] = 2\n            root['error_msg'] = 'Parse tracert result error:{}'.format(e)\n            return json.dumps(root, indent=4)\n        root['trace'] = result\n        root['ip'] = targetIP\n        root['domain'] = targetDomain\n        root['max_ttl'] = maxTTL\n        root['ttl'] = TTL\n        root['is_reached'] =isReachDestination\n        return json.dumps(root, indent=4)\n\n    def _result_parse_json_Linux(self, resultText):\n        # TODO: Unix和Linux系统由于存在解析结果错误，暂未实现功能\n        pass\n\n    def result_parse_json(self, resultText):\n        '''\n        将Tracert原始结果解析成json格式\n        :param resultText: 需要解析的结果\n        :return: 解析后的json格式结果\n        '''\n        return self._result_parse_json_Windows(resultText)\n        '''\n        if isWindwosOS():\n            return self._result_parse_json_Windows(resultText)\n        else:\n            raise 'Not Supported Unix or Linux.'\n            return self._result_parse_json_Linux(resultText)\n        '''\n\t\t\t\n    def trace(self, domain, taskId=-1, uid=-1, state='', maxHops=30, timeout=1):\n        '''#######################################################################\n        路由跟踪操作，兼容Windows \"tracert\"命令和Unix/Linux 下的\"traceroute\"命令。\n        :param domain:需要进行路由跟踪的IP或者域名\n        :param taskId:任务ID\n        :param uid:[可选]被路由跟踪的IP或者域名的唯一标记，用户指定\n        :param state:[可选]需要传递的额外内容，用户指定\n        :param maxHops:[可选]路由跟踪最大跳数\n        :param timeout:[可选]路由跟踪超时时间，单位为秒\n        :return:如下格式的Json字符串：\n        --------\n             {\n                \"target\":\"baidu.com\",                #需要路由跟踪的IP或者域名目标\n                \"raw\":\"[...]\",                       #路由跟踪的原始结果\n                \"state\":\"\",                          #需要传递的额外内容，用户指定\n                \"max_hops\":30,                       #路由跟踪最大跳数\n                \"timeout\":1,                         #路由跟踪超时时间，单位为秒\n                \"time\":\"2017-01-12 02:15:37.166689\", #路由跟踪完成时间（UTC）\n                \"os\":\"Linux\",                        #执行路由跟踪命令的操作系统类型(Windows/Linux)\n                \"uid\":-1,                            #被路由跟踪的IP或者域名的唯一标记，用户指定\n                \"task_id\":-1                         #任务ID\n             }\n        --------\n        ##########################################################################'''\n\n        domain = domain.strip()\n        result = {}\n        result['target'] = domain\n        result['task_id'] = taskId\n        result['uid'] = uid\n        result['state'] = state\n        result['max_hops'] = maxHops\n        result['timeout'] = timeout\n        if isWindwosOS():\n            cmd = 'tracert -h %d -w %d %s ' % (maxHops, timeout * 1000, domain)\n            result['os'] = 'Windows'\n        else:\n            cmd = 'traceroute -m %d -w %d %s ' % (maxHops, timeout, domain)\n            result['os'] = 'Linux'\n            # raise 'Not Supported Unix or Linux.'\n        try:\n            proc = subprocess.Popen(shlex.split(cmd), stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n            out, err = proc.communicate()\n            encoding = chardet.detect(out)['encoding'].lower()\n            if encoding == 'gb2312':\n                out = out.decode('gb2312').encode('utf-8')\n            elif encoding == 'gbk':\n                out = out.decode('gbk').encode('utf-8')\n            strTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))\n            print(\"[%s] Task [%s] Finished ...OK\" % (strTime, domain))\n            result['raw'] = out\n\n        except Exception as e:\n            strTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(time.time()))\n            print(\"[{0}] Task [{1}] Error:{2} ...Failed!\".format(strTime, domain, e))\n            result['raw'] = ''\n        result['time'] = str(datetime.datetime.utcnow())\n        jsonResult = json.dumps(result, ensure_ascii=False)\n        # with open('%s.json' % (domain), 'w') as f:\n        #    f.write(jsonResult)\n        return jsonResult\n","sub_path":"lb-dcp/TYBotSDK2-Main-Solution/TYBotSDK2-Main/OldFrameThread/TYFGroup_D_Task/TY2_Bot_DRunPlugin/BotPlugin/routeTracerManager.py","file_name":"routeTracerManager.py","file_ext":"py","file_size_in_byte":17015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"588754412","text":"# CODING BAT > Warumup-2 > string_match\n\ndef string_match(a, b):\n    if len(a) > len(b): # or use the 'min' function\n        c = a\n    else:\n        c = b\n\n    count = 0\n    \n    for i in range(len(c) - 1):\n        sub_a = a[i:i + 2]\n        sub_b = b[i:i + 2]\n        \n        if sub_a == sub_b:\n            count += 1\n\n    return count\n","sub_path":"Warmup-2: 09 - string_match.py","file_name":"Warmup-2: 09 - string_match.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"639021047","text":"from django.http.response import HttpResponseBadRequest\nfrom .models import *\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom django.core import serializers\nimport json\n\n@api_view(['GET'])\ndef get_categories(request):\n    categories = Category.objects.all()\n    category_json = serializers.serialize('json', categories) \n    return Response({'data': category_json})\n\n@api_view(['GET'])\ndef get_products(request):\n    product = Product.objects.all()\n    product_json = serializers.serialize('json', product ) \n    return Response({'data': product_json})\n\n@api_view(['POST'])\ndef create_categories(request):\n    name = ''\n    description = ''\n\n    if(request.body != None):\n        print(request.body)\n        json_data_from_body = json.loads(request.body)\n        name = json_data_from_body['name']\n        description = json_data_from_body['description']\n\n    model_category = Category.objects.create(name=name,description=description)\n    model_category.save()\n    model_json = serializers.serialize('json', [model_category]) \n    return Response({'message': 'success creating categories', 'data_created': model_json})\n\n\n@api_view(['POST'])\ndef create_review(request):\n    if(request.body != None):\n\n        review_data = {}\n        if(request.POST != {}):\n            # ini dari parameter POST\n            review_data['name'] = request.POST.get('name')\n            review_data['email'] = request.POST.get('email')\n            review_data['review'] = request.POST.get('review')\n        else:\n            # ini dari body\n            json_data = json.loads(request.body)\n            review_data['name'] = json_data['name']\n            review_data['email'] = json_data['email']\n            review_data['review'] = json_data['review']\n\n        product_model = Product.objects.get(pk=1)\n        review_model = Review.objects.create(\n            name=review_data['name'],\n            email=review_data['email'],\n            review=review_data['review'],\n            product=product_model,\n        )\n        review_model.save()\n        model_json = serializers.serialize('json', [review_model])\n        return Response({'message': 'success create review', 'model_created': model_json})\n    else:\n        return HttpResponseBadRequest(\"Tidak ada request\")\n","sub_path":"mywebsite/mainapp/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"129976483","text":"from data_loader import DataLoader\nimport numpy as np\nnp.random.seed(1)\nfrom skip_thought import SkipThoughtEncoder\nimport os\nfrom time import time\n\n# TO CREATE SKIPTHOUGHT EMBEDDINGS OF 4 CONTEXT SENTENCES OF LSTM GENERATED DATA\n\nstart_time = time()\nLOAD_STORED_VAL_EMBEDDINGS = False\nenc_test_data_filename = \"../data/ours/enc_testreport_data10.csv\"\nenc_test_context_data_filename = \"../data/ours/enc_testreport_data10_context.csv\"\n\n# load NEW TRAINING data with GENERATED FAKE ENDINGS\nd = DataLoader()\ntest_right_ending_nr, test_context_sentences, test_ending_sentence1, test_ending_sentence2 = d.load_test_data_with_right_ending_nr()\nnumber_of_training_samples = len(test_right_ending_nr)\nprint(\"There are \", number_of_training_samples, \" test training samples.\")\n\ntest_context_sentences = list(map(lambda i: [\" \".join(i)], test_context_sentences))\ntest_context_sentences = np.array(test_context_sentences)\nprint(\"shape of test data context: \", test_context_sentences.shape)\n\nif LOAD_STORED_VAL_EMBEDDINGS:\n    print(\"Do nothing.\")\n\nelse:\n    # initialize SkipThoughtEncoder\n    s = SkipThoughtEncoder()\n    s.init()\n\n    # skipthought-encode test generated data\n    enc_test_ending_sentence2 = np.array(list(map(s.encoder.encode, test_ending_sentence2)))\n    enc_test_context_sentences = np.array(list(map(s.encoder.encode, test_context_sentences)))\n    print(\"Dimension of test context vector: \", enc_test_context_sentences.shape)\n    enc_test_ending_sentence1 = np.array(list(map(s.encoder.encode, test_ending_sentence1)))\n\n    # stack encoded test data to write to file\n    stacked_endings = np.concatenate((enc_test_ending_sentence1, enc_test_ending_sentence2), axis=1)\n    print(\"shape of enc_test_context_sentences: \", enc_test_context_sentences.shape)\n    print(\"shape of stacked_endings: \", stacked_endings.shape)\n    stacked_enc_test_data = np.concatenate((enc_test_context_sentences, stacked_endings), axis=1)\n\n    # expected data shape: number_of_test_samples x 3 x 4800\n    print(\"Dimension of stacked test data: \", stacked_enc_test_data.shape)\n    # write to file\n    flattened_test_data = np.ravel(stacked_enc_test_data)\n    print(\"First element: \", flattened_test_data[0])\n    print(\"Second element: \", flattened_test_data[1])\n    print(\"Third element: \", flattened_test_data[2])\n\n    os.makedirs(os.path.dirname(enc_test_context_data_filename), exist_ok=True)\n    np.savetxt(enc_test_context_data_filename, flattened_test_data, fmt='%1.10f')\n\nprint(\"Done. It took \", (time() - start_time) / 60 / 60, \" hours. \")\n","sub_path":"code/testreport_create_context_skipthought_embeddings.py","file_name":"testreport_create_context_skipthought_embeddings.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"611417339","text":"import os, sys, pickle\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom time import sleep\n\nRESULT_DIR = \"./\"\n\ndef main(start_page, end_page, key):\n\n    '''\n    start_page (int) : １〜\n    key (str) : 検索文字列\n    \n    >>> main(start_page=1, end_page=20, key=\"久保利明\")\n\n    保存されるデータ : 各棋譜データ（辞書）の入ったリスト\n    各棋譜データは以下のような形式。\n    {\n        'info': {\n            '先手': '久保利明',\n            '場所': '関西将棋会館',\n            '後手': '渡辺明',\n            '戦型': '先手四間飛車',\n            '手合割': '平手',\n            '持ち時間': '4時間',\n            '棋戦': '棋聖戦',\n            '棋戦詳細': '棋聖戦',\n            '消費時間': '',\n            '開始日時': '2010-03-01 09:00:00'\n        },\n        'kifu': ['+7776FU', '-3334FU', ... ]\n    }\n\n    '''\n\n    # ブラウザを開く。\n    driver = webdriver.Chrome()\n    # TOP画面を開く。\n    driver.get(\"https://shogidb2.com/\")\n\n    name = key\n\n    # 検索窓\n    elem_serach = driver.find_element_by_name(\"q\")\n    elem_serach.send_keys(name)\n    # 検索\n    search_button = driver.find_element_by_class_name(\"input-group-append\")\n    search_button.click()\n\n\n    result = []\n\n    no_more_page = False\n    page_number = start_page - 1\n    kifu_list_page = None\n\n    while True:\n        sleep(0.5)\n        \n        if kifu_list_page is not None:\n            driver.get(kifu_list_page)\n            \n        sleep(0.5)\n\n        if no_more_page:\n            break\n\n        page_number += 1\n        print(\"page number : {}\".format(str(page_number)))\n        if page_number == end_page + 1:\n            break\n\n\n        def find_the_pagenation():\n\n            while True:\n        \n                # ページネーション\n                def find_the_pagenation_in_this_page():\n\n                    pagenations = driver.find_elements_by_class_name(\"page-item\")\n                    pagenations = pagenations[:int(len(pagenations)/2)]\n                    for pagenation in pagenations:\n                        pagenation_link = pagenation.find_element_by_tag_name(\"a\")\n                        if pagenation_link.text == str(page_number):\n                            href = pagenation_link.get_attribute(\"href\")\n            #                 pagenation_link.click()\n                            driver.get(href)\n                            \n                            return True\n\n                    return False\n\n                found = find_the_pagenation_in_this_page()\n\n                if found:\n                    break\n\n                else:\n                    link_arial_next = driver.find_elements_by_class_name(\"page-link\")[-1]\n                    # 「>」ボタンをクリック\n                    href_arial_next = link_arial_next.get_attribute(\"href\")\n                    driver.get(href_arial_next)\n\n        find_the_pagenation()\n\n        kifu_list_page = driver.current_url\n\n        # 各ページ\n        i = 0\n\n        print(\"page number : \")\n        print(page_number)\n\n        while True:\n\n            # 各棋譜をみる\n            sleep(1)\n            print(i)\n\n            contents = driver.find_element_by_class_name(\"list-group\")\n            all_kifu_link = contents.find_elements_by_class_name(\"list-group-item\")\n            if i == len(all_kifu_link):\n                if len(all_kifu_link) < 20:\n                    no_more_page = True\n                break\n            all_kifu_link[i].click()\n            i += 1\n\n            # 対局情報を取得\n            rows = driver.find_elements_by_tag_name(\"tr\")\n            info = {}\n            # info = []\n            for row in rows:\n                abouts = row.find_elements_by_tag_name(\"th\")\n                if len(abouts) > 0:\n                    about_text = abouts[0].text\n                    val = row.find_element_by_tag_name(\"td\")\n                    val_text = val.text\n                    info[str(about_text)] = val_text\n                    # info.append((about_text, val_text))\n\n            print(info)\n\n    #         print(info)\n\n            # 棋譜モーダルを開く\n            csa_modal_buttom = driver.find_element_by_id(\"csa-export\")\n            csa_modal_buttom.click()\n            sleep(1)\n            # 棋譜を取得\n            modal_field = driver.find_element_by_class_name(\"control-group\")\n            modal_textarea = modal_field.find_element_by_tag_name(\"textarea\")\n            csa_text = modal_textarea.text\n\n            # csaから指し手を取り出す\n            hands = []  # hands = [] になることがある（棋譜がない）\n            for csa_row in csa_text.split(\"\\n\"):\n                if len(str(csa_row)) > 0:\n                    if (csa_row[0] == \"+\") or (csa_row[0] == \"-\"):\n                        if csa_row != \"+\":\n                            hands.append(csa_row)\n\n            result_thie_war = {\"info\" : info, \"kifu\" : hands}\n            result.append(result_thie_war)\n\n            # 棋譜一覧に戻る\n            driver.back()\n\n        print(len(result))\n        \n    with open(os.path.join(RESULT_DIR, \"四間飛車_1to10.pickle\"), mode=\"wb\") as f:\n        pickle.dump(result, f)\n        \nif __name__ == \"__main__\":\n\n    main(start_page=1, end_page=10, key=\"四間飛車\")","sub_path":"bot/src/scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":5364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"8541720","text":"import pymysql.cursors\nimport discord\nfrom utils import parsing, output\n\nclass Mysql:\n    \"\"\"\n    Handles database related tasks.\n    \"\"\"\n\n    def __init__(self):\n        config = parsing.parse_json('config.json')[\"mysql\"]\n        self.__host = config[\"db_host\"]\n        self.__port = int(config.get(\"db_port\", 3306))\n        self.__db_user = config[\"db_user\"]\n        self.__db_pass = config[\"db_pass\"]\n        self.__db = config[\"db\"]\n        self.__connected = 1\n        self.__setup_connection()\n\n    def __setup_connection(self):\n        self.__connection = pymysql.connect(\n            host=self.__host,\n            port=self.__port,\n            user=self.__db_user,\n            password=self.__db_pass,\n            db=self.__db)\n        self.__cursor = self.__connection.cursor(pymysql.cursors.DictCursor)\n\n    def make_user(self, name, snowflake):\n        \"\"\"\n        Sets up a new user in the database with given name and snowflake ID\n        \"\"\"\n        to_exec = \"INSERT INTO users (snowflake_pk, username, balance) VALUES(%s, %s, %s)\"\n        self.__cursor.execute(to_exec, (str(snowflake), name, '0'))\n        self.__connection.commit()\n\n    def check_for_user(self, name, snowflake):\n        \"\"\"\n        Checks for a new user and creates one if needed.\n        Returns snowflake of created user.\n        \"\"\"\n\n        to_exec = \"SELECT snowflake_pk FROM users WHERE snowflake_pk LIKE %s\"\n        self.__cursor.execute(to_exec, (str(snowflake)))\n        result_set = self.__cursor.fetchone()\n        if result_set is None:\n            self.make_user(name, snowflake)\n            result_set = (snowflake)\n        return result_set\n\n    def get_user(self, snowflake):\n        \"\"\"\n        Gets a user given a snowflake ID.\n        \"\"\"\n        to_exec = \"SELECT snowflake_pk, username, balance FROM users WHERE snowflake_pk LIKE %s\"\n        self.__cursor.execute(to_exec, (str(snowflake)))\n        result_set = self.__cursor.fetchone()\n\n        return result_set\n\n    def add_server(self, server: discord.Server):\n        \"\"\"\n        Adds a server to the database.\n        \"\"\"\n        to_exec = \"INSERT INTO server (server_id, enable_soak) VALUES(%s, %s)\"\n        self.__cursor.execute(to_exec, (str(server.id), str(int(server.large))))\n        self.__connection.commit()\n\n    def remove_server(self, server: discord.Server):\n        \"\"\"\n        Removes a server from the database.\n        \"\"\"\n        to_exec = \"DELETE FROM server WHERE server_id = %s\"\n        self.__cursor.execute(to_exec, (str(server.id),))\n        to_exec = \"DELETE FROM channel WHERE server_id = %s\"\n        self.__cursor.execute(to_exec, (str(server.id),))\n        self.__connection.commit()\n\n    def add_channel(self, channel: discord.Channel):\n        \"\"\"\n        Adds a channel to the database.\n        \"\"\"\n        to_exec = \"INSERT INTO channel(channel_id, server_id, enabled) VALUES(%s, %s, 1)\"\n        self.__cursor.execute(to_exec, (str(channel.id), str(channel.server.id)))\n        self.__connection.commit()\n\n    def remove_channel(self, channel):\n        \"\"\"\n        Removes a channel from the database.\n        \"\"\"\n        to_exec = \"DELETE FROM channel WHERE channel_id = %s\"\n        self.__cursor.execute(to_exec, (str(channel.id),))\n        self.__connection.commit()\n\n    def check_soak(self, server: discord.Server) -> bool:\n        \"\"\"\n        Checks if soak is enabled for a specific server.\n        \"\"\"\n        to_exec = \"SELECT enable_soak FROM server WHERE server_id = %s\"\n        self.__cursor.execute(to_exec, (str(server.id)))\n        result_set = self.__cursor.fetchone()\n        return result_set['enable_soak']\n\n    def set_soak(self, server, to):\n        \"\"\"\n        Sets the soak setting for a server.\n        \"\"\"\n        to_exec = \"UPDATE server SET enable_soak = %s WHERE server_id = %s\"\n        self.__cursor.execute(to_exec, (to, str(server.id),))\n        self.__connection.commit()\n\n    def set_balance(self, user, to):\n        \"\"\"\n        Sets the soak setting for a server.\n        \"\"\"\n        to_exec = \"UPDATE users SET balance = %s WHERE snowflake_pk = %s\"\n        self.__cursor.execute(to_exec, (to, user.id,))\n        self.__connection.commit()\n","sub_path":"utils/mysql_module.py","file_name":"mysql_module.py","file_ext":"py","file_size_in_byte":4168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"323127332","text":"import os\nimport shutil\nimport torch\nfrom globals import globals\n\n\ndef get_last_checkpoint(checkpoint_dir):\n    checkpoints = [checkpoint for checkpoint in os.listdir(checkpoint_dir)\n                   if checkpoint.startswith('epoch_') and checkpoint.endswith('.pth')]\n    if checkpoints:\n        return os.path.join(checkpoint_dir, list(sorted(checkpoints))[-1])\n    return None\n\n\ndef get_initial_checkpoint(train_dir):\n    checkpoint_dir = os.path.join(train_dir, 'checkpoint')\n    return get_last_checkpoint(checkpoint_dir)\n\n\ndef get_checkpoint(train_dir, name):\n    checkpoint_dir = os.path.join(train_dir, 'checkpoint')\n    return os.path.join(checkpoint_dir, name)\n\n\ndef remove_old_checkpoint(checkpoint_dir, keep):\n    checkpoints = [checkpoint for checkpoint in os.listdir(checkpoint_dir)\n                   if checkpoint.startswith('epoch_') and checkpoint.endswith('.pth')]\n    checkpoints = sorted(checkpoints)\n    for checkpoint in checkpoints[:-keep]:\n        os.remove(os.path.join(checkpoint_dir, checkpoint))\n\n\ndef copy_last_n_checkpoints(train_dir, n, name):\n    checkpoint_dir = os.path.join(train_dir, 'checkpoint')\n    checkpoints = [checkpoint for checkpoint in os.listdir(checkpoint_dir)\n                   if checkpoint.startswith('epoch_') and checkpoint.endswith('.pth')]\n    checkpoints = sorted(checkpoints)\n    for i, checkpoint in enumerate(checkpoints[-n:]):\n        shutil.copyfile(os.path.join(checkpoint_dir, checkpoint),\n                        os.path.join(checkpoint_dir, name.format(i)))\n\n\ndef load_checkpoint(model, optimizer, checkpoint):\n    if globals.verbose:\n        print(\"Loading checkpoint [{}]\".format(checkpoint))\n    if torch.cuda.device_count() >= 1:\n        checkpoint = torch.load(checkpoint)\n    else:\n        checkpoint = torch.load(checkpoint, map_location='cpu')\n\n    checkpoint_dict = {}\n    for k, v in checkpoint['state_dict'].items():\n        if k.startswith('module.'):\n            checkpoint_dict[k[7:]] = v\n        else:\n            checkpoint_dict[k] = v\n\n    model.load_state_dict(checkpoint_dict)  # , strict=False)\n\n    if optimizer is not None:\n        optimizer.load_state_dict(checkpoint['optimizer_dict'])\n\n    step = checkpoint['step'] if 'step' in checkpoint else -1\n    last_epoch = checkpoint['epoch'] if 'epoch' in checkpoint else -1\n\n    if globals.verbose:\n        print('last epoch: {}'.format(last_epoch))\n\n    return last_epoch, step\n\n\ndef save_checkpoint(train_dir, model, optimizer, epoch, step=0, keep=None, weights_dict=None, name=None):\n    checkpoint_dir = os.path.join(train_dir, 'checkpoint')\n\n    if name:\n        checkpoint_path = os.path.join(checkpoint_dir, '{}.pth'.format(name))\n    else:\n        checkpoint_path = os.path.join(checkpoint_dir, 'epoch_{:04d}.pth'.format(epoch))\n\n    if weights_dict is None:\n        weights_dict = {\n            'state_dict': model.state_dict(),\n            'optimizer_dict': optimizer.state_dict(),\n            'epoch': epoch,\n            'step': step,\n        }\n    torch.save(weights_dict, checkpoint_path)\n\n    if keep is not None and keep > 0:\n        remove_old_checkpoint(checkpoint_dir, keep)\n","sub_path":"utils/checkpoint.py","file_name":"checkpoint.py","file_ext":"py","file_size_in_byte":3130,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"477524704","text":"import src.rq as rq\nimport pprint\nimport config\nimport json\nimport os\nimport csv\n\napp_path = os.path.dirname(__file__)\n__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))\n\ngroupname = input('Group: ')\n\nprompt_file_location = input ('File Location: ')\nfile_location = os.path.expanduser(prompt_file_location)\n\n# # filepath = os.path.join(file_directory, filename)\nprint(file_location)\n\n\ndef pretty(string):\n    pp = pprint.PrettyPrinter(indent=4)\n    return pp.pprint(string)\n\n\ndef flatten(f_data, d):\n    for k, v in d.items():\n        if isinstance(v, dict):\n            flatten(f_data, v)\n        else:\n            f_data[k] = v\n\nOKTA_BASE_URL = 'https://[ORGNAME].okta.com/api/v1'\nokta_search_group_arguments = {\n    'url': OKTA_BASE_URL+'/groups/',\n    'headers': {\n        'Accept': 'application/json',\n        'Content-Type': 'application/json',\n        'Authorization': config.okta_api_token,\n    },\n    'params': {\n        'q': groupname,\n}\n}\nokta_group_user_update_args = {\n    'url': OKTA_BASE_URL+'/groups/x/users/x',\n    'headers': {\n        'Accept': 'application/json',\n        'Content-Type': 'application/json',\n        'Authorization': config.okta_api_token,\n    },\n}\nokta_user_arguments = {\n    'url': OKTA_BASE_URL+'/users/x',\n    'headers': {\n        'Accept': 'application/json',\n        'Content-Type': 'application/json',\n        'Authorization': config.okta_api_token,\n    }\n}\n\ndef main():\n    okta()\n\ndef okta():\n    def search_okta_group():\n        try:\n            r = rq.get_url_kwargs(**okta_search_group_arguments)\n            data = json.loads(r.content)\n            output = [{k: v for k, v in i.items() if k in ['id']} for i in data]\n            \n            for i in output:\n                for k, v in i.items():\n                    return v\n        except Exception as e:\n            print(e)\n\n    okta_group_id = search_okta_group()\n    userids = []\n\n    def get_okta_user_ids():\n        try:\n            with open(file_location) as f:\n                read_csv = csv.DictReader(f)\n                for row in read_csv:\n                    okta_user_arguments.update({'url': OKTA_BASE_URL+'/users/{}'.format(row['username'])})\n                    r = rq.get_url_kwargs(**okta_user_arguments)  \n                    data = json.loads(r.content)\n                    userids.append(data['id'])\n\n\n        except Exception as e:\n            print(e)\n\n    get_okta_user_ids()\n    print(userids)\n\n    def push_okta_user_to_group():\n        try:\n            for i in userids:\n                okta_group_user_update_args.update({'url': OKTA_BASE_URL+'/groups/{}/users/{}'.format(okta_group_id, i)})\n                r = rq.put_url_kwargs(**okta_group_user_update_args)\n        except Exception as e:\n            print(e)\n\n    push_okta_user_to_group()\n    \n\n\n\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"push-group-users.py","file_name":"push-group-users.py","file_ext":"py","file_size_in_byte":2857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"653458946","text":"# Найти сумму и произведение цифр, введённого пользователем трёхзначного числа.\n\nnum = int(input('Введите трёъзначное число: '))\n\n# Находим сумму цифр\nsumm = (num // 100) + ((num // 10) % 10) + (num % 10)\n# Находим произведение цифр\nproizv = (num // 100) * ((num // 10) % 10) * (num % 10)\n\nprint(f'Сумма цифр = {summ}')\nprint(f'Произведение цифр = {proizv}')\n","sub_path":"homework_1/task_1.py","file_name":"task_1.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"320220651","text":"# 完整神经网络样例程序\n\n## 神经网络训练步骤\n## 1. 定义神经网络的结构和前向传播的输出结果 得到预测值, 并对比预测值和真实值的差距\n## 2. 定义损失函数以及选择反向传播优化的算法\n## 3. 生成会话并在训练数据上反复运行反向传播优化算法\n\nimport tensorflow as tf\n# Numpy 生成模拟数据集\nfrom numpy.random import RandomState\n\n# 训练数据 batch 大小\nbatch_size = 8\n\nw1 = tf.Variable(tf.random_normal([2, 3], stddev=1, seed=1), name='w1')\nw2 = tf.Variable(tf.random_normal([3, 1], stddev=1, seed=1), name='w2')\n\n# shape 的一个维度使用 None 可方便使用不打的 batch 大小\n# 训练时需要将数据分成较小 batch\n# 测试时可一次性使用全部数据\n# 数据集较小时比较方便测试\n# 数据集较大时，大量数据放入一个 batch 可能导致内存溢出\nx = tf.placeholder(tf.float32, shape=(None, 2), name='x-input')\ny_ = tf.placeholder(tf.float32, shape=(None, 1), name='y-input')\n\n# 定义神经网络前向传播过程\na = tf.matmul(x, w1)\ny = tf.matmul(a, w2)\n\n# 损失函数和反向传播过程\n# 交叉熵 H(p, q) = -∑p(x)logq(x)\n\n# y_ 正确结果\n# y 预测结果\n# tf.clip_by_value 限定张量数值在一个范围内 1.0x10^-10 ~ 1.0\n# * 矩阵元素直接相乘，不同于矩阵乘法 tf.matmul()\n# 平均交叉熵\ncross_entropy = -tf.reduce_mean(y_*tf.log(tf.clip_by_value(y, 1e-10, 1.0)))\n# cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(y, y_)\ntrain_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)\n\n# 随机数生成模拟数据集\nrdm = RandomState(1)\ndataset_size = 128\nX = rdm.rand(dataset_size, 2)\n# 定义规则给出样本的标签\n# x1 + x2 < 1 为正样本, 其他为负样本\n# 此处 0 为负样本, 1  为正样本\nY = [[int(x1+x2 < 1)] for (x1, x2) in X]\n\nwith tf.Session() as sess:\n    init_op = tf.global_variables_initializer()\n    sess.run(init_op)\n    print(sess.run(w1))\n    print(sess.run(w2))\n\n    # print(8%128)  8\n    # print(16%128) 16\n\n    # 训练轮数\n    STEPS = 5000\n    for i in range(STEPS):\n        # 每次选取 batch_size 个样本进行训练\n        start = (i * batch_size) % dataset_size\n        end = min(start+batch_size, dataset_size)\n\n        # 选取样本训练神经网络并更新参数\n        sess.run(train_step,\n                 feed_dict={\n                     x: X[start:end],\n                     y_: Y[start:end]\n                 })\n        if i % 1000 == 0:\n            # 每隔一段时间在所有数据的交叉熵并输出\n            total_cross_entropy = sess.run(\n                cross_entropy, feed_dict={\n                    x: X, y_: Y\n                }\n            )\n            # 结果随训练进行，交叉熵逐渐变小，说明预测结果与真实结果差距越小\n            print(\"After %d training step(s), cross entropy on all data is %g\" %(i, total_cross_entropy))\n    print(sess.run(w1))\n    print(sess.run(w2))","sub_path":"tf_gdl_in_action/3.4.5.py","file_name":"3.4.5.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"380512687","text":"from collections import OrderedDict\r\n#Using Ordered Dictionary\r\nd = OrderedDict()\r\nn = int(input())\r\nwhile n:\r\n    s = input().split()\r\n    if len(s) > 2:\r\n        key = s[0]+\" \"+s[1]\r\n        value = s[2]\r\n    else:\r\n        key = s[0]\r\n        value = s[1]\r\n    if not key in d:\r\n        d[key] = int(value)\r\n    else:\r\n        d[key] += int(value)\r\n    n -= 1\r\nfor k,v in d.items():\r\n    print(k,v)\r\n","sub_path":"Dictionary_Test.py","file_name":"Dictionary_Test.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"222751559","text":"import json\n\nbase_path = \"../config/\"\n\n\ndef read_json(file_name, key=None):\n    if file_name is None:\n        file_path = base_path\n    else:\n        file_path = base_path + file_name\n    with open(file_path, encoding='UTF-8') as f:\n        f = json.load(f)\n        if key is None:\n            return None\n        else:\n            # 通过key来获取每个定位所有信息\n            data = f.get(key)\n            return data\n\n\ndef get_value(key, file_path, key1):\n    data = read_json(file_path, key1)\n    # print(data.get(key))\n    return data.get(key)\n\n\nif __name__ == '__main__':\n    # read_json(\"rank.json\", \"EnterRank\")\n    get_value(\"type\", \"rank.json\", \"EnterRank\")\n\n","sub_path":"Tool/handjson.py","file_name":"handjson.py","file_ext":"py","file_size_in_byte":680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"470534086","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#import sqlite, create and connect to data file \nimport sqlite3\n\n\n# In[2]:\n\n\nconn = sqlite3.connect('demo_data.sqlite3')\n\n\n# In[3]:\n\n\ncurs = conn.cursor()\n\n\n# In[4]:\n\n\n#create a table\ndef nbm_create():\n    b = '''CREATE TABLE demo(\n        s text NOT NULL,\n        x integer NOT NULL,\n        y integer NOT NULL\n    )'''\n    curs.execute(b)\n\n\n# In[5]:\n\n\nnbm_create()\n\n\n# In[6]:\n\n\n#add DATA ROWS TO THE TABLE \ndef nbm_insert():\n    b1 = '''INSERT INTO demo VALUES\n    ('g', 3, 9),\n    ('v', 5, 7),\n    ('f', 8, 7)'''\n    curs.execute(b1)\n\n\n# In[7]:\n\n\nnbm_insert()\n\n\n# In[8]:\n\n\n#saving my data table\ndef nbm_save():\n    conn.commit()\n    \nnbm_save()\n\n\n# In[9]:\n\n\n#Count Rows\ndef row_count():\n    a2 = '''\n    SELECT COUNT (s)\n    FROM demo\n    '''\n    curs.execute(a2)\n    return curs.fetchall()\n\nrow_count()\n\n\n# In[10]:\n\n\n#Count rows greater than 5\ndef min_count():\n    p1 = '''\n    SELECT COUNT(*)\n    FROM demo\n    WHERE x >=5 and y>=5\n    '''\n    curs.execute(p1)\n    return curs.fetchall()\n\nmin_count()\n\n\n# In[11]:\n\n\n#Count Unique y Values\ndef unique_count():\n    a4 = '''\n    SELECT COUNT(DISTINCT y)\n    FROM demo\n    '''\n    curs.execute(a4)\n    return curs.fetchall()\n\nunique_count()\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"unit3-sprint2-sprint-challenge/demo_data.py","file_name":"demo_data.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"101065367","text":"end = None\n\n\ndef skoczek_moves(pos):\n    c, r = pos\n    return ((c - 2, r - 1),\n            (c - 2, r + 1),\n\n            (c + 2, r - 1),\n            (c + 2, r + 1),\n\n            (c - 1, r - 2),\n            (c + 1, r - 2),\n\n            (c - 1, r + 2),\n            (c + 1, r + 2))\n\n\ndef konik(board_size, starting_point=None, finito=None):\n    def move_valid(move):\n        return 0 <= move[0] < board_size and 0 <= move[1] < board_size\n\n    static_moves = [[list() for _ in range(board_size)] for _ in range(board_size)]\n\n    for c in range(board_size):\n        for r in range(board_size):\n            static_moves[c][r] = list(filter(move_valid, skoczek_moves((c, r))))\n        end\n    end\n\n    board = [[-1 for _ in range(board_size)] for _ in range(board_size)]\n\n    def num_possible_moves(move):\n        n = 0\n        for m in static_moves[move[0]][move[1]]:\n            if board[m[0]][m[1]] < 0:\n                n += 1\n            end\n        end\n        return n\n\n    def warnsdorff(moves):\n        moves.sort(key=num_possible_moves)\n        return moves\n\n    def jumpto(pos, jumps=1):\n        board[pos[0]][pos[1]] = jumps\n\n        if jumps >= board_size * board_size and (finito is None or pos == finito):\n            return True\n\n        for move in warnsdorff(static_moves[pos[0]][pos[1]]):\n            if board[move[0]][move[1]] < 0:\n                if jumpto(move, jumps + 1):\n                    return True\n                end\n            end\n        end\n\n        board[pos[0]][pos[1]] = -1\n\n        return False\n\n    if starting_point is not None:\n        jumpto(starting_point)\n    else:\n        for c in range(board_size):\n            for r in range(board_size):\n                if jumpto((c, r)):\n                    return board\n                end\n            end\n        end\n\n    return board\n\n\nb = konik(31, starting_point=(0, 0))\nfor row in b:\n    for i in row:\n        print(\"\\t\" + str(i), end=\"\")\n    end\n    print()\nend\n","sub_path":"konik_warnsdorffa.py","file_name":"konik_warnsdorffa.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"384306399","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.dates as mdates\nimport datetime\nimport sys\nimport wget\nimport os\nimport random\nfrom spacepy import pycdf\nfrom urllib import error\nfrom matplotlib.pyplot import cm\nimport calendar\nfrom dateutil.relativedelta import relativedelta\n\nfrom scipy import signal\nfrom matplotlib.pyplot import cm \n\n\nimport shutil\n\n\nplt.close(\"all\")\n# plt.ion()\n\n# ======= Parameters to set\n\ndata_directory = '/Users/bryanyamashiro/Documents/Research_Projects/Data'\nsave_option = 'yes' # either 'yes' or 'no'\n\nevent_option = 'yes' # either 'yes' or 'no'\ngoes_corrected_option = 'yes'\n\nsave_plot_option = 'yes'\nevent_option_show = 'no'\n\n# ========== Event list (Still being implemented, do not uncomment)\n\nscript_directory = '/Users/bryanyamashiro/Documents/Research_Projects/Scripts'\nevent_list_directory = '/Users/bryanyamashiro/Documents/Research_Projects/Scripts/event_lists'\n\nevent_plot_folder = 'a_events'\nevent_list_name = 'a_events_list.txt'\n\n\nevent_columns = ['event_date_st', 'event_date_ed', 'flare_int', 'event_st_hr', 'event_ed_hr', 'opt_1', 'opt_2', 'opt_3', 'prot_sat', 'xray_sat', 'prot_opt'] # , 'opt_4'\n\n\nif event_option == 'yes':\n\tevent_list_file = pd.read_csv(f'{event_list_directory}/{event_list_name}', sep = '\\t', names=event_columns, comment='#')\n\n\n# ========== Definitions\n\ndef daterange( start_date, end_date ):\n    if start_date <= end_date: #\n        for n in range( ( end_date - start_date ).days + 1 ):\n            yield start_date + datetime.timedelta( n )\n    else:\n        for n in range( ( start_date - end_date ).days + 1 ):\n            yield start_date - datetime.timedelta( n )\n\n#==============Choosing Dataset\nprint(f'\\n{\"=\"*40}\\n{\"=\" + \"OMNI Event Script\".center(38,\" \") + \"=\"}\\n{\"=\"*40}')\n\n\n# radio_check = os.path.isfile(f'{data_directory}/WIND/RAD1/{radio_name}')\nfor event_no in range(len(event_list_file)):\n\n\tevent_list = event_list_file.loc[event_no]\n\n\n\tif os.path.isfile(f\"{script_directory}/{event_plot_folder}/omni_test_{event_list['event_date_st']}.png\") == True:\n\t\tprint(f\"The plot for {event_list['event_date_st']} already exists.\")\n\n\telif os.path.isfile(f\"{script_directory}/{event_plot_folder}/omni_test_{event_list['event_date_st']}.png\") == False:\n\t\tprint(f\"== {event_list['event_date_st']} ==\")\n\t\t# event_list = event_list_file.loc[event_no]\n\t\n\t\t# print(f'{\"=\"*40}\\n{\"=\" + \"DATASETS\".center(38,\" \") + \"=\"}\\n{\"=\"*40}\\n1 - GOES-13/15 Proton Flux\\n2 - Wind Type III Radio Bursts\\n3 - Neutron Monitor Counts (Requires Internet Connection)\\n4 - ACE/Wind Solar Wind Speed\\n5 - GOES-13/15 Xray Flux\\n{\"=\"*40}')\n\t\t# \\n{\"=\"*40}\\n1 - GOES-13/15 Proton Flux\\n2 - Wind Type III Radio Bursts\\n3 - Neutron Monitor Counts (Requires Internet Connection)\\n4 - ACE/Wind Solar Wind Speed\\n5 - GOES-13/15 Xray Flux\\n{\"=\"*40}')\n\t\n\t\t'''\n\t\t1 - GOES Proton Flux\n\t\t2 - Wind Type III Radio Bursts\n\t\t3 - Neutron Monitor Counts\n\t\t4 - ACE/Wind Solar Wind Speed'\n\t\t5 - GOES-15 Xray Flux\n\t\t'''\n\t\t\n\t\t\n\t\toption_bin_set = set()\n\t\twhile True: # energy_bin != 'done':\n\t\t\tif event_option == 'yes':\n\t\t\t\toption_bin_set = {'1', '4', '5'}\n\t\t\n\t\t\t\tbreak\n\t\t\n\t\t\tif event_option != 'yes':\n\t\t\t\toption_bin = input('Enter Dataset Option then \"done\" or \"all\": ').lower()\n\t\t\n\t\t\t\tif option_bin != 'done':\n\t\t\t\t\tif option_bin == 'all':\n\t\t\t\t\t\toption_bin_set.add('1')\n\t\t\t\t\t\toption_bin_set.add('2')\n\t\t\t\t\t\toption_bin_set.add('3')\n\t\t\t\t\t\toption_bin_set.add('4')\n\t\t\t\t\t\toption_bin_set.add('5')\n\t\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\t\t\t\telif int(option_bin) < 6:\n\t\t\t\t\t\toption_bin_set.add(option_bin)\n\t\t\t\n\t\t\t\t\t\tif len(option_bin_set) > 4:\n\t\t\t\t\t\t\tprint('SELECTION ERROR: Only 4 datasets are allowed per canvas.')\n\t\t\t\t\t\t\tsys.exit(0)\n\t\t\t\n\t\t\t\telif option_bin == 'done':\n\t\t\t\t\tbreak\n\t\t\n\t\t\n\t\t#===============Time frame\n\t\tif event_option == 'yes':\n\t\t\tstart_date = str(event_list['event_date_st'])\n\t\t\tend_date = str(event_list['event_date_ed'])\n\t\t\n\t\tif event_option != 'yes':\n\t\t\tstart_date = input('Enter a start date (yyyymmdd): ')\n\t\t\tend_date = input('Enter a end date (yyyymmdd): ')\n\t\t\n\t\t\n\t\tif end_date == '':\n\t\t\tend_date = start_date\n\t\t\n\t\tstart_day = start_date[6:8]\n\t\tstart_month = start_date[4:6]\n\t\tstart_year = start_date[:4]\n\t\t\n\t\tend_day = end_date[6:8]\n\t\tend_month = end_date[4:6]\n\t\tend_year = end_date[:4]\n\t\t'''\n\t\tif int(start_year) < 2010: # testing old 2003 data, uncomment laterß\n\t\t\tprint('\\nDATE ERROR: Date must start after September 1, 2010')\n\t\t\tsys.exit(0)\n\t\t'''\n\t\tif len(start_date) != 8 or len(end_date) != 8:\n\t\t\tprint('\\nDATE ERROR: Dates must have 8 digits.')\n\t\t\tsys.exit(0)\n\t\t\n\t\t\n\t\tif event_option == 'yes':\n\t\t\tstart_hour = str(event_list['event_st_hr'])\n\t\t\tend_hour = str(event_list['event_ed_hr'])\n\t\t\n\t\tif event_option != 'yes':\n\t\t\tstart_hour = input('Enter a start hour or \"full\": ').zfill(2)\n\t\t\n\t\t\tif start_hour.isdigit() == True:\n\t\t\t\tend_hour = input('Enter a end hour: ').zfill(2)\n\t\t\t\tif start_date == end_date:\n\t\t\t\t\tif (int(end_hour) - int(start_hour)) < 0:\n\t\t\t\t\t\tprint('\\nTIME ERROR: Difference between two hours must be greater than zero.')\n\t\t\t\t\t\tsys.exit(0)\n\t\t\t\telif int(end_hour) > 24 or int(start_hour) > 23:\n\t\t\t\t\tprint('\\nTIME ERROR: Hours must be between 0 and 23.')\n\t\t\t\t\tsys.exit(0)\n\t\t\n\t\t\tif start_hour.isdigit() == False:\n\t\t\t\tif start_hour == 'full':\n\t\t\t\t\tstart_hour = '00'.zfill(2)\n\t\t\t\t\tend_hour = '23'.zfill(2)\n\t\t\t\telse:\n\t\t\t\t\tprint('\\nTIME ERROR: Not a valid alternative hour.')\n\t\t\t\t\tsys.exit(0)\n\t\t\n\t\t\n\t\tstart = datetime.date( year = int(f'{start_date[0:4]}'), month = int(f'{start_date[4:6]}') , day = int(f'{start_date[6:8]}') )\n\t\tend = datetime.date( year = int(f'{end_date[0:4]}'), month = int(f'{end_date[4:6]}') , day = int(f'{end_date[6:8]}') )\n\t\t\n\t\t#=========Defining event strings\n\t\t\n\t\tevent_obj_start = datetime.datetime.strptime(f'{start_date} {start_hour}', '%Y%m%d %H')\n\t\tevent_obj_start_str = datetime.datetime.strftime(event_obj_start, '%Y%m%d %H:%M:%S')\n\t\tevent_obj_start_str_date = datetime.datetime.strftime(event_obj_start, '%Y%m%d %H')\n\t\t\n\t\tevent_obj_end = datetime.datetime.strptime(f'{end_date} {end_hour}', '%Y%m%d %H')\n\t\tevent_obj_end_str = datetime.datetime.strftime(event_obj_end, '%Y%m%d %H:%M:%S')\n\t\tevent_obj_end_str_date = datetime.datetime.strftime(event_obj_end, '%Y%m%d %H')\n\t\t\n\t\t\n\t\t#=========== 1: GOES Proton Flux\n\t\tif '1' in option_bin_set:\n\t\t\tif event_option == 'yes':\n\t\t\t\tsatellite_no = str(event_list['prot_sat'])\n\t\t\n\t\t\tif event_option != 'yes':\n\t\t\t\tsatellite_no = input('Specify which GOES Satellite for Proton Flux (13 or 15): ')\n\t\t\t\tif satellite_no != '13':\n\t\t\t\t\tif satellite_no != '15':\n\t\t\t\t\t\tprint('SATELLITE ERROR: Must specify either 13 or 15.')\n\t\t\t\t\t\tsys.exit(0)\n\t\t\n\t\t\t# print(f'\\n{\"=\"*40}\\n{\"=\" + f\"GOES-{satellite_no} Proton Flux\".center(38,\" \") + \"=\"}\\n{\"=\"*40}')\n\t\t\tprint(f'\\rProcessing GOES-{satellite_no} Proton Flux...', end='\\r')\n\t\t\t# print(f'{\"Energy Channels\".center(7, \" \")}\\n{\"-\"*20}\\n1: 6.5 MeV\\n2: 11.6 MeV\\n3: 30.6 MeV\\n4: 63.1 MeV\\n5: 165 MeV\\n6: 433 MeV')\n\t\n\t\t\tif goes_corrected_option == 'yes':\n\t\t\t\tprint(f'\\n{\"=\"*40}\\n{\"=\" + f\"GOES-{satellite_no} Proton Flux\".center(38,\" \") + \"=\"}\\n{\"=\"*40}')\n\t\t\n\t\t\t\tenergy_bin_list = []\n\t\t\n\t\t\t\tif start_date[4:6] != end_date[4:6]: # if the month of the start day is NOT the same as the month of the end date\n\t\t\t\t\tproton_df = pd.DataFrame([])\n\t\t\n\t\t\t\t\tcur_date =  start\n\t\t\t\t\tdate_in_year = [cur_date]\n\t\t\n\t\t\t\t\twhile cur_date < end:\n\t\t\t\t\t\t# print(cur_date)\n\t\t\t\t\t\tcur_date += relativedelta(months=1)\n\t\t\t\t\t\tdate_in_year.append(cur_date)\n\t\t\n\t\t\n\t\t\t\t\tif start.year >= 2011:\n\t\t\t\t\t\tcpflux_names = ['time_tag','ZPGT1E_QUAL_FLAG', 'ZPGT1E', 'ZPGT5E_QUAL_FLAG', 'ZPGT5E', 'ZPGT10E_QUAL_FLAG', 'ZPGT10E', 'ZPGT30E_QUAL_FLAG', 'ZPGT30E', 'ZPGT50E_QUAL_FLAG', 'ZPGT50E', 'ZPGT60E_QUAL_FLAG', 'ZPGT60E', 'ZPGT100E_QUAL_FLAG', 'ZPGT100E', 'ZPGT1W_QUAL_FLAG', 'ZPGT1W', 'ZPGT5W_QUAL_FLAG', 'ZPGT5W', 'ZPGT10W_QUAL_FLAG', 'ZPGT10W', 'ZPGT30W_QUAL_FLAG', 'ZPGT30W', 'ZPGT50W_QUAL_FLAG', 'ZPGT50W', 'ZPGT60W_QUAL_FLAG', 'ZPGT60W', 'ZPGT100W_QUAL_FLAG', 'ZPGT100W', 'ZPEQ5E_QUAL_FLAG', 'ZPEQ5E', 'ZPEQ15E_QUAL_FLAG', 'ZPEQ15E', 'ZPEQ30E_QUAL_FLAG', 'ZPEQ30E', 'ZPEQ50E_QUAL_FLAG', 'ZPEQ50E', 'ZPEQ60E_QUAL_FLAG', 'ZPEQ60E', 'ZPEQ100E_QUAL_FLAG', 'ZPEQ100E', 'ZPEQ5W_QUAL_FLAG', 'ZPEQ5W', 'ZPEQ15W_QUAL_FLAG', 'ZPEQ15W', 'ZPEQ30W_QUAL_FLAG', 'ZPEQ30W', 'ZPEQ50W_QUAL_FLAG', 'ZPEQ50W', 'ZPEQ60W_QUAL_FLAG', 'ZPEQ60W', 'ZPEQ100W_QUAL_FLAG', 'ZPEQ100W']\n\t\t\n\t\t\t\t\t\tenergy_bin_list.append(['ZPGT10W','>10 MeV', 'red'])\n\t\t\t\t\t\tenergy_bin_list.append(['ZPGT50W','>50 MeV', 'blue'])\n\t\t\t\t\t\tenergy_bin_list.append(['ZPGT100W','>100 MeV', 'lime'])\n\t\t\n\t\t\t\t\t\tfor date_event in date_in_year:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tf_l_day = calendar.monthrange(int(date_event.year), int(date_event.month)) #\tf_l_day = calendar.monthrange(int(detection_year), int(month_event))\n\t\t\t\t\t\t\t\tevent_f_day = str(f'{date_event.year}{str(date_event.month).zfill(2)}01') # {str(f_l_day[0]).zfill(2)}\n\t\t\t\t\t\t\t\tevent_l_day = str(f'{date_event.year}{str(date_event.month).zfill(2)}{str(f_l_day[1]).zfill(2)}')\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\tdir_check = os.path.isdir(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}')\n\t\t\t\t\t\t\t\tif dir_check == False:\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t    os.makedirs(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}')\n\t\t\t\t\t\t\t\t\texcept OSError as e:\n\t\t\t\t\t\t\t\t\t    if e.errno != errno.EEXIST:\n\t\t\t\t\t\t\t\t\t        raise\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tproton_name = f'g{satellite_no}_epead_cpflux_5m_{event_f_day}_{event_l_day}.csv' #g13_epead_cpflux_5m_20110101_20110131.csv\n\t\t\t\t\t\t\t\tproton_check = os.path.isfile(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}/{proton_name}')\n\t\t\t\t\t\n\t\t\t\t\t\t\t\tif proton_check == True:\n\t\t\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\t\t\tproton_df_ind = pd.read_csv(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}/{proton_name}', skiprows=718, date_parser=dateparse, names=cpflux_names,index_col='time_tag', header=0)\n\t\t\t\t\t\t\t\t\tproton_df = proton_df.append(proton_df_ind)\n\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\t\telif proton_check == False:\n\t\t\t\t\t\t\t\t\tproton_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_avg/{date_event.year}/{str(date_event.month).zfill(2)}/goes{satellite_no}/csv/{proton_name}'\n\t\t\t\t\t\t\t\t\t# proton_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_avg/{event_date[:4]}/{event_date[4:6]}/goes{satellite_no}/csv/{proton_name}'\n\t\t\t\t\t\t\t\t\tproton_in = wget.download(proton_url)\n\t\t\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\t\t\tproton_df_ind = pd.read_csv(f'{proton_in}', skiprows=718, date_parser=dateparse, names=cpflux_names,index_col='time_tag', header=0) # ZPGT100W\n\t\t\t\t\t\t\t\t\tproton_df = proton_df.append(proton_df_ind)\n\t\t\t\n\t\t\t\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\t\t\t\tshutil.move(f'{proton_name}', f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}')\n\t\t\t\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\t\t\t\tos.remove(proton_name)\n\t\t\t\t\t\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\n\t\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\t\tprint(e)\n\t\t\t\t\t\t\t\tprint(f'{date_event.month} does not have data.')\n\t\t\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['ZPGT10W'] <= 0.0].index, inplace=True)\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['ZPGT50W'] <= 0.0].index, inplace=True)\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['ZPGT100W'] <= 0.0].index, inplace=True)\n\t\t\n\t\t\n\t\t\n\t\t\t\t\t# ====== Legacy GOES data protons (GOES-10)\n\t\t\n\t\t\t\t\telif start.year < 2011:\n\t\t\t\t\t\tcpflux_names_legacy = ['time_tag','e1_flux_ic','e2_flux_ic','e3_flux_ic','p1_flux','p2_flux','p3_flux','p4_flux','p5_flux','p6_flux','p7_flux','a1_flux','a2_flux','a3_flux','a4_flux','a5_flux','a6_flux','p1_flux_c','p2_flux_c','p3_flux_c','p4_flux_c','p5_flux_c','p6_flux_c','p7_flux_c','p1_flux_ic','p2_flux_ic','p3_flux_ic','p4_flux_ic','p5_flux_ic','p6_flux_ic','p7_flux_ic']\n\t\t\n\t\t\t\t\t\tenergy_bin_list.append(['p3_flux_ic','>10 MeV', 'red'])\n\t\t\t\t\t\tenergy_bin_list.append(['p5_flux_ic','>50 MeV', 'blue'])\n\t\t\t\t\t\tenergy_bin_list.append(['p7_flux_ic','>100 MeV', 'lime'])\n\t\t\n\t\t\t\t\t\tfor date_event in date_in_year:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\tf_l_day = calendar.monthrange(int(date_event.year), int(date_event.month)) #\tf_l_day = calendar.monthrange(int(detection_year), int(month_event))\n\t\t\t\t\t\t\t\tevent_f_day = str(f'{date_event.year}{str(date_event.month).zfill(2)}01') # {str(f_l_day[0]).zfill(2)}\n\t\t\t\t\t\t\t\tevent_l_day = str(f'{date_event.year}{str(date_event.month).zfill(2)}{str(f_l_day[1]).zfill(2)}')\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\t\t\tdir_check = os.path.isdir(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}')\n\t\t\t\t\t\t\t\tif dir_check == False:\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t    os.makedirs(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}')\n\t\t\t\t\t\t\t\t\texcept OSError as e:\n\t\t\t\t\t\t\t\t\t    if e.errno != errno.EEXIST:\n\t\t\t\t\t\t\t\t\t        raise\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tproton_name = f'g{satellite_no}_eps_5m_{event_f_day}_{event_l_day}.csv' #g13_epead_cpflux_5m_20110101_20110131.csv\n\t\t\t\t\t\t\t\tproton_check = os.path.isfile(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}/{proton_name}')\n\t\t\t\t\t\n\t\t\t\t\t\t\t\tif proton_check == True:\n\t\t\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\t\t\tproton_df_ind = pd.read_csv(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}/{proton_name}', skiprows=452, date_parser=dateparse, names=cpflux_names_legacy,index_col='time_tag', header=0)\n\t\t\t\t\t\t\t\t\tproton_df = proton_df.append(proton_df_ind)\n\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\t\t\telif proton_check == False:\n\t\t\t\t\t\t\t\t\tproton_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_avg/{date_event.year}/{date_event.month}/goes{satellite_no}/csv/{proton_name}'\n\t\t\t\t\t\t\t\t\t# proton_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_avg/{event_date[:4]}/{event_date[4:6]}/goes{satellite_no}/csv/{proton_name}'\n\t\t\t\t\t\t\t\t\tproton_in = wget.download(proton_url)\n\t\t\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\t\t\tproton_df_ind = pd.read_csv(f'{proton_in}', skiprows=452, date_parser=dateparse, names=cpflux_names_legacy,index_col='time_tag', header=0) # ZPGT100W\n\t\t\t\t\t\t\t\t\tproton_df = proton_df.append(proton_df_ind)\n\t\t\t\n\t\t\t\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\t\t\t\tshutil.move(f'{proton_name}', f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{date_event.year}')\n\t\t\t\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\t\t\t\tos.remove(proton_name)\n\t\t\t\t\t\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\n\t\t\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t\t\tprint(e)\n\t\t\t\t\t\t\t\tprint(f'{date_event.month} does not have data.')\n\t\t\t\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['p3_flux_ic'] <= 0.0].index, inplace=True)\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['p5_flux_ic'] <= 0.0].index, inplace=True)\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['p7_flux_ic'] <= 0.0].index, inplace=True)\n\t\t\n\t\t\n\t\t\n\t\t\t\telif start_date[4:6] == end_date[4:6]: # if the month of the start day is the same as the month of the end date\n\t\t\t\t\tif start.year >= 2011:\n\t\t\t\t\t\tcpflux_names = ['time_tag','ZPGT1E_QUAL_FLAG', 'ZPGT1E', 'ZPGT5E_QUAL_FLAG', 'ZPGT5E', 'ZPGT10E_QUAL_FLAG', 'ZPGT10E', 'ZPGT30E_QUAL_FLAG', 'ZPGT30E', 'ZPGT50E_QUAL_FLAG', 'ZPGT50E', 'ZPGT60E_QUAL_FLAG', 'ZPGT60E', 'ZPGT100E_QUAL_FLAG', 'ZPGT100E', 'ZPGT1W_QUAL_FLAG', 'ZPGT1W', 'ZPGT5W_QUAL_FLAG', 'ZPGT5W', 'ZPGT10W_QUAL_FLAG', 'ZPGT10W', 'ZPGT30W_QUAL_FLAG', 'ZPGT30W', 'ZPGT50W_QUAL_FLAG', 'ZPGT50W', 'ZPGT60W_QUAL_FLAG', 'ZPGT60W', 'ZPGT100W_QUAL_FLAG', 'ZPGT100W', 'ZPEQ5E_QUAL_FLAG', 'ZPEQ5E', 'ZPEQ15E_QUAL_FLAG', 'ZPEQ15E', 'ZPEQ30E_QUAL_FLAG', 'ZPEQ30E', 'ZPEQ50E_QUAL_FLAG', 'ZPEQ50E', 'ZPEQ60E_QUAL_FLAG', 'ZPEQ60E', 'ZPEQ100E_QUAL_FLAG', 'ZPEQ100E', 'ZPEQ5W_QUAL_FLAG', 'ZPEQ5W', 'ZPEQ15W_QUAL_FLAG', 'ZPEQ15W', 'ZPEQ30W_QUAL_FLAG', 'ZPEQ30W', 'ZPEQ50W_QUAL_FLAG', 'ZPEQ50W', 'ZPEQ60W_QUAL_FLAG', 'ZPEQ60W', 'ZPEQ100W_QUAL_FLAG', 'ZPEQ100W']\n\t\t\t\t\t\tenergy_bin_list.append(['ZPGT10W','>10 MeV', 'red'])\n\t\t\t\t\t\tenergy_bin_list.append(['ZPGT50W','>50 MeV', 'blue'])\n\t\t\t\t\t\tenergy_bin_list.append(['ZPGT100W','>100 MeV', 'lime'])\n\t\t\n\t\t\t\t\t\tf_l_day = calendar.monthrange(int(f'{start_year}'), int(f'{start_month}'))\n\t\t\t\t\t\tevent_f_day = str(f'{start_year}{str(start_month).zfill(2)}01') # {str(f_l_day[0]).zfill(2)}\n\t\t\t\t\t\tevent_l_day = str(f'{start_year}{str(start_month).zfill(2)}{str(f_l_day[1]).zfill(2)}')\n\t\t\t\t\t\tdir_check = os.path.isdir(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{start_year}')\n\t\t\t\t\t\tif dir_check == False:\n\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t    os.makedirs(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{start_year}')\n\t\t\t\t\t\t\texcept OSError as e:\n\t\t\t\t\t\t\t    if e.errno != errno.EEXIST:\n\t\t\t\t\t\t\t        raise\n\t\t\n\t\t\t\t\t\tproton_name = f'g{satellite_no}_epead_cpflux_5m_{event_f_day}_{event_l_day}.csv' #g13_epead_cpflux_5m_20110101_20110131.csv\n\t\t\t\t\t\tproton_check = os.path.isfile(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{start_year}/{proton_name}')\n\t\t\t\t\n\t\t\t\t\t\tif proton_check == True:\n\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\tproton_df = pd.read_csv(f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{start_year}/{proton_name}', skiprows=718, date_parser=dateparse, names=cpflux_names,index_col='time_tag', header=0)\n\t\t\t\t\n\t\t\t\t\n\t\t\t\t\t\telif proton_check == False:\n\t\t\t\t\t\t\tproton_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_avg/{start_year}/{start_month}/goes{satellite_no}/csv/{proton_name}'\n\t\t\t\t\t\t\t# proton_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_avg/{event_date[:4]}/{event_date[4:6]}/goes{satellite_no}/csv/{proton_name}'\n\t\t\t\t\t\t\tproton_in = wget.download(proton_url)\n\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\tproton_df = pd.read_csv(f'{proton_in}', skiprows=718, date_parser=dateparse, names=cpflux_names,index_col='time_tag', header=0) # ZPGT100W\n\t\t\t\t\n\t\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\t\tshutil.move(f'{proton_name}', f'{data_directory}/GOES_Detection/GOES_{satellite_no}/{start_year}')\n\t\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\t\tos.remove(proton_name)\n\t\t\t\t\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['ZPGT10W'] <= 0.0].index, inplace=True)\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['ZPGT50W'] <= 0.0].index, inplace=True)\n\t\t\t\t\t\tproton_df.drop(proton_df[proton_df['ZPGT100W'] <= 0.0].index, inplace=True)\n\n\t\t\t# ======= proton event detection\n\t\t\t# ======= added for event options\n\t\t\tproton_channel = 'ZPGT100W' # t3_freq = 120\n\t\t\tproton_event_option = 'smooth' # 'standard' or 'smooth'\n\t\t\n\t\t\tif proton_event_option == 'standard':\n\t\t\t\tproton_threshold = pow(10,-1.3)# pow(10,-1.25)\n\t\t\t\tproton_data_event = pd.DataFrame([])\n\t\t\t\tproton_concat_event = proton_df[[proton_channel]]\n\t\t\t\tproton_data_event = proton_data_event.append(proton_concat_event)\n\t\t\t\n\t\t\t\tproton_event_df = pd.DataFrame([])\n\t\t\t\tproton_list_temp = []\n\t\t\t\tproton_list_event = []\n\t\t\t\tproton_counter = 0\n\t\t\n\t\t\n\t\t\t# ======== (BEGIN) Interpolating and smoothing data\n\t\t\telif proton_event_option == 'smooth':\n\t\t\t\tproton_threshold = pow(10,-1.26) # pow(10,-1.45) when Butterworth filter was at (1 or 2, 0.08)\n\t\t\t\tbutter_order = 1\n\t\t\t\tbutter_filter = f'butter{butter_order}'\n\t\t\t\tproton_smooth_df = pd.DataFrame(proton_df[f'{proton_channel}']) # pd.DataFrame(proton_df[f'{proton_channel}'].loc[event_obj_start:event_obj_end])\n\t\t\n\t\t\t\tproton_smooth_df_rs_1m = proton_smooth_df.resample('min')\n\t\t\t\tproton_smooth_df_rs_1s = proton_smooth_df.resample('S')\n\t\t\t\n\t\t\t\tproton_smooth_df_interp_1m = proton_smooth_df_rs_1m.interpolate(method='cubic')\n\t\t\t\tproton_smooth_df_interp_1s = proton_smooth_df_rs_1s.interpolate(method='cubic')\n\t\t\t\n\t\t\t\torder_list = [1,2,3,4,5,6,7,8,9,10]\n\t\t\t\n\t\t\t\tfor order in order_list:\n\t\t\t\t\tb, a = signal.butter(order, 0.4) # order of the filter (increases in integer values), cut off frequency # 0.08\n\t\t\t\t\n\t\t\t\t\ty1 = signal.filtfilt(b, a, proton_smooth_df[f'{proton_channel}'])\n\t\t\t\t\tproton_smooth_df[f'butter{order}'] = y1\n\t\t\n\t\t\t\tproton_smooth_df[proton_smooth_df[f'{butter_filter}'] <= 0.024] = 0.024\n\t\t\n\t\t\t\tproton_data_event = pd.DataFrame([])\n\t\t\t\tproton_concat_event = proton_smooth_df[[f'{butter_filter}']] # [[proton_channel]]\n\t\t\t\tproton_data_event = proton_data_event.append(proton_concat_event)\n\t\t\t\t\t\n\t\t\t\tproton_event_df = pd.DataFrame([])\n\t\t\t\tproton_list_temp = []\n\t\t\t\tproton_list_event = []\n\t\t\t\tproton_counter = 0\n\t\t\n\t\t\t# ======== (END) Interpolating and smoothing data\n\t\t\n\t\t\tmin_length_event = 500 # changed thresholds (03/06/2018): 1000 # 60\n\t\t\tmin_t_between_pts = 60 # changed thresholds (03/06/2018): 60 # 40\n\t\t\n\t\t\tfor i in proton_data_event[proton_data_event.values > proton_threshold].index: # for i in rb_data[rb_data.values > 300].index: # one level is 1 minute\n\t\t\t\tif len(proton_list_temp) == 0:\n\t\t\t\t\tproton_list_temp.append(i)\n\t\t\n\t\t\t\telif len(proton_list_temp) >= 1:\n\t\t\t\t\t# time between points\n\t\t\t\t\tif (i - proton_list_temp[-1]) <= datetime.timedelta(minutes=min_t_between_pts): # originally 5 minutes # also had at 30 minutes, but increasing to 40\n\t\t\t\t\t\tproton_list_temp.append(i)\n\t\t\n\t\t\t\t\telif (i - proton_list_temp[-1]) > datetime.timedelta(minutes=min_t_between_pts): # originally 5 minutes # time between first interval of time event to the second\n\t\t\t\t\t\tif (proton_list_temp[-1] - proton_list_temp[0]) >= datetime.timedelta(minutes=min_length_event): # length of event\n\t\t\t\t\t\t\tproton_list_event.append(proton_list_temp)\n\t\t\t\t\t\t\tproton_list_temp = []\n\t\t\t\t\t\t\tproton_list_temp.append(i)\n\t\t\n\t\t\t\t\t\telif (proton_list_temp[-1] - proton_list_temp[0]) < datetime.timedelta(minutes=min_length_event): # if the time difference is less than 30 minutes, then create a new event\n\t\t\t\t\t\t\tproton_list_temp = []\n\t\t\t\t\t\t\tproton_list_temp.append(i)\n\t\t\n\t\t\tif len(proton_list_temp) > 0:\n\t\t\t\tif (proton_list_temp[-1] - proton_list_temp[0]) >= datetime.timedelta(minutes=min_length_event):\n\t\t\t\t\tproton_list_event.append(proton_list_temp)\n\t\t\t\t\tproton_list_temp = []\n\t\t\t\t\tproton_list_temp.append(i)\n\t\t\t\telif (proton_list_temp[-1] - proton_list_temp[0]) < datetime.timedelta(minutes=min_length_event):\n\t\t\t\t\tproton_list_temp = []\n\t\t\t\t\tproton_list_temp.append(i)\n\t\t\t\tproton_list_temp = []\n\t\t\n\t\t\tprint(\"\\n\")\n\t\t\tproton_event_df = pd.DataFrame(columns=('start_time', 'end_time', 'proton_duration', 'proton_max_int'))\n\t\t\n\t\t\n\t\t\n\t\t\t# =========  Outlier list\n\t\t\tif len( proton_list_event ) == 1:\n\t\t\t\tproton_event_df.loc[0] = [proton_list_event[0][0], proton_list_event[0][-1], ((proton_list_event[0][-1] - proton_list_event[0][0]).total_seconds()/60), float(proton_data_event.loc[proton_list_event[0][0]:proton_list_event[0][-1]].max().values)] # days_hours_minutes(rb_list_event[i][-1] - rb_list_event[i][0])\n\t\t\n\t\t\telif len( proton_list_event ) > 1:\n\t\t\t\tfor i in range(len(proton_list_event)):\n\t\t\t\t\tproton_event_df.loc[i] = [proton_list_event[i][0], proton_list_event[i][-1], ((proton_list_event[i][-1] - proton_list_event[i][0]).total_seconds()/60), float(proton_data_event.loc[proton_list_event[i][0]:proton_list_event[i][-1]].max().values)] # days_hours_minutes(rb_list_event[i][-1] - rb_list_event[i][0])\n\t\t\n\t\t\tprint('='*40)\n\t\t\tprint(f\"Number of Proton Events ({start} - {end}): \", len(proton_list_event))\n\t\t\tprint(proton_event_df)\n\t\t\tprint('='*40)\n\n\t\t\n\t\t#=========== 2: Wind Type III Radio Burst\n\t\tif '2' in option_bin_set:\n\t\t\tprint(f'\\r                                                                                                    ', end='\\r')\n\t\t\tprint('\\rProcessing Wind Type III Radio Bursts...', end='\\r')\n\t\t\t# sys.stdout.write('/r Processing Wind Type III Radio Bursts')\n\t\t\t# sys.stdout.flush()\n\t\t\t# print(f'\\n{\"=\"*40}\\n{\"=\" + \"Wind Type III Radio Bursts\".center(38,\" \") + \"=\"}\\n{\"=\"*40}')\n\t\t\trb_data = pd.DataFrame([])\n\t\t\t\n\t\t\tfor date in daterange( start, end ):\n\t\t\t\ttry:\n\t\t\t\t\tevent_date = str(date).replace('-','')\n\t\t\n\t\t\t\t\t#print(event_date[0:6])\n\t\t\t\t\tradio_name = f'wi_h1_wav_{event_date}_v01.cdf'\n\t\t\t\t\tradio_check = os.path.isfile(f'{data_directory}/WIND/RAD1/{radio_name}')\n\t\t\n\t\t\t\t\tif radio_check == True:\n\t\t\t\t\t\tcdf = pycdf.CDF(f'{data_directory}/WIND/RAD1/{radio_name}')\n\t\t\n\t\t\t\t\telif radio_check == False:\n\t\t\t\t\t\tradio_url = f'https://cdaweb.gsfc.nasa.gov/pub/data/wind/waves/wav_h1/{event_date[:4]}/{radio_name}'\n\t\t\t\t\t\tradio_in = wget.download(radio_url)\t\n\t\t\t\t\t\tcdf = pycdf.CDF(radio_in) # cdf = pycdf.CDF('wi_h1_wav_20120307_v01.cdf')\n\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\tshutil.move(f'{radio_name}', f'{data_directory}/WIND/RAD1/')\n\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\tos.remove(radio_name)\n\t\t\n\t\t\t\t\t# radio_in = wget.download(url)\n\t\t\t\t\t\n\t\t\t\t\t# os.remove(radio_name)\n\t\t\t\t\t# print(f'\\nParsing Type III Data for {date}')\n\t\t\t\t\ttime_rb = []\n\t\t\t\t\tfor i in cdf['Epoch']:\n\t\t\t\t\t\ttime_rb.append(i)\n\t\t\t\t\tfreq_rb = []\n\t\t\t\t\tfor i in cdf['Frequency_RAD1']:\n\t\t\t\t\t\tfreq_rb.append(i)\n\t\t\t\t\trad1_rb = []\n\t\t\t\t\tfor i in cdf['E_VOLTAGE_RAD1']:\n\t\t\t\t\t\trad1_rb.append(i)\n\t\t\n\t\t\t\t\tdata_time = pd.DataFrame(time_rb)\n\t\t\t\t\tdata_time.columns = ['date_time']\n\t\t\n\t\t\t\t\tdata_freq = pd.DataFrame(freq_rb)\n\t\t\t\t\tdata_freq.columns = ['freq']\n\t\t\n\t\t\t\t\tdata_rad1 = pd.DataFrame(rad1_rb)\n\t\t\t\t\tdata_rad1.columns = data_freq['freq']\n\t\t\n\t\t\t\t\trb_concat = pd.concat([data_time, data_rad1], axis=1)\n\t\t\t\t\trb_concat.set_index(['date_time'], inplace=True)\n\t\t\t\t\trb_data = rb_data.append(rb_concat)\n\t\t\n\t\t\n\t\t\n\t\t\t\t\t'''\n\t\t\t\t\tradio_name = f'wi_h1_wav_{event_date}_v01.cdf'\n\t\t\t\t\turl = f'https://cdaweb.gsfc.nasa.gov/pub/data/wind/waves/wav_h1/{event_date[:4]}/{radio_name}'\n\t\t\t\t\tradio_in = wget.download(url)\n\t\t\t\t\t\n\t\t\t\t\tcdf = pycdf.CDF(radio_in) # cdf = pycdf.CDF('wi_h1_wav_20120307_v01.cdf')\n\t\t\t\t\tos.remove(radio_name)\n\t\t\t\t\n\t\t\t\t\t# print(f'\\nParsing Type III Data for {date}')\n\t\t\t\t\t\n\t\t\t\t\ttime_rb = []\n\t\t\t\t\tfor i in cdf['Epoch']:\n\t\t\t\t\t\ttime_rb.append(i)\n\t\t\t\t\t\n\t\t\t\t\tfreq_rb = []\n\t\t\t\t\tfor i in cdf['Frequency_RAD1']:\n\t\t\t\t\t\tfreq_rb.append(i)\n\t\t\t\t\t\n\t\t\t\t\trad1_rb = []\n\t\t\t\t\tfor i in cdf['E_VOLTAGE_RAD1']:\n\t\t\t\t\t\trad1_rb.append(i)\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tdata_time = pd.DataFrame(time_rb)\n\t\t\t\t\tdata_time.columns = ['date_time']\n\t\t\t\t\t\n\t\t\t\t\tdata_freq = pd.DataFrame(freq_rb)\n\t\t\t\t\tdata_freq.columns = ['freq']\n\t\t\t\t\t\n\t\t\t\t\tdata_rad1 = pd.DataFrame(rad1_rb)\n\t\t\t\t\tdata_rad1.columns = data_freq['freq']\n\t\t\t\t\t\n\t\t\t\t\trb_concat = pd.concat([data_time, data_rad1], axis=1)\n\t\t\t\t\trb_concat.set_index(['date_time'], inplace=True)\n\t\t\t\t\n\t\t\t\t\trb_data = rb_data.append(rb_concat)\n\t\t\t\t\t'''\n\t\t\t\texcept:\n\t\t\t\t\tprint(f'\\nMISSING DATA FOR: {date}\\n')\n\t\t\t\t\tcontinue\n\t\t\t\n\t\t\trb_data['avg'] = rb_data.mean(axis=1, numeric_only=True)\n\n\n\n\n\t\t\t\n\t\t\n\t\t\n\t\t\t# ============ EXPERIMENTAL FITTING (DO NOT USE)\n\t\t\t'''\n\t\t\tfit_choice = input('\\nType \"1 - 3\" if you would like to fit: (currently in work)')\n\t\t\n\t\t\n\t\t\tif fit_choice == '1': # curve fitting\n\t\t\t\trb_data['d_int'] = mdates.date2num(rb_data.index.to_pydatetime())\n\t\t\n\t\t\t\tz4 = np.polyfit(rb_data['d_int'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], rb_data['avg'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], 3)\n\t\t\t\tp4 = np.poly1d(z4)\n\t\t\n\t\t\t\txx = np.linspace(rb_data['d_int'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].min(), rb_data['d_int'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].max(), 20)\n\t\t\t\tdd = mdates.num2date(xx)\n\t\t\n\t\t\t\tplt.plot(dd, p4(xx), 'o')\n\t\t\t\tplt.show()\n\t\t\n\t\t\n\t\t\n\t\t\tif fit_choice == '2': # gaussian fitting\n\t\t\t\tfrom astropy.modeling import models, fitting\n\t\t\n\t\t\t\trb_data['d_int'] = mdates.date2num(rb_data.index.to_pydatetime())\n\t\t\t\trs_time = rb_data['d_int'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']\n\t\t\n\t\t\n\t\t\t\tn_obs = len(rb_data.loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].index)\n\t\t\t\tdata = rb_data['avg'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']\n\t\t\t\t# X = np.arange(n_obs)\n\t\t\t\tX = rs_time\n\t\t\n\t\t\n\t\t\t\tx = np.sum(X * data)/np.sum(data)\n\t\t\t\twidth = np.sqrt(np.abs(np.sum((X-x)**2*data)/np.sum(data)))\n\t\t\n\t\t\t\tmax_data = data.max()\n\t\t\n\t\t\t\tgauss_fit = lambda t : max_data*np.exp(-(t-x)**2/(2*width**2))\n\t\t\n\t\t\t\tplt.plot(gauss_fit(X), '-', color='blue')\n\t\t\t\tplt.plot(rs_time, data, 'o', color='red')\n\t\t\t\t\n\t\t\n\t\t\n\t\t\t\tplt.show()\n\t\t\n\t\t\tif fit_choice == '3': # skewed gaussian fitting\n\t\t\t\tfrom lmfit.models import SkewedGaussianModel\n\t\t\t\n\t\t\t\tfrom astropy.modeling import models, fitting\n\t\t\t\n\t\t\t\trb_data['d_int'] = mdates.date2num(rb_data.index.to_pydatetime())\n\t\t\t\trs_time = rb_data['d_int'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']\n\t\t\t\tn_obs = len(rb_data.loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].index)\n\t\t\t\tdata = rb_data['avg'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']\n\t\t\n\t\t\t\tdata_max_index = mdates.date2num(rb_data['avg'].idxmax().to_pydatetime())\n\t\t\t\t# X = np.arange(n_obs)\n\t\t\t\tX = rs_time\n\t\t\t\tx = np.sum(X * data)/np.sum(data)\n\t\t\n\t\t\t\twidth = np.sqrt(np.abs(np.sum((X-x)**2*data)/np.sum(data)))\n\t\t\t\tmax_data = data.max()\n\t\t\t\tgauss_fit = lambda t : max_data*np.exp(-(t-x)**2/(2*width**2))\n\t\t\t\n\t\t\t\tskg_model = SkewedGaussianModel()\n\t\t\t\tskg_params = skg_model.make_params(amplitude = 147, center = 734569.0245138889, sigma = 1, gamma = 0) # sigma, gamma = 1, 0\n\t\t\t\t# skg_params = skg_model.make_params(amplitude = data.max(), center = data_max_index, sigma = 1, gamma = 0) # sigma, gamma = 1, 0\n\t\t\t\t\n\t\t\n\t\t\t\tskg_result = skg_model.fit(data, skg_params, x=rs_time)\n\t\t\t\tprint(skg_result.fit_report())\n\t\t\n\t\t\t\tplt.axvline(734569.0245138889)\n\t\t\t\tplt.plot(gauss_fit(X), '-', color='blue')\n\t\t\t\tplt.plot(rs_time, data, 'o', color='red')\n\t\t\t\tplt.plot(rs_time, skg_result.best_fit)\n\t\t\t\t\n\t\t\t\tplt.show()\n\t\t\n\t\t\tif fit_choice == '4': # inverse gaussian fit\n\t\t\t\tfrom scipy.stats import invgauss\n\t\t\n\t\t\t\trb_data['d_int'] = mdates.date2num(rb_data.index.to_pydatetime())\n\t\t\t\trs_time = rb_data['d_int'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']\n\t\t\t\tn_obs = len(rb_data.loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].index)\n\t\t\t\tdata = rb_data['avg'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']\n\t\t\t\tdata_max_index = mdates.date2num(rb_data['avg'].idxmax().to_pydatetime())\n\t\t\t\t# X = np.arange(n_obs)\n\t\t\t\tX = rs_time\n\t\t\t\tx = np.sum(X * data)/np.sum(data)\n\t\t\t\twidth = np.sqrt(np.abs(np.sum((X-x)**2*data)/np.sum(data)))\n\t\t\t\tmax_data = data.max()\n\t\t\t\tgauss_fit = lambda t : max_data*np.exp(-(t-x)**2/(2*width**2))\n\t\t\t\tskg_model = SkewedGaussianModel()\n\t\t\t\tskg_params = skg_model.make_params(amplitude = 147, center = 734569.0245138889, sigma = 1, gamma = 0) # sigma, gamma = 1, 0\n\t\t\t\t# skg_params = skg_model.make_params(amplitude = data.max(), center = data_max_index, sigma = 1, gamma = 0) # sigma, gamma = 1, 0\n\t\t\t\tskg_result = skg_model.fit(data, skg_params, x=rs_time)\n\t\t\t\tprint(skg_result.fit_report())\n\t\t\n\t\t\t\tplt.axvline(734569.0245138889)\n\t\t\n\t\t\t\tinv_gauss = invgauss.fit(data)\n\t\t\n\t\t\t\tplt.plot(gauss_fit(X), '-', color='blue')\n\t\t\t\tplt.plot(rs_time, data, 'o', color='red')\n\t\t\t\tplt.plot(rs_time, skg_result.best_fit)\n\t\t\t\tplt.plot(rs_time, inv_gauss, color = 'green')\n\t\t\n\t\t\t\tplt.show()\n\t\t\n\t\t\n\t\t''' # end experimental fitting\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t#=========== 3: Neutron Monitors\n\t\tif '3' in option_bin_set:\n\t\t\tprint(f'\\n{\"=\"*40}\\n{\"=\" + \"Neutron Monitors\".center(38,\" \") + \"=\"}\\n{\"=\"*40}')\n\t\t\t\n\t\t\tlist_nm = ['AATB','APTY','ARNM','ATHN','BKSN','CALG','CALM','DOMB',\n\t\t\t\t\t\t'DOMC','DRBS','ESOI','FSMT','HRMS','INVK','IRK2','IRK3',\n\t\t\t\t\t\t'IRKT','JBGO','JUNG','JUNG1','KERG','KIEL','KIEL2','LMKS',\n\t\t\t\t\t\t'MCRL','MGDN','MOSC','MRNY','MWSN','MXCO','NAIN','NANM','NEU3',\n\t\t\t\t\t\t'NEWK','NRLK','NVBK','OULU','PSNM','PTFM','PWNK','ROME','SANB','SNAE'\n\t\t\t\t\t\t,'SOPB','SOPO','TERA','THUL','TIBT','TXBY','YKTK']\n\t\t\t\n\t\t\tnum_station = int(input('How many stations to parse: '))\n\t\t\tprint(f'You are parsing {num_station} station(s)')\n\t\t\t\n\t\t\tstation_multi = []\n\t\t\tfor i in range(num_station):\n\t\t\t\tstation = input('Enter station names: ').upper()\n\t\t\t\tif station == '':\n\t\t\t\t\tstation = 'OULU'\n\t\t\t\t\tstation_multi.append(station)\n\t\t\t\telif station == 'RANDOM':\n\t\t\t\t\tstation = random.choice(list_nm)\n\t\t\t\t\tstation_multi.append(station)\n\t\t\t\telse:\n\t\t\t\t\tstation_multi.append(station)\n\t\t\t\n\t\t\tprint(f'Parsing the {station_multi} stations')\n\t\t\t\n\t\t\t\n\t\t\t#sorter_list = ['PSNM', 'TIBT', 'ESOI', 'ATHN', 'MXCO', 'ARNM', 'NANM', 'PTFM', 'CALM', 'AATB', 'ROME', 'BKSN', 'HRMS', 'JUNG', 'JUNG1', 'LMKS', 'IRK2', 'IRK3', 'IRKT', 'DRBS', 'NVBK', 'MCRL', 'MOSC', 'NEWK', 'KIEL', 'KIEL2', 'MGDN', 'YKTK', 'KERG', 'CALG', 'OULU', 'SANB', 'SNAE', 'APTY', 'NRLK', 'TXBY', 'FSMT', 'INVK', 'JBGO', 'NAIN', 'PWNK', 'THUL', 'MWSN', 'NEU3', 'SOPB', 'SOPO', 'MRNY', 'DOMB', 'DOMC', 'TERA']\n\t\t\tsorter = {'PSNM':0, 'TIBT':1, 'ESOI':2, 'ATHN':3, 'MXCO':4, 'ARNM':5, 'NANM':6, 'PTFM':7, 'CALM':8, 'AATB':9, 'ROME':10, 'BKSN':11, 'HRMS':12, 'JUNG':13, 'JUNG1':14, 'LMKS':15, 'IRK2':16, 'IRK3':17, 'IRKT':18, 'DRBS':19, 'NVBK':20, 'MCRL':21, 'MOSC':22, 'NEWK':23, 'KIEL':24, 'KIEL2':25, 'MGDN':26, 'YKTK':27, 'KERG':28, 'CALG':29, 'OULU':30, 'SANB':31, 'SNAE':32, 'APTY':33, 'NRLK':34, 'TXBY':35, 'FSMT':36, 'INVK':37, 'JBGO':38, 'NAIN':39, 'PWNK':40, 'THUL':41, 'MWSN':42, 'NEU3':43, 'SOPB':44, 'SOPO':45, 'MRNY':46, 'DOMB':47, 'DOMC':48, 'TERA':49}\n\t\t\tsorted_lambda = sorted(sorter.items(), key=lambda x: x[1])\n\t\t\t\n\t\t\t\n\t\t\tsorted_nm_list = []\n\t\t\tfor i in [i[0] for i in sorted_lambda]:\n\t\t\t\tif i in station_multi:\n\t\t\t\t\tsorted_nm_list.append(i)\n\t\t\t\n\t\t\t\n\t\t\tstation_str = ''\n\t\t\tfor i in sorted_nm_list:\n\t\t\t\tstation_str += f'&stations[]={i}'\n\t\t\t\n\t\t\t\n\t\t\turl = f'http://www.nmdb.eu/nest/draw_graph.php?formchk=1{station_str}&tabchoice=revori&dtype=corr_for_efficiency&tresolution=0&yunits=0&date_choice=bydate&start_day={start_day}&start_month={start_month}&start_year={start_year}&start_hour={start_hour}&start_min=00&end_day={end_day}&end_month={end_month}&end_year={end_year}&end_hour={end_hour}&end_min=00&output=ascii'\n\t\t\t# start minute set to '00', fix this eventually using {start_minute} and {end_minute}\n\t\t\tnm_data = pd.DataFrame([])\n\t\t\t\n\t\t\tname_list = ['datetime'] + [ str(i) for i in sorted_nm_list]\n\t\t\t\n\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S')\n\t\t\tnm_data = pd.read_csv(url,sep=';|\\n|\\b', skiprows=133, skipfooter=3, engine='python', index_col='datetime', date_parser=dateparse, names=name_list, na_values=['   null']) #, , parse_dates=['datetime'], date_parser=dateparse\n\t\t\t\n\t\t\tnm_counter = []\n\t\t\tfor item in sorted_nm_list:\n\t\t\t\tif nm_data[f'{item}'].isnull().values.any() == True:\n\t\t\t\t\tnm_counter.append(1)\n\t\t\t\telse:\n\t\t\t\t\tnm_counter.append(0)\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t#=========== 4: Solar Wind (ACE, Wind)\n\t\tif '4' in option_bin_set:\n\t\t\tprint(f'\\r                                                                                                    ', end='\\r')\n\t\t\tprint(f'\\rProcessing ACE/Wind Solar Wind Speed...', end='\\r')\n\t\t\t# print(f'\\n{\"=\"*40}\\n{\"=\" + \"ACE/Wind Solar Wind Speed\".center(38,\" \") + \"=\"}\\n{\"=\"*40}')\n\t\t\t\n\t\t\tace_data = pd.DataFrame([])\n\t\t\twind_data = pd.DataFrame([])\n\t\t\t\n\t\t\t\n\t\t\tfor date in daterange( start, end ):\n\t\t\t\ttry:\n\t\t\t\t\tevent_date = str(date).replace('-','')\n\t\t\t\n\t\t\t\t\t#====ACE\n\t\t\t\t\tswind_ace_name = f'ac_h0_swe_{event_date}_v10.cdf'\n\t\t\t\t\tswind_ace_check = os.path.isfile(f'{data_directory}/ACE/SWE_H0/{swind_ace_name}')\n\t\t\n\t\t\t\t\tif swind_ace_check == True:\n\t\t\t\t\t\tswind_ace_cdf = pycdf.CDF(f'{data_directory}/ACE/SWE_H0/{swind_ace_name}')\n\t\t\n\t\t\t\t\telif swind_ace_check == False:\n\t\t\t\t\t\tswind_ace_url = f'https://cdaweb.gsfc.nasa.gov/pub/data/ace/swepam/level_2_cdaweb/swe_h0/{event_date[:4]}/{swind_ace_name}'\n\t\t\t\t\t\tswind_ace_in = wget.download(swind_ace_url)\n\t\t\t\t\t\tswind_ace_cdf = pycdf.CDF(swind_ace_in) # cdf = pycdf.CDF('wi_h1_wav_20120307_v01.cdf')\n\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\tshutil.move(f'{swind_ace_name}', f'{data_directory}/ACE/SWE_H0/')\n\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\tos.remove(swind_ace_name)\n\t\t\n\t\t\t\t\t# os.remove(swind_ace_name)\n\t\t\t\t\t\t\n\t\t\t\t\ttime_ace_swind = []\n\t\t\t\t\tfor i in swind_ace_cdf['Epoch']:\n\t\t\t\t\t\ttime_ace_swind.append(i)\n\t\t\t\t\t\n\t\t\t\t\tbulk_ace_vel = []\n\t\t\t\t\tfor i in swind_ace_cdf['Vp']:\n\t\t\t\t\t\tbulk_ace_vel.append(i)\n\t\t\t\n\t\t\t\t\tdata_ace_time = pd.DataFrame(time_ace_swind)\n\t\t\t\t\tdata_ace_time.columns = ['date_time']\n\t\t\t\t\t\n\t\t\t\t\tace_vel = pd.DataFrame(bulk_ace_vel)\n\t\t\t\t\tace_vel.columns = ['ace_bulk_vel']\n\t\t\t\t\t\n\t\t\t\t\tace_concat = pd.concat([data_ace_time, ace_vel], axis=1)\n\t\t\t\t\tace_concat.set_index(['date_time'], inplace=True)\n\t\t\t\t\n\t\t\t\t\tace_data = ace_data.append(ace_concat)\n\t\t\t\n\t\t\t\t\t#====WIND\n\t\t\t\t\tfor i in range(10):\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tswind_wind_name = f'wi_k0_swe_{event_date}_v0{i}.cdf'\n\t\t\t\t\t\t\tswind_wind_check = os.path.isfile(f'{data_directory}/WIND/K0/{swind_wind_name}')\n\t\t\n\t\t\t\t\t\t\tif swind_wind_check == True:\n\t\t\t\t\t\t\t\tswind_wind_cdf = pycdf.CDF(f'{data_directory}/WIND/K0/{swind_wind_name}')\n\t\t\n\t\t\t\t\t\t\telif swind_wind_check == False:\n\t\t\t\t\t\t\t\tswind_wind_url = f'https://cdaweb.gsfc.nasa.gov/pub/data/wind/swe/swe_k0/{event_date[:4]}/{swind_wind_name}'\n\t\t\t\t\t\t\t\tswind_wind_in = wget.download(swind_wind_url)\n\t\t\t\t\t\t\t\tswind_wind_cdf = pycdf.CDF(swind_wind_in)\n\t\t\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\t\t\tshutil.move(f'{swind_wind_name}', f'{data_directory}/WIND/K0/')\n\t\t\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\t\t\tos.remove(swind_wind_name)\n\t\t\n\t\t\t\t\t\texcept error.HTTPError as err:\n\t\t\t\t\t\t\t#print(f'\\nVERSION ERROR: The version v0{i} for WIND data does not exist, attempting v0{i+1}')\n\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tbreak\n\t\t\t\n\t\t\t\n\t\t\t\t\t# swind_wind_cdf = pycdf.CDF(swind_wind_name) # cdf = pycdf.CDF('wi_h1_wav_20120307_v01.cdf')\n\t\t\t\t\t# os.remove(swind_wind_name)\n\t\t\t\t\t\t\n\t\t\t\t\ttime_wind_swind = []\n\t\t\t\t\tfor i in swind_wind_cdf['Epoch']:\n\t\t\t\t\t\ttime_wind_swind.append(i)\n\t\t\t\t\t\n\t\t\t\t\tbulk_wind_vel = []\n\t\t\t\t\tfor i in swind_wind_cdf['V_GSE_p']:\n\t\t\t\t\t\tbulk_wind_vel.append(i)\n\t\t\t\n\t\t\t\t\tdata_wind_time = pd.DataFrame(time_wind_swind)\n\t\t\t\t\tdata_wind_time.columns = ['date_time']\n\t\t\t\t\t\n\t\t\t\t\twind_vel = pd.DataFrame(bulk_wind_vel)\n\t\t\t\t\twind_vel.columns = ['wind_bulk_vel', 'longitude', 'latitude']\n\t\t\t\t\t\n\t\t\t\t\twind_concat = pd.concat([data_wind_time, wind_vel], axis=1)\n\t\t\t\t\twind_concat.set_index(['date_time'], inplace=True)\n\t\t\t\t\n\t\t\t\t\twind_data = wind_data.append(wind_concat)\n\t\t\t\n\t\t\t\texcept:\n\t\t\t\t\tprint(f'\\nMissing data for {date}')\n\t\t\t\t\tcontinue\n\t\t\t\n\t\t\t\n\t\t\t\n\t\t\tace_data.loc[ace_data['ace_bulk_vel'] <= 0.0] = np.nan #6.5 MeV\n\t\t\twind_data.loc[wind_data['wind_bulk_vel'] <= 0.0] = np.nan #6.5 MeV\n\t\t\n\t\t\n\t\t#=========== 5: GOES-15 Xray Flux\n\t\n\t\tif '5' in option_bin_set:\n\t\t\tif event_option == 'yes':\n\t\t\t\tsatellite_no_xray = str(event_list['xray_sat'])\n\t\t\tif event_option != 'yes':\n\t\t\t\tsatellite_no_xray = input('Specify which GOES Satellite for Xray Flux (13 or 15): ')\n\t\t\t\tif satellite_no_xray != '13':\n\t\t\t\t\tif satellite_no_xray != '15':\n\t\t\t\t\t\tprint('SATELLITE ERROR: Must specify either 13 or 15.')\n\t\t\t\t\t\tsys.exit(0)\n\t\t\tprint(f'\\r                                                                                                    ', end='\\r')\n\t\t\tprint(f'\\rProcessing GOES-{satellite_no_xray} Xray Flux...', end='\\r')\n\t\t\t\n\t\t\txray_df = pd.DataFrame([])\n\t\t\t\n\t\t\tfor date in daterange( start, end ):\n\n\t\t\t\ttry:\n\t\t\t\t\tevent_date = str(date).replace('-','')\n\t\t\n\t\t\t\t\txray_name = f'g{satellite_no_xray}_xrs_2s_{event_date}_{event_date}.csv' #g15_xrs_2s_20120307_20120307.csv\n\t\t\t\t\txray_check = os.path.isfile(f'{data_directory}/GOES/GOES_{satellite_no_xray}/XRflux/{xray_name}')\n\t\t\t\t\txray_name_list = ['time_tag','A_QUAL_FLAG','A_COUNT','A_FLUX','B_QUAL_FLAG','B_COUNT','B_FLUX']\t\t\n\t\t\n\t\t\t\t\tif xray_check == True:\n\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\txray_df_ind = pd.read_csv(f'{data_directory}/GOES/GOES_{satellite_no_xray}/XRflux/{xray_name}', skiprows=140, names=xray_name_list, date_parser=dateparse,index_col='time_tag', header=0)\n\t\t\t\t\t\txray_df = xray_df.append(xray_df_ind)\n\t\t\n\t\t\n\t\t\t\t\telif xray_check == False:\n\t\t\t\t\t\txray_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_full/{event_date[:4]}/{event_date[4:6]}/goes{satellite_no_xray}/csv/{xray_name}'\n\t\t\t\t\t\txray_in = wget.download(xray_url)\n\t\t\t\n\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\txray_df_ind = pd.read_csv(f'{xray_in}', skiprows=140, names=xray_name_list, date_parser=dateparse,index_col='time_tag', header=0) # 138 for 20120307\n\t\t\t\t\t\txray_df = xray_df.append(xray_df_ind)\n\t\t\n\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\tshutil.move(f'{xray_name}', f'{data_directory}/GOES/GOES_{satellite_no_xray}/XRflux/')\n\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\tos.remove(xray_name)\n\t\t\n\t\t\n\t\t\t\t\n\t\t\t\texcept error.HTTPError as err:\n\t\t\t\t\tsatellite_no_xray_error = ['13', '15']\n\t\t\n\t\t\t\t\tfor i in satellite_no_xray_error:\n\t\t\t\t\t\tif i == satellite_no_xray:\n\t\t\t\t\t\t\tsatellite_no_xray_error.remove(f'{i}')\n\t\t\t\t\t\t\tsatellite_no_xray = satellite_no_xray_error[0]\n\t\t\t\t\t\t\tprint(f'GOES-{i} data does not exist for this date ({event_date}), using data from GOES-{satellite_no_xray}.')\n\t\t\n\t\t\t\t\t\t\txray_name_error = f'g{satellite_no_xray_error[0]}_xrs_2s_{event_date}_{event_date}.csv' #g15_xrs_2s_20120307_20120307.csv\n\t\t\t\t\t\t\txray_error_check = os.path.isfile(f'{data_directory}/GOES/GOES_{satellite_no_xray_error[0]}/XRflux/{xray_name_error}')\n\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tif xray_error_check == True:\n\t\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\t\txray_df_ind = pd.read_csv(f'{data_directory}/GOES/GOES_{satellite_no_xray_error[0]}/XRflux/{xray_name_error}', skiprows=140, names=xray_name_list, date_parser=dateparse,index_col='time_tag', header=0)\n\t\t\t\t\t\t\t\txray_df = xray_df.append(xray_df_ind)\n\t\t\n\t\t\t\t\t\t\telif xray_error_check == False:\n\t\t\t\t\t\t\t\txray_url = f'https://satdat.ngdc.noaa.gov/sem/goes/data/new_full/{event_date[:4]}/{event_date[4:6]}/goes{satellite_no_xray_error[0]}/csv/{xray_name_error}'\n\t\t\t\t\t\t\t\txray_in = wget.download(xray_url)\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\tdateparse = lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S.%f')\n\t\t\t\t\t\t\t\txray_df_ind = pd.read_csv(f'{xray_in}', skiprows=140, names=xray_name_list, date_parser=dateparse,index_col='time_tag', header=0) # 138 for 20120307\n\t\t\t\t\t\t\t\txray_df = xray_df.append(xray_df_ind)\n\t\t\t\t\n\t\t\t\t\t\t\t\tif save_option == 'yes':\n\t\t\t\t\t\t\t\t\tshutil.move(f'{xray_name_error}', f'{data_directory}/GOES/GOES_{satellite_no_xray_error[0]}/XRflux/')\n\t\t\t\t\t\t\t\telif save_option == 'no':\n\t\t\t\t\t\t\t\t\tos.remove(xray_name)\n\t\t\n\t\t\t\texcept:\n\t\t\t\t\tprint(f'\\nMissing data for {date}')\n\t\t\t\t\tcontinue\n\t\t\n\t\t\t\telse:\n\t\t\t\t\tcontinue\t\n\t\t\n\t\t\t\n\t\t\t# xray_df.loc[xray_df['A_FLUX'] <= 0.0] = np.nan #6.5 MeV\n\t\t\t# xray_df.loc[xray_df['B_FLUX'] <= 0.0] = np.nan #11.6 MeV\n\t\t\t\n\t\t\txray_df.drop(xray_df[xray_df['A_FLUX'] <= 0.0].index, inplace=True)\n\t\t\txray_df.drop(xray_df[xray_df['B_FLUX'] <= 0.0].index, inplace=True)\n\t\t\n\t\t\n\t\t\n\t\t''' Templates for new data\n\t\t#===========\n\t\tprint(f'\\n{\"=\"*40}\\nNew Dataset Name Goes Here\\n{\"=\"*40}')\n\t\t\n\t\t#===========\n\t\tprint(f'\\n{\"=\"*40}\\nNew Dataset Name Goes Here\\n{\"=\"*40}')\n\t\t'''\n\t\t\n\t\t#=========== Plotting Data\n\t\tsys.stdout.flush()\n\t\tprint(f'\\r                                                                                                    ', end='\\r')\n\t\tprint(f'\\rGenerating plot for {event_date}...')\n\t\tsys.stdout.flush()\n\t\n\t\t'''\n\t\t1 - GOES-15 Proton Flux\n\t\t2 - Wind Type III Radio Bursts\n\t\t3 - Neutron Monitor Counts\n\t\t4 - ACE/Wind Solar Wind Speed'\n\t\t'''\n\t\tlength_data = int(len(option_bin_set))\n\t\tlength_data_list = []\n\t\tfor i in range(length_data):\n\t\t\tlength_data_list.append(i)\n\t\t\n\t\t\n\t\tj = -1\n\t\tdef next_global():\n\t\t\tglobal j\n\t\t\tif (j < length_data+2):\n\t\t\t\tj += 1\n\t\t\t#print(length_data_list[j])\n\t\t\n\t\t#high_bin_proton = sorted(energy_bin_list[-1])[1]\n\t\t#low_bin_proton = sorted(energy_bin_list[0])[1]\n\t\t\n\t\t#high_bin_proton_str = sorted(energy_bin_list[-1])[0]\n\t\t#low_bin_proton_str = sorted(energy_bin_list[0])[0]\n\t\t\n\t\t\n\t\t\n\t\t\n\t\t\n\t\tdef applyPlotStyle():\n\t\t\taxes[length_data_list[j]].grid(True, linestyle='dotted')\n\t\t\taxes[length_data_list[j]].tick_params(axis='both', which='both', direction='in')\n\n\n\t\t\taxes[length_data_list[j]].minorticks_on()\n\t\t\taxes[length_data_list[j]].legend(loc='upper right', ncol=1,fontsize=10)# borderaxespad=0)# bbox_to_anchor=(1, 0.5)) # bbox_to_anchor=(1.02,1.0)\n\t\t\tif '1' in option_bin_set:\n\t\t\t\thigh_bin_proton = sorted(energy_bin_list)[-1][0]\n\t\t\t\tlow_bin_proton = sorted(energy_bin_list)[0][0]\n\t\t\n\t\t\t\thigh_bin_proton_str = sorted(energy_bin_list)[-1][1]\n\t\t\t\tlow_bin_proton_str = sorted(energy_bin_list)[0][1]\n\t\t\t\t# axes[length_data_list[j]].set_zorder(0)\n\t\t\t\t# axes[length_data_list[j]].axvline(proton_df[f'{low_bin_proton}'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].idxmax(), zorder=1) # (proton_df.P6W_UNCOR_FLUX.max()) # changed maximum flux to be within time interval specified\n\t\t\t# axes[length_data_list[j]].axvline(proton_df.idxmax().P6W_UNCOR_FLUX) # (proton_df.P6W_UNCOR_FLUX.max())\n\t\t\n\t\t\n\t\tif length_data > 1:\n\t\t\tf, axes = plt.subplots(nrows=length_data, ncols=1, sharex=True, figsize=(10,12)) # figsize=(10, 6))\n\t\t\n\t\tif length_data == 1:\n\t\t\tlength_data_list[0] = 0,0\n\t\t\tf, axes = plt.subplots(nrows=length_data, ncols=1, sharex=False, figsize=(10, 6), squeeze=False)\n\t\t\n\t\t\n\t\t#======dataset plotting\n\t\t\n\t\t'''\n\t\tif '1' in option_bin_set:\n\t\t\tnext_global()\n\t\t\tfor i in sorted(energy_bin_list):\n\t\t\t\taxes[length_data_list[j]].plot(proton_df[f'{i[0]}'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color=f'{i[2]}', label= f'{i[1]}', zorder=5)#, logy=True)\n\t\t\taxes[length_data_list[j]].set_yscale('log')\n\t\t\taxes[length_data_list[j]].set_ylabel(f'GOES-{satellite_no} Proton\\nFlux [pfu]', fontname=\"Arial\", fontsize = 12)\n\t\t\tapplyPlotStyle()\n\t\t'''\n\n\t\tif '1' in option_bin_set:\n\t\t\tnext_global()\n\t\t\tif goes_corrected_option == 'yes':\n\t\t\t\tfor i in energy_bin_list: # for i in sorted(energy_bin_list): # changed to unsorted because the sort queued off of 10 -> 100 -> 50 rather than 10 -> 50 -> 100\n\t\t\t\t\taxes[length_data_list[j]].plot(proton_df[f'{i[0]}'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], '.', mfc='none', color=f'{i[2]}', label= f'{i[1]}', zorder=5)#, logy=True)\n\t\t\t\t\n\t\t\t\t# axes[length_data_list[j]].plot(proton_smooth_df['ZPGT100W'].ewm(span=20).mean(), color='red', linewidth=1, label= 'EWM', zorder=5) # butter filter\n\t\t\n\t\t\n\t\t\t\tcolor_tree = iter(cm.rainbow(np.linspace(0,1,10)))\n\t\t\t\t'''\n\t\t\t\tfor order in range(10):\n\t\t\t\t\taxes[length_data_list[j]].plot(proton_smooth_df[f'butter{order+1}'], color=next(color_tree), linewidth=1, label= f'Butter-{order}', zorder=5) # butter filter\n\t\t\t\t'''\n\t\t\t\tif proton_event_option == 'smooth':\n\t\t\t\t\taxes[length_data_list[j]].plot(proton_smooth_df[f'{butter_filter}'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='purple', linewidth=2, label=f'Butter-{butter_order}', zorder=5)\n\t\t\t\t# axes[length_data_list[j]].plot(proton_smooth_df_interp_1m, color='red', linestyle='-.', label= '1m Interp.', zorder=5) # interpolated (no time reshape)\n\t\t\t\t# axes[length_data_list[j]].plot(proton_smooth_df_interp_1s, color='purple', linestyle=':', label= '1s Interp.', zorder=5) # interpolated (1 second)\n\t\t\n\t\t\t\taxes[length_data_list[j]].set_yscale('log')\n\t\t\t\t# axes[length_data_list[j]].set_ylim((10**(-3)), (10**3))\n\t\t\n\t\t\n\t\t\t\t\n\t\t\t\t'''\n\t\t\t\taxes[length_data_list[j]].set_yticks([10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3]) # 10**-3, \n\t\t\t\t'''\n\t\t\t\taxes[length_data_list[j]].set_ylabel(f'GOES-{satellite_no} Epead Proton\\nFlux [pfu]', fontname=\"Arial\", fontsize = 16)\n\t\t\n\t\t\telif goes_corrected_option == 'no':\n\t\t\t\tfor i in sorted(energy_bin_list):\n\t\t\t\t\taxes[length_data_list[j]].plot(proton_df[f'{i[0]}'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color=f'{i[2]}', label= f'{i[1]}', zorder=5)#, logy=True)\n\t\t\t\taxes[length_data_list[j]].set_yscale('log')\n\t\t\t\t# axes[length_data_list[j]].set_ylim((10**(-3)), (10**3))\n\t\t\t\n\t\t\t\taxes[length_data_list[j]].set_yticks([10**-3, 10**-2, 10**-1, 10**0, 10**1, 10**2, 10**3])\n\t\t\t\taxes[length_data_list[j]].set_ylabel(f'GOES-{satellite_no} Epead Proton\\nFlux [pfu]', fontname=\"Arial\", fontsize = 16)\n\t\t\n\t\t\n\t\t\n\t\t\ts_time = datetime.datetime.strptime(start_hour, '%H').time()\n\t\t\te_time = datetime.datetime.strptime(end_hour, '%H').time()\n\t\t\n\t\t\ts_dtime = datetime.datetime.combine(start, s_time)\n\t\t\te_dtime = datetime.datetime.combine(end, e_time)\n\t\t\n\t\t\tfor i in range(len(proton_event_df)):\n\t\t\t\tif s_dtime <= (proton_event_df['start_time'][i] and proton_event_df['end_time'][i]) <= e_dtime:\n\t\t\t\t\taxes[length_data_list[j]].axvspan(proton_event_df['start_time'][i], proton_event_df['end_time'][i], color='palegreen', alpha=0.5)\n\t\t\t\t\taxes[length_data_list[j]].axvline(proton_event_df['start_time'][i], linewidth=1, zorder=2, color='green', linestyle='-') #  xmin=0, xmax=1\n\t\t\t\t\taxes[length_data_list[j]].axvline(proton_event_df['end_time'][i], linewidth=1, zorder=2, color='green', linestyle='-') #  xmin=0, xmax=1\n\t\t\n\t\t\taxes[length_data_list[j]].axhline(proton_threshold, linewidth=1, zorder=2, color='red', linestyle='-.', label=f'{round(proton_threshold, 3)} pfu') #  xmin=0, xmax=1 # threshold\n\t\t\n\t\t\t# axes[length_data_list[j]].axvline(s_dtime, linewidth=1, zorder=2, color='green', linestyle=':') #  xmin=0, xmax=1\n\t\t\t# axes[length_data_list[j]].axvline(e_dtime, linewidth=1, zorder=2, color='green', linestyle=':') #  xmin=0, xmax=1\n\t\t\n\t\t\n\t\t\tapplyPlotStyle()\n\t\t\n\t\t\n\t\t\n\t\tif '2' in option_bin_set:\n\t\t\tnext_global()\n\t\t\t#=============== Spectrogram testing (begin)\n\t\t\t'''\n\t\t\tfrom scipy import signal\n\t\t\tfrom matplotlib.pyplot import cm\n\t\t\timport seaborn as sns\n\t\t\t\n\t\t\tax = sns.heatmap(rb_data.loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], cmap=cm.viridis)\n\t\t\t#axes[length_data_list[j]].set_ylabel('Wind Type III\\nRadio Burst [sfu]', fontname=\"Arial\", fontsize = 12)\n\t\t\t#axes[length_data_list[j]].set_ylabel('Type III Radio\\nBurst Intensity [sfu]', fontname=\"Arial\", fontsize = 12)\n\t\t\n\t\t\t\n\t\t\t'''\n\t\t\t#=============== Spectrogram testing (end)\n\t\t\n\t\t\n\t\t\t# axes[length_data_list[j]].plot(p4(xx).loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='red', label= 'Gaussian')\n\t\t\n\t\t\t#================= Working code (uncommented) ---- begin ----\n\t\t\t\n\t\t\tcolor_cm=iter(cm.viridis(np.linspace(0,1, 5 )))\n\t\t\tfreq_list = [100, 300, 500, 700, 900]\n\t\n\t\t\tfor frequency in freq_list:\n\t\t\t\tcolor_choice = next(color_cm)\n\t\t\t\taxes[length_data_list[j]].plot(rb_data[frequency].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color=color_choice, label= f'{frequency} kHz', zorder=5)\n\t\t\t\t\n\t\t\taxes[length_data_list[j]].set_ylabel('Wind Type III\\nRadio Burst [sfu]', fontname=\"Arial\", fontsize = 16)\n\t\t\taxes[length_data_list[j]].set_ylabel('Type III Radio\\nBurst Int. [sfu]', fontname=\"Arial\", fontsize = 16)\n\t\t\t\n\t\t\n\t\t\t#================= Working code (uncommented) ---- end ----\n\t\t\t# axes[length_data_list[j]].plot(rb_data[int('1020')].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='red', label= '1020')\n\t\t\t# wind_range = range(20,1041, 2)\n\t\t\t# axes[length_data_list[j]].plot(rb_data.loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}']) # [wind_range] , color='red', label= '1020')\n\t\t\n\t\t\t# rb_data.loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'].plot(cmap='viridis') # using viridis\n\t\t\n\t\t\t''' This is the next step of the program, uncomment when using\n\t\t\taxes[length_data_list[j]].plot(rb_data[20].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='red', label= '20 kHz')\n\t\t\taxes[length_data_list[j]].plot(rb_data[1040].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='green', label= '1040 kHz')\n\t\t\t'''\n\t\t\n\t\t\t'''\n\t\t\tfor i in rb_data.columns:\n\t\t\t\tif i != 'avg':\n\t\t\t\t\taxes[length_data_list[j]].plot(rb_data[i].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], label= f'i')\n\t\t\t'''\n\t\t\n\t\t\n\t\t\n\t\t\tapplyPlotStyle()\n\t\t\n\t\t\n\t\tif '3' in option_bin_set:\n\t\t\tnext_global()\n\t\t\tcolor_count = []\n\t\t\n\t\t\tfor i in sorted_nm_list:\n\t\t\t\n\t\t\t\tcolor_list = ['red','orange','green','blue','indigo','violet','purple'] #,'yellow'\n\t\t\t\tcolor_list = list(set(color_list) - set(color_count))\n\t\t\t\n\t\t\t\trand_color = random.choice(color_list)\n\t\t\t\tcolor_count.append(rand_color)\n\t\t\t\n\t\t\t\taxes[length_data_list[j]].plot(nm_data[f'{i}'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color=rand_color, label=f'{i}', zorder=5)\n\t\t\taxes[length_data_list[j]].set_ylabel('Neu. Monitor\\n[counts/s]', fontname=\"Arial\", fontsize = 16)\n\t\t\tapplyPlotStyle()\n\t\t\n\t\tif '4' in option_bin_set:\n\t\t\tnext_global()\n\t\t\taxes[length_data_list[j]].plot(wind_data['wind_bulk_vel'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='red', label='Wind: Ion Bulk Flow Speed GSE', zorder=5)\n\t\t\taxes[length_data_list[j]].plot(ace_data['ace_bulk_vel'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='blue', label='ACE: H Bulk Speed', zorder=5)\n\t\t\taxes[length_data_list[j]].set_ylabel('Solar Wind\\nSpeed [km/s]', fontname=\"Arial\", fontsize = 16)\n\t\t\tapplyPlotStyle()\n\t\t\n\n\t\tif '5' in option_bin_set:\n\t\t\tdef flare_class(X):\n\t\t\t\tif X == 10**-4:\n\t\t\t\t\treturn \"X\"\n\t\t\n\t\t\tnext_global()\n\t\t\taxes[length_data_list[j]].plot(xray_df['B_FLUX'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='blue', label='0.1-0.8 nm', zorder=5)\n\t\t\taxes[length_data_list[j]].plot(xray_df['A_FLUX'].loc[f'{event_obj_start_str_date}':f'{event_obj_end_str_date}'], color='red', label='0.05-0.4 nm', zorder=5)\n\t\t\taxes[length_data_list[j]].set_yscale('log')\n\t\t\n\t\t\tflare_classes = ['', 'A', 'B', 'C', 'M', 'X']\n\t\t\tax2 = axes[length_data_list[j]].twinx()\n\t\t\tax2.set_yscale('log')\n\t\t\tax2.set_ylim(axes[length_data_list[j]].get_ylim())\n\t\t\tax2.set_yticks([10**-9, 10**-8, 10**-7, 10**-6, 10**-5, 10**-4, 10**-3, 10**-2])\n\t\t\tax2.set_yticklabels(labels=flare_classes)\n\t\t\tax2.tick_params(axis='y', which='both', direction='in')\n\t\t\n\t\t\taxes[length_data_list[j]].set_yticks([10**-9, 10**-8, 10**-7, 10**-6, 10**-5, 10**-4, 10**-3, 10**-2])\n\t\t\n\t\t\taxes[length_data_list[j]].set_ylabel(f'GOES-{satellite_no_xray} Xray\\nFlux [Wm$^2$]', fontname=\"Arial\", fontsize = 16)\n\t\t\tapplyPlotStyle()\n\t\t\n\t\t# plt.xlabel('Time [UT]', fontname=\"Arial\", fontsize = 16)\n\t\taxes[length_data_list[j]].set_xlabel('Time [UT]', fontname=\"Arial\", fontsize = 16)\n\n\t\t\n\t\tmyFmt = mdates.DateFormatter('%m/%d\\n%H:%M')\n\t\tax = plt.gca()\n\t\tax.xaxis.set_major_formatter(myFmt)\n\t\t\n\t\tplt.setp(ax.xaxis.get_majorticklabels(), rotation=0, horizontalalignment='center')\n\t\tplt.suptitle(f\"Space Weather Monitor\\n[{event_obj_start_str} -- {event_obj_end_str}]  (Flare Class: {event_list['flare_int']})\", fontname=\"Arial\", fontsize = 16) #, y=1.04,\n\t\t#plt.tight_layout() ({event_list['flare_int']})\n\t\t\n\t\tplt.subplots_adjust(wspace = 0, hspace = 0, top=0.91)\n\n\t\t#plt.savefig('omni_test_legacy.png', format='png', dpi=900)\n\t\tif save_plot_option == 'yes':\n\t\t\tplt.savefig(f'a_events/omni_{event_obj_start_str_date[:8]}.png', format='png', dpi=500)\n\t\t\tprint(f'\\nPlot successfully generated for: {event_obj_start_str_date[:8]}', end='\\n')\n\t\t\n\t\tif event_option_show == 'no':\n\t\t\tcontinue\n\n\t\tif event_option_show == 'yes':\n\t\t\tplt.show()\n\t\t\n\n\nsys.exit(0)\n\n\t","sub_path":"Scripts/pandas_test_omni_event_v2.py","file_name":"pandas_test_omni_event_v2.py","file_ext":"py","file_size_in_byte":54144,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"401181994","text":"\n\nfrom xai.brain.wordbase.adjectives._wood import _WOOD\n\n#calss header\nclass _WOODS(_WOOD, ):\n\tdef __init__(self,): \n\t\t_WOOD.__init__(self)\n\t\tself.name = \"WOODS\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"wood\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_woods.py","file_name":"_woods.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"18160118","text":"#!/usr/bin/env python\nimport os\nimport sys\nimport re\n\ntry:\n    from setuptools import setup\n    setup\nexcept ImportError:\n    from distutils.core import setup\n    setup\n\nif sys.argv[-1] == \"publish\":\n    os.system(\"python setup.py sdist upload\")\n    sys.exit()\n\n# Handle encoding\nmajor, minor1, minor2, release, serial = sys.version_info\nif major >= 3:\n    def rd(filename):\n        f = open(filename, encoding=\"utf-8\")\n        r = f.read()\n        f.close()\n        return r\nelse:\n    def rd(filename):\n        f = open(filename)\n        r = f.read()\n        f.close()\n        return r\n\nsetup(\n    name='moscatel',\n    packages =['moscatel'],\n    version=\"0.1.1\",\n    author='John Livingston and Jerome de Leon',\n    author_email = 'jpdeleon.bsap@gmail.com',\n    url = 'https://github.com/jpdeleon/moscatel',\n    license = ['GNU GPLv3'],\n    description ='A simple pipeline for obtaining light curve of MUSCAT data.',\n    long_description=rd(\"README.md\") + \"\\n\\n\"\n                    + \"---------\\n\\n\",\n    package_dir={\"moscatel\": \"moscatel\"},\n    scripts=['scripts/moscatel-phot', 'scripts/moscatel-analysis', 'scripts/moscatel-init'],\n    include_package_data=True,\n    keywords=['muscat','multi-color','transit photometry'],\n    classifiers = [\n        'Development Status :: 5 - Production/Stable',\n        'Intended Audience :: Science/Research',\n        'Topic :: Scientific/Engineering',\n        'Programming Language :: Python'\n        ],\n    install_requires = ['numpy', 'pandas', 'matplotlib', 'astropy', 'photutils', 'tqdm', 'scikit-image'],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"8918420","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the license found in the\n# LICENSE file in the root directory of this source tree.\n#\nimport logging\nimport os\nimport tempfile\nimport traceback\nfrom shutil import copy2, move\n\nimport torch\n\ndef init_model_from_weights(\n    model,\n    state_dict,\n    skip_layers=None,\n    print_init_layers=True,\n    replace_suffix=None,\n    freeze_bb=False,\n    append_suffix=\"trunk.base_model.\",\n):\n    state_dict = state_dict[\"model\"]\n\n    all_layers = model.state_dict()\n    init_layers = {layername: False for layername in all_layers}\n\n    new_state_dict = {}\n    for param_name in state_dict:\n        if \"module.trunk.2\" not in param_name:\n            continue\n        param_data = param_name.split(\".\")\n        newname = \"\"\n        for i in range(len(param_data[3:])):\n            newname += param_data[i+3]+\".\"\n        new_state_dict[newname[:-1]] = state_dict[param_name]\n\n    state_dict = new_state_dict\n            \n    local_rank = int(os.environ.get(\"LOCAL_RANK\", 0))\n    not_found, not_init = [], []\n    for layername in all_layers.keys():\n        if (\n            skip_layers and len(skip_layers) > 0 and layername.find(skip_layers) >= 0\n        ) or layername.find(\"num_batches_tracked\") >= 0:\n            if print_init_layers and (local_rank == 0):\n                not_init.append(layername)\n                print(f\"Ignored layer:\\t{layername}\")\n            continue\n        if layername in state_dict:\n            param = state_dict[layername]\n            if not isinstance(param, torch.Tensor):\n                param = torch.from_numpy(param)\n            all_layers[layername].copy_(param)\n            init_layers[layername] = True\n            if print_init_layers and (local_rank == 0):\n                print(f\"Init layer:\\t{layername}\")\n        else:\n            not_found.append(layername)\n            if print_init_layers and (local_rank == 0):\n                print(f\"Not found:\\t{layername}\")\n    ####################### DEBUG ############################\n    # _print_state_dict_shapes(model.state_dict())\n    torch.cuda.empty_cache()\n    return model\n","sub_path":"checkpoint.py","file_name":"checkpoint.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"464032142","text":"import os, random, io, aiohttp, datetime, pytz\n\nimport discord\nfrom discord.ext import commands\nfrom discord.ext.commands import has_permissions\n\nclient = commands.Bot(command_prefix = '!')\n\n\n# Ready Annoucement\n@client.event\nasync def on_ready():\n        print('KermodeBot is ready for action!')\n\n# Help Command\n@client.command()\nasync def intro(ctx):\n        await ctx.send('Current commands are:\\n\\n!pingu - checks bot ping\\n' \\\n'!time - tells the local time\\n' \\\n'!joke - tells a hilarious (terrible) joke\\n' \\\n'!yeet - kicks a member from the server (mods only)\\n' \\\n'!clear - clears n-1 of the most recent messages from the channel (mods only)\\n' \\\n)\n\n# On Message\n@client.event\nasync def on_message(message):\n        # React to Michael\n        if message.author.name == 'God has forsaken us':\n                emoji = '😡'\n                await message.add_reaction(emoji)\n        await client.process_commands(message)\n\n# Tell a joke\n@client.command()\nasync def joke(ctx):\n        jokes = ['How does Janna shield her allies?\\nWith ease!', \\\n        \"Why did Twisted Fate get deported?\\nHe doesn't have a green card!\", \\\n        \"Why does Taliyah love bread?\\n Because she's a sand witch!\", \\\n        \"What's Vayne's favorite website? Tumblr!\"]\n        await ctx.send(f'{random.choice(jokes)}')\n\n# Check Ping\n@client.command()\nasync def pingu(ctx):\n        await ctx.send(f'Noot Noot! {round(client.latency * 1000)}ms')\n\n#Time Check\n# getting utc timezone\nutc = pytz.utc\n\n# getting timezone by name\npst = pytz.timezone('US/Pacific')\n\n@client.command()\nasync def time(ctx):\n        await ctx.send(f'The current time in Squamish is {datetime.datetime.now(tz=pst).time()}')\n\n# Clear Messages - default clear just the previous message\n@client.command()\n@commands.has_permissions(manage_messages=True)\nasync def clear(ctx, amount=2):\n        await ctx.channel.purge(limit=amount)\n\n#Kick - Kick completely from server\n@client.command()\n@commands.has_permissions(kick_members=True)\nasync def yeet(ctx, member : discord.Member):\n        await member.kick()\n\n# Actual Run\nclient.run('NjMzNzE0Mzk4ODgzNzQxNzI5.Xa4BPQ.PkDew1nsY7Xh-WD8_Fe36pE3Lg4')\n\n\n                                                     \n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"390159198","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport PIL as P\n\nfrom .coords import Drawable, mk_domain\nfrom .objects import TileSet\n\n\nclass Image:\n    \"\"\"\n    Our base representation of image data, perfectly functional for the\n    majority of use cases.\n\n    pixels : ndarry\n        The numpy array holding the raw image data\n    \"\"\"\n\n    def __init__(self, width=None, height=None, domain=None,\n                 background=(255, 255, 255, 255), xAA=1, pixels=None):\n        \"\"\"\n        Image Constructor\n\n        There are two ways to construct an image, the first is to specify the\n        width and height in pixels plus a fill color.\n\n        Or you can provide a numpy array with the shape (height, width, 4)\n        which you can use as an image.\n\n        It's important to note that if you provide the pixels argument it will\n        override all the others.\n\n        width : int, optional\n            The width of the image in pixels. Default None\n        height : int, optional. Default None\n            The height of the image in pixels\n        domain : (int, int) -> np.meshgrid-like\n            This is a function which given a width and a height returns a grid\n            containing the mathematical points associated with each pixel.\n            Default: None\n        background : 4-tuple, optional\n            The fill RGBA colour of the image, each value can be in the range\n            0-255 (default is (0, 0, 0, 0) - fully transparent black)\n        pixels : numpy array, optional\n            If you already have a numpy array you want to use as an image\n            simply pass it in here. **Note:** The array must have the shape\n            (height, width, 4). Default value None.\n        \"\"\"\n\n        self._domain = domain\n        self._xAA = xAA\n\n        if pixels is not None:\n            shape = pixels.shape\n\n            if len(shape) != 3 or shape[-1] != 4:\n                raise ValueError(\"Pixels array must have shape:\"\n                                 \" (height, width, 4)\")\n\n            self.pixels = pixels\n            self._width = shape[1]\n            self._height = shape[0]\n        else:\n            if width is None or height is None:\n                raise ValueError(\"If no pixels array is given you\"\n                                 \" must specify a width and height\")\n\n            # If given an RGB lookalike, convert it to RGBA\n            if len(background) == 3:\n                background = tuple([*background, 255])\n\n            self._height = height\n            self._width = width\n\n            self.pixels = np.full((height * xAA, width * xAA, 4), background,\n                                  dtype=np.uint8)\n\n    @property\n    def width(self):\n        return self._width\n\n    @property\n    def height(self):\n        return self._height\n\n    @property\n    def color(self):\n        return self.pixels[:, :, 0:3]\n\n    @color.setter\n    def color(self, value):\n        self.pixels[:, :, 0:3] = value\n\n    @property\n    def red(self):\n        return self.pixels[:, :, 0]\n\n    @red.setter\n    def red(self, value):\n        self.pixels[:, :, 0] = value\n\n    @property\n    def green(self):\n        return self.pixels[:, :, 1]\n\n    @green.setter\n    def green(self, value):\n        self.pixels[:, :, 1] = value\n\n    @property\n    def blue(self):\n        return self.pixels[:, :, 2]\n\n    @blue.setter\n    def blue(self, value):\n        self.pixels[:, :, 2] = value\n\n    @property\n    def alpha(self):\n        return self.pixels[:, :, 3]\n\n    @alpha.setter\n    def alpha(self, value):\n        self.pixels[:, :, 3] = value\n\n    @property\n    def xAA(self):\n        return self._xAA\n\n    @property\n    def domain(self):\n        return self._domain\n\n    @domain.setter\n    def domain(self, value):\n        self._domain = value\n\n    @classmethod\n    def fromarray(cls, px):\n        return cls(pixels=px)\n\n    @classmethod\n    def fromfile(cls, filename):\n        img = P.Image.open(filename)\n        arr = np.array(img.getdata(), dtype=np.uint8)\n        arr = arr.reshape(img.size[1], img.size[0], 4)\n        return Image(pixels=arr)\n\n    def _flip_index(self, index):\n        \"\"\"\n        The underlying representation is in (y,x) 'coordinates' which\n        breaks normal mental models of coordinates being (x,y). So in\n        an effort to keep this mental model consistent throughout this\n        method reorders a given index in that manner.\n\n        If index is just a number, we assume it to be indexing the x\n        coordinate so we return (:, x)\n\n        If index is tuple of indices we swap the first two entries\n        \"\"\"\n\n        if isinstance(index, (tuple,)):\n            if len(index) == 2:\n                return (index[1], index[0])\n            else:\n                return (index[1], index[0], *index[2:])\n        else:\n            return (slice(None), index)\n\n    def _get_by_pixels(self, index):\n        \"\"\"\n        Once we have decided that the index represents pixels\n        this method is responsible for interpreting it and\n        returning the appropriate object\n        \"\"\"\n        px = self.pixels[index]\n        shape = px.shape\n\n        if len(shape) == 3:\n            return Image.fromarray(px)\n        else:\n            return px\n\n    def _is_pixel_addr(self, index):\n        \"\"\"\n        This method is for deciding whether or not a given\n        index represents a pixel index or mathematical points\n        \"\"\"\n\n        for idx in index:\n\n            # Is this item a simple integer - hence pixel address?\n            if isinstance(idx, (int,)):\n                continue\n\n            # Is it a float?\n            if isinstance(idx, (float,)):\n                return False\n\n            # Is it a slice?\n            if isinstance(idx, (slice,)):\n\n                if isinstance(idx.start, (float,)) or\\\n                   isinstance(idx.stop, (float,)):\n                    return False\n\n        # If we get this far, then it must be a pixel address\n        return True\n\n    def __getitem__(self, index):\n        idx = self._flip_index(index)\n\n        if self._is_pixel_addr(idx):\n            return self._get_by_pixels(idx)\n\n        # If we get this far, we must be dealing with a 'mathematical\n        # address' So we 'simply' have to figure out which pixels are mapped\n        # to the mathematial points in question\n\n        # This involves using the built-in domain function - which\n        # of course has to exist for this to work\n        if self._domain is None:\n            raise RuntimeError('In order to index pixels by mathematical '\n                               'points the image must be associated with '\n                               'a domain!')\n\n        # For the moment we will restrict ourselves to the index being made\n        # up of 2 slices. This might change eventually\n        if not isinstance(idx[0], (slice,)) or\\\n           not isinstance(idx[1], (slice,)):\n            raise ValueError('Mathematical indexing is currently only '\n                             'supported for slices')\n\n        # Step 0: Unpack the slices\n        yslice = idx[0]\n        ystart = yslice.start\n        ystop = yslice.stop\n\n        xslice = idx[1]\n        xstart = xslice.start\n        xstop = xslice.stop\n\n        # Step 1: Evaluate the domain function to get the points\n        (xs, *_), YS = self._domain(self.width * self.xAA,\n                                    self.height * self.xAA)\n        ys = np.array([it[0] for it in YS])\n\n        # Step 2: See which pixels the values are closest to\n        xstart_d = [(None, None)] if xstart is None else\\\n                   sorted([(abs(x - xstart), i) for i, x in enumerate(xs)])\n\n        xstop_d = [(None, None)] if xstop is None else\\\n                  sorted([(abs(x - xstop), i) for i, x in enumerate(xs)])\n\n        ystart_d = [(None, None)] if ystart is None else\\\n                   sorted([(abs(y - ystart), i) for i, y in enumerate(ys)])\n\n        ystop_d = [(None, None)] if ystop is None else\\\n                  sorted([(abs(y - ystop), i) for i, y in enumerate(ys)])\n\n        # Step 3: Construct new slice objects with the new pixel addresses\n        # and pass it off to the _get_by_pixels method\n        slice_x = slice(xstart_d[0][1], xstop_d[0][1], None)\n        slice_y = slice(ystart_d[0][1], ystop_d[0][1], None)\n\n        return self._get_by_pixels((slice_y, slice_x))\n\n    def __setitem__(self, index, value):\n\n        # If we are given something that looks like RGB\n        # convert it to RGBA\n        if len(value) == 3:\n            value = tuple([*value, 255])\n\n        self.pixels[self._flip_index(index)] = value\n\n    def __repr__(self):\n        shape = self.pixels.shape\n        return '%ix%i Image' % (shape[1], shape[0])\n\n    def __neg__(self):\n        \"\"\"\n        Easily obtain the negative of an image by typing\n        (-img)\n        \"\"\"\n\n        # Create the function that will invert the values of\n        # the color pixels and vectorize it\n        neg = lambda v: abs(v - 255)\n        vneg = np.vectorize(neg, otypes=(np.uint8,))\n\n        # Create the new pixel array\n        alphas = self.alpha\n        alphas.shape = (self.height, self.width, 1)\n        colors = vneg(self.color)\n\n        px = np.append(colors, alphas, axis=2)\n\n        return Image(pixels=px)\n\n    def __and__(self, other):\n        \"\"\"\n        Implementation of the boolean AND, best when used with black and\n        white images\n        \"\"\"\n\n        if not isinstance(other, (Image,)):\n            raise TypeError('AND is only supported between instances of '\n                            'the Image class')\n\n        if self.pixels.shape != other.pixels.shape:\n            raise ValueError('AND can only be used with Images that have '\n                             'the same dimensions!')\n\n        ax = self.pixels\n        bx = other.pixels\n\n        # Scale the pixels of b\n        scaled = bx / 255\n\n        # Do the multiplication - it's important to note that the entries\n        # of cx are now 'float64'\n        cx = ax * scaled\n\n        # Create a new numpy array so that the conversion back to uint8 is\n        # correct\n        px = np.array(cx, dtype=np.uint8)\n\n        return Image.fromarray(px)\n\n    def __call__(self, f, overwrite_domain=True, use_host_domain=False):\n        \"\"\"\n        Implementing this 'magic' method allows us to use img(drawable)\n        to trigger the 'drawing' of an object onto an arbitrary image.\n\n        The default behavior of this function is to take the drawable\n        f, extract it's domain function and proceed to compute the mask\n        and color of the affected pixels. Once that is done, the extracted\n        domain function is stored internally so that users may optionally\n        index the pixels by mathematical points, instead of raw pixels.\n\n        However, it is possible to override this behavior with the two\n        options:\n            - use_host_domain -> False by default\n            - overwrite_domain -> True by default\n\n        If use_host_domain is True, instead of using the domain that\n        comes with the drawable, the domain already associated with\n        the underlying image is used instead.\n\n        If overwrite_domain is False, the image keeps its original\n        domain, instead of saving the new one that the drawable\n        possesses\n        \"\"\"\n\n        # Of course, the image has to have a domain already if we are\n        # to use this one instead\n        if use_host_domain and self._domain is None:\n            raise RuntimeError('The image does not have an associated '\n                               'domain!')\n\n        # Get the domain from the correct place\n        if use_host_domain:\n            XS, YS = self._domain(self.width * self.xAA,\n                                  self.height * self.xAA)\n        else:\n            XS, YS = f.domainfunc(self.width * self.xAA,\n                                  self.height * self.xAA)\n\n        # Compute the mask\n        mask = f.maskfunc(XS, YS)\n\n        # When it comes to the color function, there are some cases\n        # to consider. If the 'function' is in fact just a tuple\n        # then we don't have to evaluate it across the entire domain\n        colorfunc = f.colorfunc\n\n        if isinstance(colorfunc, (tuple,)):\n            self.pixels[mask] = colorfunc\n        else:\n            self.pixels[mask] = colorfunc(XS[mask], YS[mask])\n\n        # Only now that all is said and done do we overwrite the domain,\n        # that way we don't get something halfway between one or the other\n        if overwrite_domain and not use_host_domain:\n            self._domain = f.domainfunc\n\n    def show(self):\n        \"\"\"\n        Show the image using a matplotlib figure - useful for use in\n        notebooks\n        \"\"\"\n        return plt.imshow(self.pixels)\n\n    def save(self, filename):\n        \"\"\"\n        Save the image to the given filename\n        \"\"\"\n\n        image = P.Image.frombuffer('RGBA', (self.width * self.xAA,\n                                   self.height * self.xAA), self.pixels,\n                                   'raw', 'RGBA', 0, 1)\n\n        # Apply our naive AA by downscaling the image\n        if self.xAA > 1:\n            image = image.resize((self.width, self.height), resample=P.Image.BICUBIC)\n\n        with open(filename, 'wb') as f:\n            image.save(f)\n\n\nclass LayeredImage:\n    \"\"\"\n    This is a container for a number of images, with a number of conveniences\n    for blending/merging them together - just as you would in GIMP/Photoshop\n    etc.\n    \"\"\"\n\n    def __init__(self, width=None, height=None, num_layers=4, imgs=None):\n\n        if imgs is not None:\n\n            # TODO: Do a check to make sure given images make sense\n            self._layers = imgs\n            self._width = imgs[0].width\n            self._height = imgs[0].height\n\n        else:\n            self._layers = [Image(width, height, background=(0, 0, 0, 0))\n                            for _ in range(num_layers)]\n\n            self._width = width\n            self._height = height\n\n    def __repr__(self):\n        return \"%ix%i Layered Image with %i layers\"\\\n                % (self.width, self.height, self.nlayers)\n\n    def __getitem__(self, key):\n\n        if isinstance(key, (int,)):\n            return self._layers[key]\n\n        else:\n            return 'Other methods coming soon'\n\n    @classmethod\n    def fromlist(cls, imgs):\n        return cls(imgs=imgs)\n\n    @property\n    def width(self):\n        return self._width\n\n    @property\n    def height(self):\n        return self._height\n\n    @property\n    def nlayers(self):\n        return len(self._layers)\n\n\nclass TiledImage(Image):\n    \"\"\"\n    This builds on the base image class, to allow easy creation of\n    images from tilesets. Useful for constructing backgrounds based\n    on a grid - e.g level designs etc.\n    \"\"\"\n\n    def __init__(self, width, height, xmax=10, ymax=10,\n                 xmin=0, ymin=0, *args, **kwargs):\n\n        # First call the parent to set things up\n        super().__init__(width, height, *args, **kwargs)\n\n        # Now we will set up the domain for the image\n        self._xmin = xmin\n        self._xmax = xmax\n\n        self._ymin = ymin\n        self._ymax = ymax\n\n        self.domain = mk_domain(xmin, xmax, ymin, ymax)\n\n    def __call__(self, tileset, overwrite_domain=False):\n        \"\"\"\n        This allows for the possibility of easy application\n        of a tileset to the Image, but bog standard drawables\n        are still ok.\n        \"\"\"\n\n        # Are we dealing with a drawable?\n        if isinstance(tileset, (Drawable,)):\n\n            # If so do the \"normal\" thing\n            super().__call__(tileset, overwrite_domain)\n            return\n\n        if isinstance(tileset, (TileSet,)):\n\n            # Here we assume that the tileset is not time dependent\n            # so just call the layout method with some, argument\n            layout = tileset.layout(0, 0)\n            tiles = tileset._tiles\n\n        elif isinstance(tileset, (tuple,)):\n\n            # Here we assume that we have been given a time dependent\n            # tileset\n            tiles, layout = tileset\n\n        else:\n            raise TypeError('TiledImages only support Drawables and '\n                            'TileSets!!')\n\n        # With that out of the way let's get down to it. First let's check\n        # that the given tileset makes sense\n\n        # Numpy freaks out if we try to .unique() an array containing None's\n        # So just turn them into zeros, as a tileset will always have a\n        # zeroth tile\n        tozero = lambda v: v if v is not None else 0\n        vtoz = np.vectorize(tozero)\n        test_layout = vtoz(layout)\n\n        if len(tiles) < len(np.unique(test_layout)):\n            raise ValueError('The layout defined references more tiles '\n                             'than are defined!')\n\n        # Transpose the layout array so that it makes sense to the user\n        layout = layout.transpose()\n\n        # Next we need to decide on the size of each of the grid squares\n        nx, ny = layout.shape\n        xsize = (self.xmax - self.xmin) / nx\n        ysize = (self.ymax - self.ymin) / ny\n\n        # Now we go through each tile in the layout and apply it to the\n        # image\n        for j in range(ny):\n\n            ystart = self.ymin + j*ysize\n            yend = ystart + ysize\n\n            for i in range(nx):\n                xstart = self.xmin + i*xsize\n                xend = xstart + xsize\n\n                tileidx = layout[i, j]\n\n                if tileidx is not None:\n                    tile = tiles[tileidx]\n                    self[xstart:xend, ystart:yend](tile)\n\n    @property\n    def xmin(self):\n        return self._xmin\n\n    @xmin.setter\n    def xmin(self, value):\n\n        if not isinstance(value, (int, float)):\n            raise TypeError('xmin must be a number!')\n\n        if value >= self.xmax:\n            raise ValueError('xmin cannot be larger than xmax!')\n\n        self._xmin = value\n        self.domain = mk_domain(value, self.xmax, self.ymin, self.ymax)\n\n    @property\n    def xmax(self):\n        return self._xmax\n\n    @xmax.setter\n    def xmax(self, value):\n\n        if not isinstance(value, (int, float)):\n            raise TypeError('xmax must be a number!')\n\n        if value <= self.xmin:\n            raise ValueError('xmax cannot be less than xmin!')\n\n        self._xmin = value\n        self.domain = mk_domain(self.xmin, value, self.ymin, self.ymax)\n\n    @property\n    def ymin(self):\n        return self._ymin\n\n    @ymin.setter\n    def ymin(self, value):\n\n        if not isinstance(value, (int, float)):\n            raise TypeError('ymax must be a number!')\n\n        if value >= self.ymax:\n            raise ValueError('ymin cannot be larger than ymax!')\n\n        self._ymin = value\n        self.domain = mk_domain(self.xmin, self.xmax, value, self.ymax)\n\n    @property\n    def ymax(self):\n        return self._ymax\n\n    @ymax.setter\n    def ymax(self, value):\n\n        if not isinstance(value, (int, float)):\n            raise TypeError('ymax must be a number!')\n\n        if value <= self.ymin:\n            raise ValueError('ymax cannot be less than ymin!')\n\n        self._ymax = value\n        self.domain = mk_domain(self.xmin, self.xmax, self.ymin, value)\n","sub_path":"stylo/image.py","file_name":"image.py","file_ext":"py","file_size_in_byte":19199,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"461700628","text":"'''\nCreated on 27-Jun-2017\n\n@author: fly\n'''\nimport os\nimport datetime\nimport csv\nimport pandas\n\nheader =['user_id','time','order_id','trading_symbol','price','quantity','order_type',\n        'product_type','exchange','disclosed_quantity','squareoff_value',\n        'stoploss_value','trailing_stoploss','trigger_price','validity','variety','status','transaction_type']\n\ndef createDirIfNotExist(dirPath):\n    if not os.path.exists(os.path.dirname(dirPath)) :\n        os.makedirs(os.path.dirname(dirPath)) \n        \n        \ndef getledgerData(orderNo, parameters,status='active'):\n    return [parameters.get('client_id'),(datetime.datetime.now()).strftime(\"%Y-%m-%d %H:%M:%S\"),\n                   orderNo,parameters.get('tradingsymbol'),parameters.get('price'),parameters.get('quantity'),\n                   parameters.get('order_type'),parameters.get('product'),parameters.get('exchange'),\n                   parameters.get('disclosed_quantity'),parameters.get('squareoff_value'),\n                   parameters.get('stoploss_value'),parameters.get('trailing_stoploss'),\n                   parameters.get('trigger_price'),parameters.get('validity'),parameters.get('variety'),\n                   status,parameters.get('transaction_type')]\n\ndef writeToLogFile(filePath, data) :\n    if os.path.exists(filePath) :\n        file_handler = csv.writer(open(filePath,\"a\"), delimiter=',',quoting=csv.QUOTE_ALL)    \n    else :\n        file_handler = csv.writer(open(filePath,\"w\"), delimiter=',',quoting=csv.QUOTE_ALL)\n        file_handler.writerow(header)\n                \n    file_handler.writerow(data)\n    \n'''log each order placed request into the file'''\ndef logRequest(orderNo, parameters,status='active', dirpath = 'dataset/ledger'):\n    filePath = '/'.join([dirpath,'ledgerbook_log_{}.csv'.format((datetime.datetime.now()).strftime(\"%Y-%m-%d\"))])\n    createDirIfNotExist(dirpath)\n    writeToLogFile(filePath,getledgerData(orderNo,parameters,status))\n   \n        \ndef logCancelRequest(orderNo,status,logFilePath = 'dataset/ledger'):\n    logFilePath = '/'.join([logFilePath,'ledgerbook_log_{}.csv'.format((datetime.datetime.now()).strftime(\"%Y-%m-%d\"))])\n    df = pandas.read_csv(logFilePath) \n    data = df.loc[df['order_id'] == int(orderNo)].values.tolist()[0]   \n    data[len(data)-1] = status    \n    writeToLogFile(logFilePath,data)    \n\ndef updateLedger(orderNo,parameters,ledgerFilePath = 'dataset/ledger/ledger_{0}.csv'):\n    data = getledgerData(orderNo,parameters)\n    ledgerFilePath = ledgerFilePath.format((datetime.datetime.now()).strftime(\"%Y-%m-%d\"))\n    df = pandas.read_csv(ledgerFilePath)\n    df.loc[df['order_id']==orderNo, header] = data\n    df.to_csv(ledgerFilePath, index=False)\n    \n\ndef cancelLedger(orderNo,ledgerFilePath = 'dataset/ledger/ledger_{0}.csv'):\n    ledgerFilePath = ledgerFilePath.format((datetime.datetime.now()).strftime(\"%Y-%m-%d\"))\n    df = pandas.read_csv(ledgerFilePath)\n    df.loc[df['order_id']== int(orderNo), ['status']] = 'cancel' \n    df.to_csv(ledgerFilePath, index=False)\n    \n\ndef addtoLedger(orderNo,parameters,ledgerFilePath = 'dataset/ledger/ledger_{0}.csv'):\n    #responsedata = json.loads(response.text) \n    #if (\"success\"==responsedata['status']): \n    ledgerFilePath = ledgerFilePath.format((datetime.datetime.now()).strftime(\"%Y-%m-%d\"))\n    createDirIfNotExist(ledgerFilePath)\n    writeToLogFile(ledgerFilePath,getledgerData(orderNo,parameters))\n       \n         \n","sub_path":"Kubera/zerodhaClient/log/OrderLog.py","file_name":"OrderLog.py","file_ext":"py","file_size_in_byte":3420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"261139255","text":"\"\"\"An idea for a cell somewhat inspired by resnets/lstms\nbut incorporating a generalised two-way connection\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport tensorflow as tf\n\nfrom mrnn.handy_ops import *\nfrom mrnn.tensor_ops import *\n\nimport mrnn.init as init\n\n\ndef _tensor_logits(inputs, states, rank, weightnorm=None, pad=True,\n                   separate_pad=True, name='t_prod'):\n    \"\"\"Gets a tensor product of inputs and states, with a few options.\n\n    Args:\n        inputs: the inputs to the layer (or just the first input).\n        states: the previous hidden states (or just the second input).\n        rank: the rank of the decomposed tensor to use.\n        weightnorm: Whether or not (or indeed how) to normalise the weights.\n            Possible values are `None` for a totally unconstrained tensor,\n            'partial' for doing a minimal normalisation (with no extra\n            parameters)  which just returns each of the matrices divided by\n            its squared frobenius norm or 'classic' which does all of the\n            rows of all of the matrices and adds the necessary learnable\n            gain coefficients.\n        pad: whether or not to add biases to the tensor product.\n        separate_pad: whether or not to incorporate the bias matrices into\n            the decomposition. If true, we will have more parameters but we\n            will definitely be capable of exactly representing a classic RNN.\n\n    Returns:\n        tensor, the result.\n    \"\"\"\n    batch_size, state_size = states.get_shape().as_list()\n    input_size = inputs.get_shape()[1].value\n    if pad and not separate_pad:\n        # just add a column of ones to inputs and states\n        inputs = tf.concat(axis=1, values=[tf.ones([batch_size, 1]), inputs])\n        states = tf.concat(axis=1, values=[tf.ones([batch_size, 1]), states])\n        # the tensor now has to be a little bit of an odd shape\n        tensor = get_cp_tensor([state_size+1,\n                                state_size,\n                                input_size+1],\n                               rank,\n                               name,\n                               weightnorm=weightnorm)\n    else:\n        # the tensor is normal shape\n        tensor = get_cp_tensor([state_size,\n                                state_size,\n                                input_size],\n                               rank,\n                               name,\n                               weightnorm=weightnorm)\n    tensor_prod = bilinear_product_cp(states, tensor, inputs)\n\n    if pad and separate_pad:\n        # then we have to do these guys too\n        input_weights = possibly_weightnormed_var([input_size, state_size],\n                                                  weightnorm,\n                                                  name + 'input_weights')\n        state_weights = possibly_weightnormed_var([state_size, state_size],\n                                                  weightnorm,\n                                                  name + 'state_weights')\n        bias = tf.get_variable(name+'bias', dtype=tf.float32,\n                               shape=[state_size],\n                               initializer=tf.constant_initializer(0.0))\n        tensor_prod += tf.nn.bias_add(\n            tf.matmul(inputs, input_weights) +\n            tf.matmul(states, state_weights),\n            bias)\n    return tensor_prod\n\n\ndef _affine(data, new_size, weightnorm=None, name='affine'):\n    \"\"\"performs an affine transormation on the data using trainable, possibly weightnormed\n    variables\"\"\"\n    input_size = data.get_shape()[1].value\n    weights = possibly_weightnormed_var([input_size, new_size],\n                                        weightnorm,\n                                        name+'_weights')\n    bias = tf.get_variable(name+'_bias', dtype=tf.float32, shape=[new_size],\n                           initializer=tf.constant_initializer(0.0))\n    return tf.nn.bias_add(tf.matmul(data, weights), bias)\n\n\nclass FullTGUCell(tf.contrib.rnn.RNNCell):\n    \"\"\"Input gate, output gate\"\"\"\n\n    def __init__(self, num_units, rank, dropout=1.0, layernorm='',\n                 weight_noise=0.0, separate_pad=True,\n                 candidate_nonlin=tf.identity, v2=False):\n        self._num_units = num_units\n        self._rank = rank\n        self._dropout = dropout\n        self.layernorm = layernorm\n        self.weight_noise = weight_noise\n        self.separate_pad = separate_pad\n        self.candidate_nonlin = candidate_nonlin\n        self.v2 = v2\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, state, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            with tf.variable_scope('accumulator',\n                                   initializer=init.orthonormal_init(1.0)):\n                input_acts = _affine(inputs, self.state_size, name='input')\n                if 'pre' in self.layernorm and 'input' in self.layernorm:\n                    input_acts = layer_normalise(input_acts)\n                update = self.candidate_nonlin(input_acts)\n\n                if self._dropout != 1.0:\n                    update = variational_wrapper(\n                        update, keep_prob=self._dropout)\n\n            with tf.variable_scope('forgetter',\n                                   initializer=init.orthonormal_init(1.0)):\n                forget_acts = _tensor_logits(\n                    inputs, state, self.rank, pad=True,\n                    separate_pad=self.separate_pad,\n                    weightnorm=None)\n                if 'pre' in self.layernorm and 'forget' in self.layernorm:\n                    forget_acts = layer_normalise(forget_acts, add_bias=True,\n                                                  bias_initialiser=0.0)\n                forget_gate = tf.nn.sigmoid(forget_acts)\n\n            c_t = forget_gate * state + (1.0-forget_gate) * update\n\n            with tf.variable_scope('output',\n                                   initializer=init.orthonormal_init(1.0)):\n                if self.v2:\n                    output_acts = _tensor_logits(\n                        inputs, state, self.rank, pad=True,\n                        separate_pad=self.separate_pad, weightnorm=None)\n                else:\n                    output_acts = _tensor_logits(\n                        inputs, c_t, self.rank, pad=True,\n                        separate_pad=self.separate_pad, weightnorm=None)\n                output = tf.nn.sigmoid(output_acts)\n\n        return output, c_t\n\n\nclass CPGateCell(tf.contrib.rnn.RNNCell):\n    \"\"\"A forget gate and an accumulator\"\"\"\n\n    def __init__(self, num_units, rank, dropout=1.0, layernorm='',\n                 weight_noise=0.0, separate_pad=True,\n                 candidate_nonlin=tf.nn.relu):\n        self._num_units = num_units\n        self._rank = rank\n        self._dropout = dropout\n        self.layernorm = layernorm\n        self.weight_noise = weight_noise\n        self.separate_pad = separate_pad\n        self.candidate_nonlin = candidate_nonlin\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, state, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            with tf.variable_scope('accumulator',\n                                   initializer=init.orthonormal_init(1.0)):\n                input_acts = _affine(inputs, self.state_size, name='input')\n                hh_weights = tf.get_variable('hh', [self.state_size,\n                                                    self.state_size])\n                if 'pre' in self.layernorm and 'input' in self.layernorm:\n                    input_acts = layer_normalise(input_acts)\n                update = self.candidate_nonlin(input_acts)\n\n                # update = _tensor_logits(update, state, self.rank)\n                # update = tf.clip_by_value(input_acts*10, -1.0, 1.0)\n                if self._dropout != 1.0:\n                    # update = tf.nn.dropout(update, self._dropout)\n                    update = variational_wrapper(\n                        update, keep_prob=self._dropout)\n\n            with tf.variable_scope('forgetter',\n                                   initializer=init.orthonormal_init(1.0)):\n                forget_acts = _tensor_logits(\n                    inputs, state, self.rank, pad=True,\n                    separate_pad=self.separate_pad,\n                    weightnorm=None)\n                if 'pre' in self.layernorm and 'forget' in self.layernorm:\n                    forget_acts = layer_normalise(forget_acts, add_bias=True,\n                                                  bias_initialiser=0.0)\n                forget_gate = tf.nn.sigmoid(forget_acts)\n                # forget_gate = tf.clip_by_value((forget_acts+1)/2, 0.0, 1.0)\n\n            result = forget_gate * state + (1.0-forget_gate) * update\n\n        return result, result\n\n\nclass CPResCell(tf.contrib.rnn.RNNCell):\n    \"\"\"try something JIC\"\"\"\n\n    def __init__(self, num_units, num_inputs, rank):\n        self._num_units = num_units\n        self._num_inputs = num_inputs\n        self._rank = rank\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def input_size(self):\n        return self._num_inputs\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, states, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            # first combine the inputs and the state\n            with tf.variable_scope('pre_act', initializer=init.orthonormal_init(0.5)):\n                pre_activations = _tensor_logits(inputs, states, self.rank,\n                                                 weightnorm=None,\n                                                 pad=False,\n                                                 separate_pad=False,\n                                                 name='pre_act')\n                # layer norm with extra bias\n                pre_activations = layer_normalise(pre_activations, add_bias=True)\n            # make the cut\n            cut = tf.nn.relu(pre_activations)\n            # project back into weight pace\n            with tf.variable_scope('post_act', initializer=init.orthonormal_init(0.5)):\n                post_activations = _affine(pre_activations, self.state_size, name='cut_proj',\n                                           weightnorm=None)\n                # re-normalise\n                result = layer_normalise(post_activations)\n            # make the skip connection\n            result += states\n        return result, result\n\n\n\nclass CPDeltaCell(tf.contrib.rnn.RNNCell):\n    \"\"\"Upon which all hopes are pinned\"\"\"\n\n    def __init__(self, num_units, num_inputs, rank, weightnorm=None):\n        self._num_units = num_units\n        self._num_inputs = num_inputs\n        self._rank = rank\n        self.weightnorm = weightnorm\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def input_size(self):\n        return self._num_inputs\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, states, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            with tf.variable_scope('plus_tensor',\n                                   initializer=init.spectral_normalised_init(1.1)):\n                pos_tensor_prod = _tensor_logits(inputs, states, self.rank,\n                                                 weightnorm=self.weightnorm,\n                                                 pad=True,\n                                                 separate_pad=False,\n                                                 name='positive')\n\n                positive = tf.nn.relu(pos_tensor_prod)\n\n            with tf.variable_scope('minus_tensor',\n                                   initializer=init.spectral_normalised_init(1.1)):\n                neg_tensor_prod = _tensor_logits(inputs, states, self.rank,\n                                                 weightnorm=self.weightnorm,\n                                                 pad=True,\n                                                 separate_pad=False,\n                                                 name='negative')\n                negative = tf.nn.relu(neg_tensor_prod)\n\n            result = positive - negative + states\n        return result, result\n\n\n\nclass CPLossyIntegrator(tf.contrib.rnn.RNNCell):\n    \"\"\"Upon which all hopes are pinned\"\"\"\n\n    def __init__(self, num_units, num_inputs, rank, weightnorm=None):\n        self._num_units = num_units\n        self._num_inputs = num_inputs\n        self._rank = rank\n        self.weightnorm = weightnorm\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def input_size(self):\n        return self._num_inputs\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, states, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            # project the input into the hidden feature space\n            with tf.variable_scope('input_projection', initializer=tf.random_normal_initializer(stddev=0.01)):\n                proj_in = _affine(inputs, self.state_size, name='cut_shift')\n                proj_in = tf.nn.relu(proj_in)\n                proj_in = _affine(inputs, self.state_size, name='state_proj')\n            # compute via a bilinear product some parts of the memory to abrade\n            with tf.variable_scope('abrasion', initializer=init.spectral_normalised_init(1.5)):\n                forgetting = _tensor_logits(inputs, states, self.rank,\n                                            pad=True, separate_pad=False)\n                forgetting = tf.nn.relu(forgetting)\n                forgetting = _affine(forgetting, self.state_size)\n\n            # combine the information\n            result = tf.nn.relu(states + proj_in - forgetting)\n\n        return result, result\n\n\nclass CPSimpleIntegrator(tf.contrib.rnn.RNNCell):\n    \"\"\"Upon which all hopes are pinned\"\"\"\n\n    def __init__(self, num_units, num_inputs, rank, layernorm=False):\n        self._num_units = num_units\n        self._num_inputs = num_inputs\n        self._rank = rank\n        self.layernorm = layernorm\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def input_size(self):\n        return self._num_inputs\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, states, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            # project the input into the hidden feature space\n            with tf.variable_scope('input_projection'):\n                pre_acts = _affine(inputs, self.state_size)\n                if self.layernorm == 'pre':\n                    pre_acts = layer_normalise(pre_acts)\n                input_info = tf.nn.relu(pre_acts)\n            # compute via a bilinear product some parts of the memory to abrade\n            with tf.variable_scope('abrasion'):\n                neg_acts = _tensor_logits(inputs, states, self.rank, pad=False)\n                if self.layernorm == 'pre':\n                    neg_acts = layer_normalise(neg_acts, add_bias=True, bias_init=1.0)\n                combined = -tf.nn.relu(neg_acts)\n\n            if self.layernorm == 'post':\n                update = layer_normalise(input_info + combined)\n            else:\n                update = input_info + combined\n        result = tf.nn.relu(states + update)\n        return result, result\n\n\nclass AddSubCPCell(tf.contrib.rnn.RNNCell):\n    \"\"\"Basically difference between two of the below\"\"\"\n\n    def __init__(self, num_units, num_inputs, rank,\n                 input_projections=None,\n                 nonlinearity=tf.nn.relu,\n                 tensor_init=init.spectral_normalised_init(0.999)):\n        self._num_units = num_units\n        self._num_inputs = num_inputs\n        self._nonlinearity = nonlinearity\n        self._rank = rank\n        self._input_projection = input_projections or num_inputs\n        self._tensor_init = tensor_init\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def input_size(self):\n        return self._num_inputs\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, states, scope=None):\n        with tf.variable_scope(scope or type(self).__name__):\n            # get the tensors first\n            with tf.variable_scope('tensor_1',\n                                   initializer=self._tensor_init):\n\n                tensor_1 = get_cp_tensor([self.input_size,\n                                          self.output_size,\n                                          self.state_size],\n                                         self.rank,\n                                         'W1',\n                                         weightnorm='partial',\n                                         trainable=True)\n            with tf.variable_scope('tensor_2',\n                                   initializer=self._tensor_init):\n                tensor_2 = get_cp_tensor([self.input_size,\n                                          self.output_size,\n                                          self.state_size],\n                                         self.rank,\n                                         'W2',\n                                         weightnorm='partial',\n                                         trainable=True)\n            combo_1 = bilinear_product_cp(inputs, tensor_1, states)\n            combo_2 = bilinear_product_cp(inputs, tensor_2, states)\n\n            input_weights_1 = tf.get_variable('U1', shape=[self.input_size,\n                                                           self.state_size])\n            input_weights_2 = tf.get_variable('U2', shape=[self.input_size,\n                                                           self.state_size])\n            input_proj_1 = tf.matmul(inputs, input_weights_1)\n            input_proj_2 = tf.matmul(inputs, input_weights_2)\n            # biases\n            bias1 = tf.get_variable('b1', shape=[self.output_size],\n                                    initializer=tf.constant_initializer(0.0))\n            bias2 = tf.get_variable('b2', shape=[self.output_size],\n                                    initializer=tf.constant_initializer(0.0))\n            positive = self._nonlinearity(combo_1 + input_proj_1 + bias1)\n            # positive = tf.nn.l2_normalize(positive, 1)\n            negative = self._nonlinearity(combo_2 + input_proj_2 + bias2)\n            # negative = tf.nn.l2_normalize(negative, 1)\n            result = positive - negative + states\n        return result, result\n\n\nclass AdditiveCPCell(tf.contrib.rnn.RNNCell):\n    \"\"\"Uses a CP decomposition to factorise the tensor\"\"\"\n\n    def __init__(self, num_units, num_inputs, rank,\n                 input_projection=None,\n                 nonlinearity=tf.nn.relu, layernorm=False):\n        self._num_units = num_units\n        self._num_inputs = num_inputs\n        self._nonlinearity = nonlinearity\n        self._rank = rank\n        self._input_projection = input_projection or num_units\n        self.layernorm = layernorm\n\n    @property\n    def rank(self):\n        return self._rank\n\n    @property\n    def state_size(self):\n        return self._num_units\n\n    @property\n    def input_size(self):\n        return self._num_inputs\n\n    @property\n    def output_size(self):\n        return self._num_units\n\n    def __call__(self, inputs, states, scope=None):\n        with tf.variable_scope(scope or type(self).__name__) as outer_scope:\n            # do it\n            # sub scope for the tensor init\n            # should inherit reuse from outer scope\n            with tf.variable_scope('tensor',\n                                   initializer=init.orthonormal_init(0.5)):\n                tensor = get_cp_tensor([self.input_size,\n                                        self.output_size,\n                                        self.state_size],\n                                       self.rank,\n                                       'W',\n                                       weightnorm=False,\n                                       trainable=True)\n            combination = bilinear_product_cp(inputs, tensor, states)\n            # and project the input\n            input_weights = tf.get_variable('U', shape=[self.input_size,\n                                                        self._input_projection],\n                                            initializer=tf.uniform_unit_scaling_initializer(1.4))\n            input_proj = tf.matmul(inputs, input_weights)\n            # apply a bias pre-nonlinearity\n            bias = tf.get_variable('b', shape=[self.output_size],\n                                   initializer=tf.constant_initializer(0.0))\n            if self.layernorm == 'pre':\n                activations = layer_normalise(combination + input_proj + bias)\n            else:\n                activations = combination + input_proj + bias\n\n            result = self._nonlinearity(activations)\n\n            if self.layernorm == 'post':\n                result = layer_normalise(result)\n\n            result = result + states\n        return result, result\n","sub_path":"mrnn/additive_tensor_rnn.py","file_name":"additive_tensor_rnn.py","file_ext":"py","file_size_in_byte":21941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"191448838","text":"import config\nimport psycopg2\n\n\ncost_per_hour = \\\n    '(CAST(cost AS DOUBLE PRECISION) / (EXTRACT(epoch FROM period) / 3600))'\n\n\nclass SQLka:\n    def __init__(self):\n        self.conn = psycopg2.connect(database=config.db_name,\n                                     user=config.db_user,\n                                     password=config.db_password)\n        self.cur = self.conn.cursor()\n\n    def close(self):\n        self.conn.close()\n\n    def select(self, fields, tables, constraints, modifyers = []):\n        q_fields = ''\n        for i in range(0, len(fields)):\n            if i != 0:\n                q_fields += ',\\n'\n            q_fields += fields[i]\n\n        q_tables = ''\n\n        for i in range(0, len(tables)):\n            if i != 0:\n                q_tables += '\\nNATURAL JOIN '\n            q_tables += '{} {}'.format(tables[i][1], tables[i][0])\n\n        q_conditions = 'TRUE'\n        for c in constraints:\n            q_conditions += '\\nAND {} {}'.format(c[0], str(c[1]))\n        query = 'SELECT ' + ' '.join(modifyers) + '\\n' + q_fields + \\\n                '\\nFROM\\n' + q_tables + \\\n                '\\nWHERE\\n' + q_conditions\n        print(query)\n        self.cur.execute(query=query)\n        return self.cur.fetchall()\n\n    def insert(self, table, fields):\n        q_fields = ''\n        q_values = ''\n        for i in range(0, len(fields)):\n            if i != 0:\n                q_fields += ', '\n                q_values += ', '\n            q_fields += fields[i][0]\n            q_values += str(fields[i][1])\n        query = \"INSERT INTO {} {} VALUES {}\".format(\n            table,\n            '({})'.format(q_fields),\n            '({})'.format(q_values)\n        )\n        print(query)\n        self.cur.execute(query)\n        self.conn.commit()\n\n\ndef id_str(a):\n    return \"'{}'\".format(a) \\\n        if isinstance(a, str) and not a == 'NULL' \\\n        else str(a)\n\n\ndef rng(a, b):\n    return 'BETWEEN {} AND {}'.format(str(a), str(b))\n\n\ndef eq(a):\n    if a == 'NULL':\n        return 'IS NULL'\n    return '= {}'.format(str(a))\n\n\ndef like(a):\n    return \"SIMILAR TO '{}%'\".format(str(a))","sub_path":"bot/dbaccess.py","file_name":"dbaccess.py","file_ext":"py","file_size_in_byte":2101,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"162002102","text":"import sys\nsys.path.append(\"../..\")\nimport game\n\ndef saisieCoup(jeu):\n    best_score = -1000\n    for index, coup in enumerate(game.getCoupsValides(jeu)):\n        jeu_copie = game.getCopieJeu(jeu)\n        game.joueCoup(jeu_copie, coup)\n        score = alpha_beta(jeu_copie, 5, -128, 128, 1, coup)\n        if score > best_score:\n            best_score = score\n            best_coup = coup\n    return best_coup\n\ndef find_alignment(jeu, position, joueur):\n    x, y = position\n    plateau = jeu[0]\n    if joueur != game.getCaseVal(jeu, y, x):\n        return [0, 0, 0, 0]\n    res = [1, 1, 1, 1]\n    tmp_x, tmp_y = position\n    while 0 < tmp_x and tmp_y < 6 - 1:\n        tmp_x -= 1\n        tmp_y += 1\n        if game.getCaseVal(jeu, tmp_y, tmp_x) == joueur:\n            res[0] += 1\n        else:\n            break ;\n    tmp_x, tmp_y = position\n    while tmp_y < 6 - 1:\n        tmp_y += 1\n        if game.getCaseVal(jeu, tmp_y, tmp_x) == joueur:\n            res[1] += 1\n        else:\n            break ;\n    tmp_x, tmp_y = position\n    while tmp_x < 7 - 1 and tmp_y < 6 - 1:\n        tmp_x += 1\n        tmp_y += 1\n        if game.getCaseVal(jeu, tmp_y, tmp_x) == joueur:\n            res[2] += 1\n        else:\n            break ;\n    tmp_x, tmp_y = position\n    while tmp_x < 7 - 1:\n        tmp_x += 1\n        if game.getCaseVal(jeu, tmp_y, tmp_x) == joueur:\n            res[3] += 1\n        else:\n            break ;\n    return res\n\n\ndef evaluation(jeu):\n    if game.finJeu(jeu):\n        return 20 if game.getGagnant(jeu) else 0\n    joueur = game.getJoueur(jeu)\n    adv = (joueur - 1) % 2 + 1\n    score = game.getScores(jeu)\n    value = 0\n    adv_value = 0\n    for x, y in [ (x, y) for x in range(7) for y in range(6) ]:\n        value += sum(find_alignment(jeu, (x, y), joueur))\n    for x, y in [ (x, y) for x in range(7) for y in range(6) ]:\n        adv_value += sum(find_alignment(jeu, (x, y), adv))\n    return value - adv_value\n\n\ndef alpha_beta(jeu, profondeur, alpha, beta, color, best_coup):\n    if game.finJeu(jeu) or not profondeur:\n        res = evaluation(jeu)\n        return res * color\n    best_value = -128\n    for index, coup in enumerate(game.getCoupsValides(jeu)):\n        jeu_copie = game.getCopieJeu(jeu)\n        game.joueCoup(jeu_copie, coup)\n        score = -alpha_beta(jeu_copie, profondeur - 1, -beta, -alpha, -color, coup)\n        if score > best_value:\n            best_value = score\n            best_play = coup\n        if score >= alpha:\n            alpha = score\n        if alpha >= beta:\n            return alpha\n    return 0\n","sub_path":"projet/Puissance4/Joueurs/alphabeta_player_winner_2.py","file_name":"alphabeta_player_winner_2.py","file_ext":"py","file_size_in_byte":2543,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"154049026","text":"# Gaussian Mixture Model Notebook\n#import necessary packages\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.stats import multivariate_normal\nfrom sklearn.preprocessing import normalize\n\n\n#set variables\npatch_size = 30  # 60*60*3\nnum_training_images = 1000\nnum_testing_images = 100\n\n#Load training data\nindex = 0\ntraining_data_face = np.zeros((num_training_images, patch_size * patch_size))\ntraining_data_nonface = np.zeros((num_training_images, patch_size * patch_size))\ntesting_data_face = np.zeros((num_testing_images, patch_size * patch_size))\ntesting_data_nonface = np.zeros((num_testing_images, patch_size * patch_size))\nwhile index < num_training_images:\n    loaded_image_face = cv2.imread('Data//AFLW//extracted_faces//train//train_face' + str(index) + '.png',0);\n    loaded_image_nonface = cv2.imread('Data//AFLW//extracted_faces//train//non_face' + str(index) + '.jpg',0);\n    resized_image_face=cv2.resize(loaded_image_face,(patch_size,patch_size),cv2.INTER_LANCZOS4) #resize it if required\n    resized_image_nonface=cv2.resize(loaded_image_nonface,(patch_size,patch_size),cv2.INTER_LANCZOS4) #resize it if required\n    training_data_face[index] = np.reshape(resized_image_face, (1, -1))\n    training_data_nonface[index] = np.reshape(resized_image_nonface, (1, -1))\n    # print(index)\n    index += 1\n\n#load testing data\nindex=0\nwhile index < num_testing_images:\n    loaded_image_face_test = cv2.imread('Data//AFLW//extracted_faces//test//test_face' + str(index) + '.png',0);\n    loaded_image_nonface_test = cv2.imread('Data//AFLW//extracted_faces//test//non_face' + str(index+1000) + '.jpg',0);\n    resized_image_face=cv2.resize(loaded_image_face_test,(patch_size,patch_size),cv2.INTER_LANCZOS4) #resize it if required\n    resized_image_nonface=cv2.resize(loaded_image_nonface_test,(patch_size,patch_size),cv2.INTER_LANCZOS4) #resize it if required\n    testing_data_face[index] = np.reshape(resized_image_face, (1, -1))\n    testing_data_nonface[index] = np.reshape(resized_image_nonface, (1, -1))\n    # print(index)\n    index += 1\nprint(training_data_face.shape)\nprint(training_data_nonface.shape)\n\n\"\"\"\n#OPTIONAL-PCA for debugging and comparison\nfrom scipy import stats\nfrom scipy.stats import multivariate_normal\nfrom sklearn.decomposition import PCA\nnum_components=60\n\npca=PCA(n_components=num_components).fit(training_data_face)\npca_faces_train=pca.transform(training_data_face)\npca_faces_test=pca.transform(testing_data_face)\n\npca1=PCA(n_components=num_components).fit(training_data_nonface)\npca_nonfaces_train=pca.transform(training_data_nonface)\npca_nonfaces_test=pca.transform(testing_data_nonface)\n\"\"\"\n\nclass GMM:\n    \n    def __init__(self, k , eps):\n        self.k = k ## number of clusters\n        self.eps = eps ## threshold to stop `epsilon`\n        \n        # All parameters from fitting/learning are kept in a named tuple\n        from collections import namedtuple\n    \n    def _estimate_log_prob(self,X,mu,s):\n        \"\"\"\n        Returns the log probability\n        \"\"\"\n        #return ((-X.shape[1]/2)**np.log(2*np.pi)+0.5**np.log(np.linalg.det(s)))+(-.5 * np.einsum('ij, ij -> i',                                                                                    X - mu, np.dot(np.linalg.inv(s), (X - mu).T).T))\n    \n    def _estimate_log_weights(self,X):\n        \"\"\"\n        returns log of weights\n        \"\"\"\n    pass\n\n    def _estimate_weighted_log_prob(self,X,mu,s):\n        return self._estimate_log_prob(X) + self._estimate_log_weights()\n\n    def _estimate_log_prob_resp(self,X,mu,s):\n        weighted_log_prob = self._estimate_weighted_log_prob(X,mu,s)\n        log_prob_norm = logsumexp(weighted_log_prob, axis=1)\n        with np.errstate(under='ignore'):\n            # ignore underflow\n            log_resp = weighted_log_prob - log_prob_norm[:, np.newaxis]\n        return log_prob_norm, log_resp\n\n    def E_step(self,X,mu,s):\n        log_prob_norm, log_resp = self._estimate_log_prob_resp(X,mu,s)\n        return np.mean(log_prob_norm), log_resp\n        \n    \n    def fit(self,X,max_iters):\n        # Fitting the Model Using Expectation-Maximization Algorithm (EM)\n        \n        #Initializing parameters needed\n        \n        # n = number of data-points, d = dimension of data points        \n        n, d = X.shape\n        \n        # randomly choose the starting centroids/means \n        ## as 3 of the points from datasets        \n        mu = X[np.random.choice(n, self.k, False), :]\n        \n        # initialize the covariance matrices for each gaussians\n        Sigma= [np.eye(d)] * self.k\n        #Sigma=[np.cov(X,rowvar=False)]*self.k\n        #print(Sigma)\n        \n        # initialize the probabilities/weights for each gaussians\n        w = [1./self.k] * self.k # equal probability for each model to be chosen\n        \n        # responsibility matrix is initialized to all zeros\n        # we have responsibility for each of n points for eack of k gaussians\n        R = np.zeros((n, self.k))\n        \n        ### log_likelihoods\n        log_likelihoods = []\n        \n        #Now, EM\n        while len(log_likelihoods) < max_iters:\n            # E-Step\n            log_prob_norm,log_resp=_self.E_step(X,mu,Sigma)\n\n    \n\n    def fit_EM(self, X, max_iters):\n        \n        # n = number of data-points, d = dimension of data points        \n        n, d = X.shape\n        \n        # randomly choose the starting centroids/means \n        ## as 3 of the points from datasets        \n        mu = X[np.random.choice(n, self.k, False), :]\n        #print(mu)\n        # initialize the covariance matrices for each gaussians\n        Sigma= [np.eye(d)] * self.k\n        #Sigma=[np.cov(X,rowvar=False)]*self.k\n        #print(Sigma)\n        \n        # initialize the probabilities/weights for each gaussians\n        w = [1./self.k] * self.k # equal probability for each model to be chosen\n        \n        # responsibility matrix is initialized to all zeros\n        # we have responsibility for each of n points for eack of k gaussians\n        R = np.zeros((n, self.k))\n        \n        ### log_likelihoods\n        log_likelihoods = []\n        \n        #use pseudo-inverse to avoid singular matrix issues!!\n        \"\"\"\n        P = lambda mu, s: np.linalg.det(s) ** -.5 ** (2 * np.pi) ** (-X.shape[1]/2.) \\\n                * np.exp(-.5 * np.einsum('ij, ij -> i',\\\n                        X - mu, np.dot(np.linalg.inv(s) , (X - mu).T).T ) ) \n        \"\"\"\n\n        #P= lambda mu,s: ((-X.shape[1]/2)*np.log(2*np.pi)+0.5*np.log(np.linalg.det(s)))+(-.5 * np.einsum('ij, ij -> i',\\\n         #               X - mu, np.dot(np.linalg.inv(s) , (X - mu).T).T ))\n        P = lambda mu, s: (-0.5 * np.log(np.linalg.det(s))) - (0.5 * X - mu * (np.linalg.inv(s) * (X - mu).T).T)\n        # P= lambda mu,s: multivariate_normal.pdf(X,mean=mu,cov=s)\n        iteration=0\n        # Iterate till max_iters iterations        \n        while len(log_likelihoods) < max_iters:\n            print(\"Iteration\",iteration)\n            print(\"E Step\")\n            \n            # E - Step\n            \n            ## Vectorized implementation of e-step equation to calculate the \n            ## membership for each of k -gaussians\n            for k in range(self.k):\n                R[:, k] = w[k] * P(mu[k],Sigma[k]) #P(mu[k], Sigma[k])\n\n            ### Likelihood computation\n            log_likelihood = np.sum(np.log(np.sum(R, axis = 1)))\n            \n            log_likelihoods.append(log_likelihood)\n            \n            ## Normalize so that the responsibility matrix is row stochastic\n            R = (R.T / np.sum(R, axis = 1)).T\n            \n            ## The number of datapoints belonging to each gaussian            \n            N_ks = np.sum(R, axis = 0)\n            \n            print(\"M Step\")\n            # M Step\n            ## calculate the new mean and covariance for each gaussian by \n            ## utilizing the new responsibilities\n            for k in range(self.k):\n                \n                ## means\n                mu[k] = 1. / N_ks[k] * np.sum(R[:, k] * X.T, axis = 1).T\n                x_mu = np.matrix(X - mu[k])\n                \n                ## covariances\n                Sigma[k] = np.array(1 / N_ks[k] * np.dot(np.multiply(x_mu.T,  R[:, k]), x_mu))\n                \n                ## and finally the probabilities\n                w[k] = 1. / n * N_ks[k]\n            # check for convergence\n            if len(log_likelihoods) < 2 : continue\n            if np.abs(log_likelihood - log_likelihoods[-2]) < self.eps: break\n            iteration+=1\n        \n        ## bind all results together\n        from collections import namedtuple\n        self.params = namedtuple('params', ['mu', 'Sigma', 'w', 'log_likelihoods', 'num_iters'])\n        self.params.mu = mu\n        self.params.Sigma = Sigma\n        self.params.w = w\n        self.params.log_likelihoods = log_likelihoods\n        self.params.num_iters = len(log_likelihoods)       \n        \n        return self.params\n    \n    def plot_log_likelihood(self):\n        import pylab as plt\n        plt.plot(self.params.log_likelihoods)\n        plt.title('Log Likelihood vs iteration plot')\n        plt.xlabel('Iterations')\n        plt.ylabel('log likelihood')\n        plt.show()\n    \n    def predict(self, x):\n        p = lambda mu, s : np.linalg.det(s) ** - 0.5 * (2 * np.pi) **\\\n                (-len(x)/2) * np.exp( -0.5 * np.dot(x - mu , \\\n                        np.dot(np.linalg.pinv(s) , x - mu)))\n        probs = np.array([w * p(mu, s) for mu, s, w in \\\n            zip(self.params.mu, self.params.Sigma, self.params.w)])\n        return probs/np.sum(probs)\n        \nfrom sklearn.mixture import GaussianMixture\n\nX1=training_data_face[0:5,0:5]\nX1+=10**-6\n#X1=(X1-np.mean(X1,axis=0))/255.0\nX1=normalize(X1,axis=0,norm='l2')\n#cov_training=np.cov(X1,rowvar=False)\n#print(cov_training)\n#X1=(X1-np.min(X1))/255.0\n#noise = np.random.normal(10, 10, X1.shape)\n#X1+=noise\nprint(X1)\n#X1=training_data_face # should be 1000*3600\n#print(np.linalg.norm(X1))\n#X1=X1/np.linalg.norm(X1)\nprint(X1.shape)\n\nnum_gmm_components=1\neps=0.001\nmax_iterations=1000\ngmm=GMM(num_gmm_components,eps) #initialize model\nparams=gmm.fit_EM(X1,max_iterations) #train the model\ngmm.plot_log_likelihood() #plot the log-likelihood curve\n\n\n#GMM_sk=GaussianMixture(num_gmm_components)\n#GMM_sk.fit(X1)","sub_path":"ggmmm.py","file_name":"ggmmm.py","file_ext":"py","file_size_in_byte":10295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"185514918","text":"\nimport os.path\n\n#from ems.qt4.application import MainApplication\nfrom ems.qt5.qmlwidget_app import App\nfrom ems.support.bootstrappers.validation import ValidationBootstrapper\n\n#from examples.qt4.bootstrap.scaffolding import ScafffoldingBootstrapper\n\napp_path = os.path.abspath(os.path.join(os.path.dirname(__file__),'..','..','..'))\n\ndef create_app(argv, appPath, env=None):\n\n    env = 'production' if env is None else env\n\n    app = App(argv, appPath)\n    app.setQuitOnLastWindowClosed(True)\n\n    app.addBootstrapper(ValidationBootstrapper())\n    #app.addBootstrapper(ScafffoldingBootstrapper())\n\n    return app","sub_path":"examples/qt5/bootstrap/create_app.py","file_name":"create_app.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"397588651","text":"def solve(n):\n    if n < 0:\n        return False\n    original = str(abs(n))\n    i = 0\n    j = len(original)-1\n    while i < j:\n        if original[i] != original[j]:\n            return False\n        i += 1\n        j -= 1\n    return True\n\n\nif __name__ == '__main__':\n    n = int(input())\n    print(solve(n))","sub_path":"Code/CodeRecords/2154/60696/281470.py","file_name":"281470.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"77843565","text":"from mobileETLlib import createShortcut\n\nif __name__ == '__main__':\n    import csv, os\n    \n    photo_point = open (r'P:\\GIS\\Columbus\\Clintonville\\report_0829\\photo_point.csv', 'rb')\n    preader = csv.DictReader(photo_point)\n\n    for row in preader:\n        path = os.path.join(os.path.split(row[\"filename\"])[0], \"%s_%s.lnk\"% (row['building_id'], row['b_id']))\n        createShortcut(path, row[\"filename\"])\n","sub_path":"MakePhotoShortcuts.py","file_name":"MakePhotoShortcuts.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"296409574","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Fri Jun 19 18:31:58 2020\r\n\r\n@author: Pierre-Marie EDLINGER\r\n\"\"\"\r\nimport FonctionsFichier as FF\r\nimport LigneParMateriel as LM\r\n\r\n# Initialisation des variables globales\r\nNOMFICHIERRESULTAT = \"Resultat\"\r\nEXTENSIONFICHIER = \".ods\"\r\nfichierResultat = None\r\nXXX = \"\" \r\nxIP = \"\"\r\nxVille = \"\"\r\nxSite = \"\"\r\nxContact = \"\"\r\nxRD = \"\"\r\nxRA = \"\"\r\nxDate = \"\"\r\nNBCLS = \"\"\r\nNBMONNAYEUR = \"\"\r\nNBIMPRSTICKERS = \"\"\r\ndebutNouveauFichier = True\r\n\r\n\r\ndef enteteColonnes(fichier):\r\n    \"\"\" Insère la première ligne du fichier résultat\r\n        ENTREE : rien\r\n        SORTIE : rien\r\n        >>> à adapter au format du fichier attendu dans IBM Lotus \"\"\"\r\n\r\n    #écriture de la 1ère ligne des noms de colonne au format attendu dans Lotus\r\n    FF.ecritTexteDansFichier(fichier, \"id du document;Nom du matériel;Description;\"\r\n                             + \"Etat de la fiche;lien vers la photo;\"\r\n                             + \"Statut de la fiche;Nom du matériel de backup;\"\r\n                             + \"Pays impacté;Composant matériel;Marque;Modèle;\"\r\n                             + \"Type de matériel;Numéro de série;Technologie;\"\r\n                             + \"Pays d'hébergement;Ville;Site;Nom du site;\"\r\n                             + \"Localisation;Emplacement;Contact sur le site;\"\r\n                             + \"Affecté à;Composant hébergé;Criticité;RD;RA;\"\r\n                             + \"Société propriétaire;Pays propriétaire;\"\r\n                             + \"Date d'entrée;Date de mise à jour;Date de fin;\"\r\n                             + \"Type de contrat d'assistance;Nom prestataire;\"\r\n                             + \"Numéro de contrat;Date de début du contrat;\"\r\n                             + \"Date de fin du contrat;Réseau : ip;Sous-réseau;\"\r\n                             + \"passerelle;Adresse Mac;Supervision;Accessibilité;\"\r\n                             + \"Fournisseur;Numéro de commande;Coût de l'achat;\"\r\n                             + \"Numéro de la facture;Valeur;Bon de livraison;\"\r\n                             + \"Date de la commande;Date de livraison;\"\r\n                             + \"Type escalade N2;Escalade N2;Escalade N3;\"\r\n                             + \"Attribut 1;Attribut 2;Attribut 3;Attribut 4;\"\r\n                             + \"Attribut 5;Attribut 6;Attribut 7;Attribut 8;\"\r\n                             + \"Attribut 9;Attribut 10;Attribut 11;Attribut 12;\"\r\n                             + \"Attribut 13;Attribut 14;Attribut 15;Attribut 16;\"\r\n                             + \"Attribut 17;Attribut 18;Attribut 19;Attribut 20;\"\r\n                             + \"Attribut 21;Attribut 22;Attribut 23;Attribut 24;\"\r\n                             + \"Attribut 25;Attribut 26;Attribut 27;Attribut 28;\"\r\n                             + \"Attribut 29;Attribut 30\")\r\n\r\n\r\n### Début programme **********************************************************\r\n    \r\n# on remplit une matrice avec toutes les lignes du fichier de travail\r\nmaMatrice = FF.getRensDansFichier(\"FichierDeTravail.csv\")\r\n\r\n# pour toutes les lignes de la matrice\r\nfor ligneFichier in range (1, len(maMatrice)):\r\n    \r\n    # s'il ne s'agit pas d'une ligne vide\r\n    if maMatrice[ligneFichier][0] != \"\" :\r\n        \r\n        #si on est au début d'un nouveau fichier\r\n        if debutNouveauFichier == True :\r\n            # on indique que l'on ne sera plus à la création d'un nouveau fichier\r\n            debutNouveauFichier = False\r\n            # on crée et ouvre un nouveau fichier qui portera\r\n            #le numéro du 1er magasin à traiter\r\n            fichierResultat = open (NOMFICHIERRESULTAT+maMatrice[ligneFichier][0]+EXTENSIONFICHIER, \"w\")    # Création et ouverture du fichier rempli\r\n            \r\n            # on rempli l'entête des colonnes\r\n            enteteColonnes(fichierResultat)\r\n        # else:\r\n        #     #on rajoute au fichier\r\n        #     fichierResultat = open (NOMFICHIERRESULTAT+maMatrice[ligneFichier][0]+EXTENSIONFICHIER, \"a\")\r\n        \r\n        #On initialise tous les paramètres\r\n        XXX = maMatrice[ligneFichier][0] \r\n        xIP = maMatrice[ligneFichier][1]\r\n        xVille = maMatrice[ligneFichier][2]\r\n        xSite = maMatrice[ligneFichier][3]\r\n        xContact = maMatrice[ligneFichier][4] \r\n        xRD = maMatrice[ligneFichier][5]\r\n        xRA = maMatrice[ligneFichier][6]\r\n        xDate = maMatrice[ligneFichier][7]\r\n        NBCLS = maMatrice[ligneFichier][8]\r\n        NBMONNAYEUR = maMatrice[ligneFichier][9]\r\n        NBIMPRSTICKERS = maMatrice[ligneFichier][10]\r\n        \r\n        # on s'occupe ici de chaque CLS et de tous leurs matériels\r\n        for i in range(int(NBCLS)) :\r\n            numero = 21+i # le numéro des CLS commence toujours par 21\r\n            \r\n            #UC dans CLS\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getCLS(XXX, str(numero), xIP, xVille, xSite, \r\n                                               xContact, xRD, xRA, xDate))\r\n            #Scanners\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getScanner(XXX, str(numero), xVille, xSite, \r\n                                                   xContact, xRD, xRA, xDate))\r\n            #Imprimantes M30\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getImprimanteM30(XXX, str(numero), xVille, \r\n                                                        xSite, xContact, xRD, \r\n                                                        xRA, xDate))\r\n        \r\n            #Mâts\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getMat(XXX, str(numero), xVille, xSite, \r\n                                               xContact, xRD, xRA, xDate))\r\n            \r\n            #Balances\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getBalance(XXX, str(numero), xVille, xSite, \r\n                                               xContact, xRD, xRA, xDate))\r\n                             \r\n            #Ecrans\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getEcran(XXX, str(numero), xVille, xSite, \r\n                                               xContact, xRD, xRA, xDate))\r\n        \r\n        #Monnayeurs\r\n        numCLS = 21 # sert à déterminer entre quelles CLS sont placés les monnayeurs\r\n        for i in range(int(NBMONNAYEUR)) :\r\n            numero = 45+i\r\n            numCLS1 = numCLS\r\n            numCLS2 = numCLS1 + 1\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getMonnayeur(XXX, str(numero), \r\n                                                     str(numCLS1), str(numCLS2), \r\n                                                     xIP, xVille, xSite, \r\n                                                     xContact, xRD, xRA, xDate))\r\n            numCLS = numCLS2 + 1\r\n        \r\n        #Imprimantes pour les stickers\r\n        for i in range(int(NBIMPRSTICKERS)) :\r\n            numero = 1+i\r\n            FF.ecritTexteDansFichier(fichierResultat, \r\n                                     LM.getImprimanteStickers(XXX, str(numero), \r\n                                                              xVille, xSite, \r\n                                                              xContact, xRD, \r\n                                                              xRA, xDate))    \r\n            \r\n    else : # si la 1ère cellule de la ligne n'est pas vide\r\n        # on indique que l'on ne sera plus à la création d'un nouveau fichier\r\n        debutNouveauFichier = True\r\n        fichierResultat.close ()  # fermeture du fichier\r\n    \r\n    # Si on a atteind la fin du fichier\r\n    if ligneFichier == len(maMatrice)-1 :  \r\n        fichierResultat.close ()  # fermeture du fichier\r\n\r\n### Fin programme **********************************************************","sub_path":"ProgCreationFichierCLS.py","file_name":"ProgCreationFichierCLS.py","file_ext":"py","file_size_in_byte":7983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"399721294","text":"import random\r\n\r\ndef move(info, ctx=None):\r\n    map = info[\"map\"]\r\n    x = info[\"x\"]\r\n    y = info[\"y\"]\r\n    height = len(map)\r\n    width = len(map[0])\r\n\r\n    def is_into(x, y):\r\n        return 0 <= x < width and 0 <= y < height\r\n\r\n    def is_free(x, y):\r\n        if not is_into(x, y):\r\n            return False\r\n\r\n        return map[y][x] != 1\r\n    moves = [(0, -1), (0, 1), (-1 , 0), (1, 0)]\r\n\r\n    while True:\r\n        move = random.randint(0, 3)\r\n        if is_free(x+moves[move][0], y+moves[move][1]):\r\n            return move\r\n","sub_path":"players/chuck.py","file_name":"chuck.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"319472626","text":"import datetime\nfrom shutil import copyfile\n\nlog_list = []\n#x=0\n\ndef log(log_text):\n    global log_list\n    log_list.append(str(datetime.datetime.now()) + '  :  ' + str(log_text) + '\\r\\n')\n\ndef flush(file_name):\n    global log_list\n    tempFile = '/home/pi/logs/' + file_name + ' ' + str(datetime.datetime.now() + '.txt')\n    #tempFile = file_name\n    file = open(tempFile, \"w\")\n    file.close()\n    x=0\n    file = open(tempFile, \"a\")\n    while(x < len(log_list)):\n        file.write(log_list[x])\n        x=x+1\n    file.close()\n\n#while(x<20):\n#    x = x+1\n#    hello = \"hello\"\n#    log(hello)\n\n#flush('log.txt')","sub_path":"Code/Navigation/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"619731257","text":"\"\"\"\nwkb solution for coulomb function\n2015/12/7\n\"\"\"\n\nimport cmath\nimport numpy as np\nfrom scipy.special import gamma\n\n\ndef coulomb_phase_shift(k, L):\n    return cmath.phase(gamma(1.0 + L - 1.0j/k))\n\ndef wkb_local_momentum(k, L, r):\n    c = 1.0 * L * (L + 1)\n    return np.sqrt(k**2 + 2.0/r - c/(r**2))\n\ndef wkb_zeta(k, L, r):\n    p = wkb_local_momentum(k, L, r)\n    c = 1.0*L*(L+1)\n    term1 = 1.0/(4.0*p**3)*(3.0*c/(r**4)-2.0/(r**3))\n    term2 = 5.0/(8.0*p**5)*(1.0/(r*r)-c/(r**3))**2\n    return p + term1 + term2\n\ndef wkb_zeta_prime(k, L, r):\n    L = 1.0 * L\n    return -(3.0*L*(L + 1)/r**5 - 3.0/(2*r**4))/(-L*(L + 1)/r**2 + k**2 + 2/r)**(3.0/2.0) + 5.0*(-12*L*(L + 1)/r**4 + 8/r**3)*(2*L*(L + 1)/r**3 - 2/r**2)/(32*(-L*(L + 1)/r**2 + k**2 + 2/r)**(5.0/2.0)) - (-3*L*(L + 1)/(4*r**4) + 1/(2*r**3))*(-3*L*(L + 1)/r**3 + 3/r**2)/(-L*(L + 1)/r**2 + k**2 + 2/r)**(5.0/2.0) + 5*(-5*L*(L + 1)/r**3 + 5/r**2)*(2*L*(L + 1)/r**3 - 2/r**2)**2/(32*(-L*(L + 1)/r**2 + k**2 + 2/r)**(7.0/2.0)) + (L*(L + 1)/r**3 - 1/r**2)/np.sqrt(-L*(L + 1)/r**2 + k**2 + 2/r)\n\ndef wkb_phi(k, L, r):\n    p = wkb_local_momentum(k, L, r)\n    if L == 0:\n        phi0 = p*r + 1.0/k*np.log(2+2*k*k*r+2*p*k*r)\n        term1 = (9+6*k**2*r+2*k**4*r**2)/(24*p**3*r**2)\n        return phi0 + term1\n    else:\n        c = 1.0 * L * (L + 1)\n        phi0 = p*r - np.sqrt(c)*np.arctan((r-c)/(np.sqrt(c)*r*p)) + 1.0/k*np.log(2+2*k*k*r+2*p*k*r)\n        term1 = - (c + 2*c**2*k**2 + 3*c*k**4*r**2 + 3*r + 6*k**2*r**2 + k**4*r**3) / (24.0*r**3*p**3*(1+c*k*k))\n        term2 = - 1.0/(8*np.sqrt(c)) * np.arctan((r-c)/(np.sqrt(c)*p*r))\n        return phi0 + term1 + term2\n\ndef wkb_delta_and_A(k, L, r, psi, d_psi):\n    zeta = wkb_zeta(k, L, r)\n    zeta_prime = wkb_zeta_prime(k, L, r)\n    arg_arctan = zeta*psi/(d_psi + psi*zeta_prime/(2.0*zeta))\n    delta = np.arctan(arg_arctan)\n    A = np.sqrt(zeta) * psi / np.sin(delta)\n    return (delta, A)\n\ndef wkb_func(k, L, r_match, psi, d_psi):\n    (delta, A) = wkb_delta_and_A(k, L, r_match, psi, d_psi)\n    phi0 = wkb_phi(k, L, r_match)\n    def __func__(r):\n        phi = wkb_phi(k, L, r) - phi0\n        zeta = wkb_zeta(k, L, r)\n        return A * zeta**(-0.5) * np.sin(phi+delta)\n    return __func__\n\ndef wkb_phase_shift(k, L, r, psi, d_psi, r_asym):\n    (delta, A) = wkb_delta_and_A(k, L, r, psi, d_psi)\n    phi = wkb_phi(k, L, r_asym) - wkb_phi(k, L, r)\n    phase_shift = phi + delta - k*r_asym - (1.0/k)*np.log(2.0*k*r_asym) + L*np.pi/2.0\n\n    if A > 0.0:\n        return phase_shift % (2*np.pi)\n    else:\n        return (phase_shift + np.pi) % (2*np.pi)\n    \ndef phase_shift_by_coulomb_func(psi_r, r, r_match, k):\n    \"\"\"\n    psi_r: caluculated wave function written in sympy symbol r\n    r:     sympy symbol used in psi_r\n    r_match: matching point \n    k:       wave number\n    \"\"\"\n    \n","sub_path":"src_py/coulomb/wkb.py","file_name":"wkb.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"33223002","text":"'''Write a program that prints the numbers from 1 to 100.\nBut for multiples of three print \"Fizz\" instead of the number\nand for the multiples of five print \"Buzz\".\nFor numbers which are multiples of both three and five print \"FizzBuzz\".'''\n\nimport sys\n\nif __name__ == '__main__':\n\tfor i in xrange(1, 101):\n\t\tinit_str = str(i)\n\t\tif i % 3 == 0:\n\t\t\tsys.stdout.write(\"Fizz\")\n\t\t\tinit_str = \"\"\n\t\tif i % 5 == 0:\n\t\t\tsys.stdout.write(\"Buzz\")\n\t\t\tinit_str = \"\"\n\t\tsys.stdout.write(init_str + '\\n')\n","sub_path":"python/fizz_buzz.py","file_name":"fizz_buzz.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"83229482","text":"\"\"\"\nThis app creates a simple sidebar layout using inline style arguments and the\ndbc.Nav component.\n\ndcc.Location is used to track the current location, and a callback uses the\ncurrent location to render the appropriate page content. The active prop of\neach NavLink is set automatically according to the current pathname. To use\nthis feature you must install dash-bootstrap-components >= 0.11.0.\n\nFor more details on building multi-page Dash applications, check out the Dash\ndocumentation: https://dash.plot.ly/urls\n\"\"\"\nimport dash\nimport dash_bootstrap_components as dbc\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash.dependencies import Input, Output\nimport uuid\n\n# Defining the Session Id\nsession_id = str(uuid.uuid4())\n\napp = dash.Dash(external_stylesheets=[dbc.themes.BOOTSTRAP])\n\n# the style arguments for the sidebar. We use position:fixed and a fixed width\nSIDEBAR_STYLE = {\n    \"position\": \"fixed\",\n    \"top\": 0,\n    \"left\": 0,\n    \"bottom\": 0,\n    \"width\": \"16rem\",\n    \"padding\": \"2rem 1rem\",\n    # \"background-color\": \"#f8f9fa\",\n    \"background-color\": \"purple\",\n}\n\nTOPBAR_STYLE = {\n    \"position\": \"fixed\",\n    \"top\": 0,\n    # \"left\": \"16rem\",\n    \"right\":0,\n    # \"bottom\": 0,\n    # \"background-color\": \"#f8f9fa\",\n    \"background-color\": \"yellow\",\n}\n\n# the styles for the main content position it to the right of the sidebar and\n# add some padding.\nCONTENT_STYLE = {\n    \"margin-left\": \"18rem\",\n    \"margin-right\": \"2rem\",\n    \"padding\": \"2rem 1rem\",\n}\n\ntopbarNav = dbc.Nav(\n    [\n        dbc.NavItem(dbc.NavLink(\"Active\", active=True, href=\"#\")),\n        dbc.NavItem(dbc.NavLink(\"A link\", href=\"#\")),\n        dbc.NavItem(dbc.NavLink(\"Another link\", href=\"#\")),\n        dbc.NavItem(dbc.NavLink(\"Disabled\", disabled=True, href=\"#\")),\n        dbc.DropdownMenu(\n            [dbc.DropdownMenuItem(\"Item 1\"), dbc.DropdownMenuItem(\"Item 2\")],\n            label=\"Dropdown\",\n            nav=True,\n        ),\n    ]\n, style=TOPBAR_STYLE)\n\ntopbar = html.Div([topbarNav])\n\nsidebar = html.Div(\n    [\n        html.H2(\"Sidebar\", className=\"display-4\"),\n        html.Hr(),\n        html.P(\n            \"A simple sidebar layout with navigation links\", className=\"lead\"\n        ),\n        dbc.Nav(\n            [\n                dbc.NavLink(\"Home\", href=\"/\", active=\"exact\"),\n                dbc.NavLink(\"Page 1\", href=\"/page-1\", active=\"exact\", id=\"page1\"),\n                dbc.NavLink(\"Page 2\", href=\"/page-2\", active=\"exact\"),\n            ],\n            vertical=True,\n            pills=True,\n        ),\n        dbc.Button(id=\"disableMe\",children=\"\", className=\"ml-2\"),\n        html.Hr(),\n        dbc.Button(children=\"button_to_disable\", id=\"buttonToDisable\"),\n        html.Hr(),\n        dbc.Button(children=\"TakeMe out of here\", id=\"takeMeOutOfHere\")\n    ],\n    style=SIDEBAR_STYLE,\n)\n\ncontent = html.Div(id=\"page-content\", style=CONTENT_STYLE)\n\napp.layout = html.Div([dcc.Location(id=\"url\"), sidebar, topbar, content, html.Div(id='hiddenContent')])\n\n\n@app.callback(\n    Output(\"page-content\", \"children\"),\n    [Input(\"url\", \"pathname\")])\ndef render_page_content(pathname):\n    if pathname == \"/\":\n        return html.P(f\"This is the content of the home page! And the session id is {session_id} \")\n    elif pathname == \"/page-1\":\n        return html.P(f\"This is the content of page 1. Yay! And the session id is {session_id}\")\n        # return sidebar\n    elif pathname == \"/page-2\":\n        return html.P(f\"Oh cool, this is page 2! And the session id is {session_id}\")\n    # If the user tries to reach a different page, return a 404 message\n    return dbc.Jumbotron(\n        [\n            html.H1(\"404: Not found\", className=\"text-danger\"),\n            html.Hr(),\n            html.P(f\"The pathname {pathname} was not recognised...\"),\n        ]\n    )\n\n# @app.callback(\n#     Output(\"disableMe\", \"children\"),\n#     Input(\"buttonToDisable\", \"n_clicks\"),\n#     prevent_initial_call=True\n# )\n# def disable_button(nclicks):\n#     if nclicks==0:\n#         return \"\"\n#     return \"Click to view DQ results\"\n\n#\n@app.callback(\n    Output(\"page1\", \"disabled\"),\n    Input(\"buttonToDisable\", \"n_clicks\"),\n    prevent_initial_call=True\n)\ndef disable_button(nclicks):\n    if nclicks==0:\n        return False\n    return True\n\n#\napp.clientside_callback(\n    \"\"\"\n    function(nClicks) {\n        if (nClicks>0) {\n            location.replace(\"http://11.15.93.81:8000/notes\")\n            }\n        return [\"\"]\n    }\n    \"\"\",\n    Output('hiddenContent', 'children'),\n    Input('takeMeOutOfHere', 'n_clicks')\n)\n\n\nif __name__ == \"__main__\":\n    app.run_server(debug=True)","sub_path":"imp_side_bar.py","file_name":"imp_side_bar.py","file_ext":"py","file_size_in_byte":4582,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"569560898","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Aug 23 23:04:26 2020\n\n@author: ZhaoJingjiao\n\"\"\"\n\nclass Solution:\n    def lemonadeChange(self, bills: List[int]) -> bool:\n        five, ten = 0, 0\n        for bill in bills:\n            if bill == 5:\n                five += 1\n            elif bill == 10:\n                if five < 1: return False\n                five -= 1\n                ten += 1\n            else:\n                if ten and five:\n                    ten -= 1\n                    five -= 1\n                elif five > 2:\n                    five -= 3\n                else: return False\n        return True\n# Time Complexity: O(N)\n# Space Complexity: O(1)","sub_path":"Week_04/lemonade-change.py","file_name":"lemonade-change.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"503029490","text":"#!/usr/local/bin/python3.8\n#Three Digits and Print all Possible Combinations from the Digits\nnum1=int(input(\"Please enter the First Number: \"))\nnum2=int(input(\"Please enter the Second Number: \"))\nnum3=int(input(\"Please enter the Third Number: \"))\nd=[]\nd.append(num1)\nd.append(num2)\nd.append(num3)\nfor i in range(0,3):\n    for j in range(0,3):\n        for k in range(0,3):\n            if (i!=j&j!=k&k!=i):\n                print (d[i],d[j],d[k])\n#Two Digits and Print all Possible Combinations from the Digits\nnum1=int(input(\"Enter first number: \"))\nnum2=int(input(\"Enter second number:\"))\nList=[]\nList.append(num1)\nList.append(num2)\nfor l in range(0,2):\n    for m in range(0,2):\n        if (l!=m&m!=l):\n            print (List[l],List[m])\n","sub_path":"python_10082020/AllCombinations.py","file_name":"AllCombinations.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"3268948","text":"from .base import BaseNetwork as base\nimport tensorflow as tf\n\nclass mstn_train_net(base):\n    def __init__(self,cfg, trainable=True):\n        self.inputs = []\n        self._cfg = cfg\n\n\n        self.img = tf.placeholder(tf.float32, shape=[None, None, None, 3], name='img')\n        self.corner_data = tf.placeholder(tf.float32, shape=[None, None, None, 4], name='corner_data')\n        self.img_info = tf.placeholder(tf.float32, shape=[None, None], name='img_info')\n        self.resize_info = tf.placeholder(tf.float32, shape=[None, None], name='resize_info')\n        self.segmentation_mask = tf.placeholder(tf.float32, shape=[None, None, self._cfg.COMMON.RESIZE_HEIGHT,\n                                                                   self._cfg.COMMON.RESIZE_WIDTH],\n                                                name='segmentation_mask')\n\n        self.layers = dict({\n            'img': self.img,\n            'corner_data': self.corner_data,\n            'img_info': self.img_info,\n            'resize_info': self.resize_info,\n            'segmentation_mask': self.segmentation_mask\n        })\n        self.trainable = trainable\n        self.setup()\n\n\n    def setup(self):\n        # 详细查询vgg16的参数\n        (self.feed('img')\n         .conv(3, 3, 64, 1, 1, name='conv1_1')\n         .conv(3, 3, 64, 1, 1, name='conv1_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool1')\n         .conv(3, 3, 128, 1, 1, name='conv2_1')\n         .conv(3, 3, 128, 1, 1, name='conv2_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool2')\n         .conv(3, 3, 256, 1, 1, name='conv3_1')\n         .conv(3, 3, 256, 1, 1, name='conv3_2')\n         .conv(3, 3, 256, 1, 1, name='conv3_3')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool3')\n         .conv(3, 3, 512, 1, 1, name='conv4_1')\n         .conv(3, 3, 512, 1, 1, name='conv4_2')\n         .conv(3, 3, 512, 1, 1, name='conv4_3')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool4')\n         .conv(3, 3, 512, 1, 1, name='conv5_1')\n         .conv(3, 3, 512, 1, 1, name='conv5_2')\n         .conv(3, 3, 512, 1, 1, name='conv5_3')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool5'))\n\n        ############# 以上为VGG16前五层 ####################################\n\n        '''\n         after conv5 follow by conv6 conv7 conv8 conv9 conv10 conv11\n        '''\n        # TODO 确定这些卷积核的大小 通道数 和池化层\n        (self.conv(3, 3, 1024, 1, 1, name='conv6_1')\n         .conv(3, 3, 1024, 1, 1, name='conv6_2')\n         # TODO 根据论文 conv6 和conv7之间没有池化层\n         .conv(3, 3, 1024, 1, 1, name='conv7_1')\n         .conv(3, 3, 1024, 1, 1, name='conv7_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool7')\n         .conv(3, 3, 1024, 1, 1, name='conv8_1')\n         .conv(3, 3, 1024, 1, 1, name='conv8_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool8')\n         .conv(3, 3, 1024, 1, 1, name='conv9_1')\n         .conv(3, 3, 1024, 1, 1, name='conv9_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool9')\n         .conv(3, 3, 1024, 1, 1, name='conv10_1')\n         .conv(3, 3, 1024, 1, 1, name='conv10_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='pool10')\n         .conv(3, 3, 1024, 1, 1, name='conv11_1')\n         .conv(3, 3, 1024, 1, 1, name='conv11_2')\n         .max_pool(2, 2, 2, 2, padding='VALID', name='f11'))\n\n        # TODO 每个f层需要有自己的输出 用于打分和回归\n\n        # TODO 前一个参数是deconv中的红色层，后一个是蓝色层\n        deconv_module_list = ['f10', 'f9', 'f8', 'f7', 'f4', 'f3']\n\n        # TODO 每个feature map上的scale\n        f_scales = {\n            'f11': [184, 208, 232, 256],\n            'f10': [124, 136, 148, 160],\n            'f9': [88, 96, 104, 112],\n            'f8': [56, 64, 72, 80],\n            'f7': [36, 40, 44, 48],\n            'f4': [20, 24, 28, 32],\n            'f3': [4, 8, 6, 10, 12, 16],\n        }\n\n        for deconv_m in deconv_module_list:\n            self.deconv_module(name=deconv_m)\n\n            self.predict_module(name=deconv_m + '_pred')\n            # TODO 将预测层的N H W 1024 输出送入全连接层准备输出\n\n            # 第一个参数是特征图的channel数目\n            # 每个f层都需要输出预测目标\n\n            # TODO 特征图每个像素 输出k * q * 2个预测得分 deconv_fc 输出 (N,H,W,k * q * 2)\n            self.feed(deconv_m + '_pred').deconv_fc(512, 1, name=deconv_m + '_corner_pred_score')\n            # TODO 特征图每个像素 输出k * q * 4个预测目标 deconv_fc 输出 (N,H,W,k * q * 4)\n            self.feed(deconv_m + '_pred').deconv_fc(512, 4, name=deconv_m + '_corner_pred_offset')\n\n            # TODO 需要计算出feat_stride 即在每个f层上一个像素点对应多少的步长\n            self.feed(deconv_m + '_corner_pred_score', 'corner_box', 'img_info', 'gt_default_box') \\\n                .corner_detect_layer(scales=f_scales[deconv_m], feat_stride=None, name=deconv_m + 'data',\n\n                                     )\n\n        # TODO 取出 f3 f4 f7 f8 f9 做segment sensitive map\n\n    def build_loss(self):\n        pass\n","sub_path":"network/mstn_train.py","file_name":"mstn_train.py","file_ext":"py","file_size_in_byte":5162,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"622014897","text":"def is_height_balanced(bin_tree):\r\n    return is_height_balanced_helper(bin_tree.root)[0]\r\n\r\ndef is_height_balanced_helper(curr_node):\r\n    if curr_node.left is None and curr_node.right is None:\r\n        return (True, 1)\r\n    if curr_node.left is not None and curr_node.right is not None:\r\n        left_height = is_height_balanced_helper(curr_node.left)\r\n        right_height = is_height_balanced_helper(curr_node.right)\r\n    elif curr_node.left is not None and curr_node.right is None:\r\n        left_height = is_height_balanced_helper(curr_node.left)\r\n        right_height = (True, 0)\r\n    else:\r\n        left_height = (True, 0)\r\n        right_height = is_height_balanced_helper(curr_node.right)\r\n    difference = max(left_height[1], right_height[1]) - min(left_height[1], right_height[1])\r\n    if difference > 1 or not left_height[0] or not right_height[0]:\r\n        return (False, max(left_height[1], right_height[1]) + 1)\r\n    return (True, max(left_height[1], right_height[1]) + 1)\r\n\r\n\r\n\r\n#BinaryTreeClass\r\nclass EmptyCollection(Exception):\r\n    pass\r\n\r\nclass EmptyTree(Exception):\r\n    pass\r\n\r\nclass LinkedBinaryTree:\r\n    class Node:\r\n        def __init__(self, data, left=None, right=None):\r\n            self.data = data\r\n            self.left = left\r\n            if (self.left is not None):\r\n                self.left.parent = self\r\n            self.right = right\r\n            if (self.right is not None):\r\n                self.right.parent = self\r\n            self.parent = None\r\n\r\n    def __init__(self, root=None):\r\n        self.root = root\r\n        self.size = self.subtree_count(self.root)\r\n\r\n    def __len__(self):\r\n        return self.size\r\n\r\n    def is_empty(self):\r\n        return (len(self) == 0)\r\n\r\n    def subtree_count(self, subtree_root):\r\n        if(subtree_root is None):\r\n            return 0\r\n        else:\r\n            left_count = self.subtree_count(subtree_root.left)\r\n            right_count = self.subtree_count(subtree_root.right)\r\n            return left_count + right_count + 1\r\n\r\n    def sum_tree(self):\r\n        return self.subtree_sum(self.root)\r\n\r\n    def subtree_sum(self, subtree_root):\r\n        if(subtree_root is None):\r\n            return 0\r\n        else:\r\n            left_sum = self.subtree_sum(subtree_root.left)\r\n            right_sum = self.subtree_sum(subtree_root.right)\r\n            return left_sum + right_sum + subtree_root.data\r\n\r\n    def height(self):\r\n        if (self.is_empty()):\r\n            raise EmptyCollection(\"Height is not defined for an empty tree\")\r\n        return self.subtree_height(self.root)\r\n\r\n    #assuming subtree_root is not empty\r\n    def subtree_height(self, subtree_root):\r\n        if((subtree_root.left is None) and (subtree_root.right is None)):\r\n            return 0\r\n        elif(subtree_root.left is None):\r\n            return 1 + self.subtree_height(subtree_root.right)\r\n        elif(subtree_root.right is None):\r\n            return 1 + self.subtree_height(subtree_root.left)\r\n        else:\r\n            left_height = self.subtree_height(subtree_root.left)\r\n            right_height = self.subtree_height(subtree_root.right)\r\n            return 1 + max(left_height, right_height)\r\n","sub_path":"hw7/cz1529_hw7_q3.py","file_name":"cz1529_hw7_q3.py","file_ext":"py","file_size_in_byte":3166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"539404302","text":"import numpy as np\nfrom random import choice\n\nsize = 2\nresult = 0\n\ndef h(x, theta, s): \n    y = x.copy()\n    y[(y - theta) > 0] = 1\n    y[(y - theta) <= 0] = -1\n    y *= s\n    return y\n\ndef fit(x, y):\n    theta = np.append(-1, (x[1:] + x[:-1]) / 2)\n    train_x = np.repeat(np.expand_dims(x, axis=0), 2 * len(theta), axis=0)\n    l = [[-1], [1]]\n    s = np.repeat(np.tile(l, (len(theta), 1)), len(x), axis=1)\n    theta_ = np.repeat(theta, 2, axis=0)\n    theta_ = np.repeat(np.expand_dims(theta_, axis=0), len(x), axis=1).reshape(train_x.shape)\n    \n    #print(train_x)\n    #print(theta_)\n    #print(s)\n    y_head = h(train_x, theta_, s)\n    #print(np.array(y))\n    #print(np.array(y_head))\n    acc = np.sum((y_head == y).astype(np.int), axis=1) / len(y)\n    #print(acc)\n    #print(np.argmax(acc) // 2)\n    #print(np.argmax(acc) % 2)\n    return theta[np.argmax(acc) // 2], l[np.argmax(acc) % 2], acc.max()\n\n\nfor i in range(10000):\n    x_in = np.random.uniform(-1, 1 + 1e-10, size)\n    x_in.sort()\n    y_in = h(x_in, 0, 1)\n    theta, s ,acc = fit(x_in, y_in)\n    \n    x_out = np.random.uniform(-1, 1 + 1e-10, 100000)\n    x_out.sort()\n    y_out = h(x_out, 0, 1)\n    y_predict = h(x_out, theta, s)\n    \n    test_acc = np.sum(y_out == y_predict).astype(np.int) / len(x_out)\n    result += ((1 - test_acc) - (1 - acc))\n\n\n\nprint(result / 10000)\n","sub_path":"hw2/16.py","file_name":"16.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"115041958","text":"from model.fcn import VGG_19bn_8s, VGG_fcn32s\nfrom src.utils import *\nfrom data_loader.fcndataset import FCNDataSet\n\nimport os\nimport torch\nimport torch.optim as optim\nimport torch.nn as nn\nimport configparser\n\nfrom torch.autograd import Variable\nfrom torch.utils.data import DataLoader\n\n\n# multi-GPU\n# device_ids = [0, 1, 2, 3]\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n\n# *********************** hyper parameter  ***********************\n\nconfig = configparser.ConfigParser()\nconfig.read('conf.text')\ndata_label_dir = config.get('data', 'data_label_dir')\nsave_dir = config.get('data', 'save_dir')\n\nlearning_rate = config.getfloat('training', 'learning_rate')\nbatch_size = config.getint('training', 'batch_size')\nepochs = config.getint('training', 'epochs')\nbegin_epoch = config.getint('training', 'begin_epoch')\n\ncuda = torch.cuda.is_available()\n# cuda = False\n\nif not os.path.exists(save_dir):\n    os.mkdir(save_dir)\n\n# *********************** Build dataset ***********************\ntrain_data = FCNDataSet(data_label_dir=data_label_dir)\nprint('Train dataset total number of images sequence is ----' + str(len(train_data)))\n\n# Data Loader\ntrain_dataset = DataLoader(train_data, batch_size=batch_size, shuffle=True)\n\n# *********************** Build model ***********************\n\nnet = VGG_fcn32s(n_class=3)\n# net = VGG_19bn_8s(n_class=3)\nif cuda:\n    net = net.to(device)\n    # net.cuda()\n# if cuda:\n#     net = net.cuda(device_ids[0])\n#     net = nn.DataParallel(net, device_ids=device_ids)\n\n\n# if begin_epoch > 0:\n#     save_path = 'ckpt/model_epoch' + str(begin_epoch) + '.pth'\n#     state_dict = torch.load(save_path)\n#     net.load_state_dict(state_dict)\n\n\ndef train(visual_train=False):\n    # *********************** initialize optimizer ***********************\n    optimizer = optim.Adam(params=net.parameters(), lr=learning_rate)\n    criterion = cross_entropy2d_loss()  # loss function pixel-wised softmax cross entropy\n    if cuda:\n        criterion = criterion.to(device)\n    net.train()\n    for epoch in range(begin_epoch, epochs + 1):\n        print('epoch....................' + str(epoch))\n        # for step, (image, label_map, center_map, imgs) in enumerate(train_dataset):\n        for step, (im_input, im_label, image_input_dir, image_label_dir) in enumerate(train_dataset):\n            image = Variable(im_input.to(device) if cuda else im_input)  # 4D Tensor\n            # Batch_size  *  3  *  width(256)  *  height(256)\n            label = Variable((255 * im_label).to(device) if cuda else (255 * im_label))\n            # Batch_size  *  1 *  width(256)  *  height(256)\n            label = torch.squeeze(label, dim=1).long()\n            # Batch_size  *  width(256)  *  height(256)\n            # if visual_train:\n            #     import torchvision.transforms as transforms\n            #     import matplotlib.pyplot as plt\n            #     image_show = image.cpu().float()\n            #     image_show = transforms.ToPILImage()(torch.squeeze(image_show, dim=0))\n            #     plt.subplot(1, 2, 1)\n            #     plt.imshow(image_show)\n            #     # plt.pause(5)\n            #     label_show = torch.squeeze(label.cpu().float(), dim=0)\n            #     plt.subplot(1, 2, 2)\n            #     plt.imshow(label_show)\n            #     plt.show()\n            optimizer.zero_grad()\n            pred = net(image)  # 4D tensor:  batch size * c * 256 * 256\n\n            if visual_train and epoch % 20 == 0:\n                import torchvision.transforms as transforms\n                import matplotlib.pyplot as plt\n                image_show = image.cpu().float()\n                image_show = transforms.ToPILImage()(torch.squeeze(image_show, dim=0))\n                plt.subplot(1, 3, 1)\n                plt.imshow(image_show)\n                # plt.pause(5)\n                label_show = torch.squeeze(label.cpu().float(), dim=0)\n                plt.subplot(1, 3, 2)\n                plt.imshow(label_show)\n\n                result_show = pred[0]\n                c, w, h = result_show.size()\n                result = np.zeros((w, h), dtype=float)\n                for i in range(w):\n                    for j in range(h):\n                        cls_result = result_show[:, i, j]\n                        result[i][j] = np.argmax(cls_result.cpu().detach().numpy())\n                plt.subplot(1, 3, 3)\n                plt.imshow(result)\n                plt.title(f'epoch: {epoch}')\n                plt.show()\n            # ******************** calculate loss ********************\n            loss = criterion(input=pred, target=label)\n\n            # backward\n            loss.backward()\n            optimizer.step()\n\n            torch.cuda.empty_cache()\n\n            print('--step .....' + str(step))\n            print('--loss ' + str(float(loss.cpu())))\n\n            # if step % 10 == 0:\n            #     print('--step .....' + str(step))\n            #     print('--loss ' + str(float(loss.data[0])))\n\n            # if step % 200 == 0:\n            #     save_images(label_map[:, 5, :, :, :], pred_6[:, 5, :, :, :], step, epoch, imgs)\n\n        if epoch % 5 == 0:\n            torch.save(net.state_dict(), os.path.join(save_dir, 'model_epoch{:d}.pth'.format(epoch)))\n\n    print('train done!')\n\n\nif __name__ == '__main__':\n    train(visual_train=True)\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":5285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"437501863","text":"import numpy as np\nimport theano\nimport theano.tensor as tt\nfrom scipy import integrate\n\n\n# class FlareLightCurve(object):\n#     \"at some point soon I'll make this a class\"\n\n#     def __init__(self):\n#         pass\n\n#     def get_light_curve(self, time=None, tpeaks=None,\n#                         fwhms=None, ampls=None,\n#                         oversample=7,\n#                         texp=None):\n#         self.time = time\n#         self.tpeaks = tpeaks\n#         self.fwhms = fwhms\n\n#         if texp is not None:\n#             # taking this oversample code from\n#             # https://github.com/dfm/exoplanet\n#             texp = tt.as_tensor_variable(texp)\n#             oversample = int(oversample)\n#             oversample += 1 - oversample % 2\n#             stencil = np.ones(oversample)\n\n#             # Construct the exposure time integration stencil\n#             dt = np.linspace(-0.5, 0.5, 2*oversample+1)[1:-1:2]\n#             stencil /= np.sum(stencil)\n\n#             dt = texp * dt\n#             tgrid = tt.shape_padright(time) + dt\n\n#             lc = multiflaremodel(tgrid)\n\ndef get_light_curve(time, tpeaks, fwhms, ampls, texp=None, oversample=7):\n    time = time.astype('float64')\n    time = tt.as_tensor_variable(time)\n\n    if texp is None:\n        tgrid = time\n    if texp is not None:\n        # taking this oversample code from\n        # https://github.com/dfm/exoplanet\n        # and https://github.com/lkreidberg/batman\n        oversample = int(oversample)\n        oversample += 1 - oversample % 2\n        dt = np.linspace(-texp / 2., texp / 2.,\n                         oversample)\n        tgrid = tt.shape_padright(time) + dt\n\n    multiflare_lc = multiflaremodel(tgrid, tpeaks, fwhms, ampls)\n\n    if texp is not None:\n        multiflare_lc = tt.mean(tt.reshape(multiflare_lc, (-1, oversample)),\n                                axis=1)\n\n    return multiflare_lc\n\n\ndef multiflaremodel(time, tpeaks, fwhms, ampls):\n    time = time.astype('float64')\n    time = tt.as_tensor_variable(time)\n    multiflare_lc = tt.zeros_like(time)\n\n    def scan_func(tpeak, fwhm, ampl):\n        zeropad_flare_lc = tt.zeros_like(time)\n        tcut = (((time - tpeak) / fwhm > -1.) *\n                ((time - tpeak) / fwhm < 20.)).nonzero()\n        flare_lc = _flaremodel(time[tcut], tpeak, fwhm, ampl)\n        zeropad_flare_lc = tt.set_subtensor(zeropad_flare_lc[tcut], flare_lc)\n        return zeropad_flare_lc\n\n    components, updates = theano.scan(fn=scan_func,\n                                      sequences=[tpeaks, fwhms, ampls],\n                                      )\n    multiflare_lc = tt.sum(components, axis=0)\n\n    return multiflare_lc\n\n\ndef _flaremodel(time, tpeak, fwhm, ampl):\n    # reuses some code from AltaiPony and Apaloosa\n    time = tt.as_tensor_variable(time)\n    flare_lc = tt.zeros_like(time)\n    flare_lc = tt.where((time <= tpeak) * ((time - tpeak) / fwhm > -1.),\n                        _before_flare(time, tpeak, fwhm, ampl),\n                        flare_lc\n                        )\n    flare_lc = tt.where((time > tpeak) * ((time - tpeak) / fwhm < 20.),\n                        _after_flare(time, tpeak, fwhm, ampl),\n                        flare_lc\n                        )\n    return flare_lc\n\n\ndef _before_flare(time, tpeak, fwhm, ampl):\n    _fr = [1.00000, 1.94053, -0.175084, -2.24588, -1.12498]\n    fout = ((_fr[0] + _fr[1] * ((time - tpeak) / fwhm) +\n             _fr[2] * ((time - tpeak) / fwhm)**2. +\n             _fr[3] * ((time - tpeak) / fwhm)**3. +\n             _fr[4] * ((time - tpeak) / fwhm)**4.) *\n            ampl)\n    return fout\n\n\ndef _after_flare(time, tpeak, fwhm, ampl):\n    _fd = [0.689008, -1.60053, 0.302963, -0.278318]\n    fout = ((_fd[0] * tt.exp(((time - tpeak) / fwhm) * _fd[1]) +\n             _fd[2] * tt.exp(((time - tpeak) / fwhm) * _fd[3])) *\n            ampl)\n    return fout\n\n\ndef multiflare(time, tpeaks, fwhms, ampls):\n\n    timex = tt.dvector('timex')\n    tpeaksx = tt.dvector('tpeaksx')\n    fwhmsx = tt.dvector('fwhmsx')\n    amplsx = tt.dvector('amplsx')\n    multiflare_function = theano.function([timex, tpeaksx, fwhmsx, amplsx],\n                                          multiflaremodel(timex,\n                                                          tpeaksx, fwhmsx,\n                                                          amplsx))\n    return multiflare_function(time, tpeaks, fwhms, ampls)\n\n\ndef eval_get_light_curve(time, tpeaks, fwhms, ampls,\n                         texp=None, oversample=7):\n    timex = tt.dvector('timex')\n    tpeaksx = tt.dvector('tpeaksx')\n    fwhmsx = tt.dvector('fwhmsx')\n    amplsx = tt.dvector('amplsx')\n    multiflare_function = theano.function([timex, tpeaksx, fwhmsx, amplsx],\n                                          get_light_curve(timex,\n                                                          tpeaksx, fwhmsx,\n                                                          amplsx, texp,\n                                                          oversample))\n    return multiflare_function(time, tpeaks, fwhms, ampls)\n\n\n# reference implementation in numpy\ndef get_light_curvenp(time, tpeaks, fwhms, ampls, texp=None, oversample=7):\n    time = np.asarray(time, dtype=float)\n\n    tpeaks = np.atleast_1d(tpeaks)\n    fwhms = np.atleast_1d(fwhms)\n    ampls = np.atleast_1d(ampls)\n\n    if texp is None:\n        tgrid = time\n    if texp is not None:\n        # taking this oversample code from\n        # https://github.com/dfm/exoplanet\n        # and https://github.com/lkreidberg/batman\n        texp = float(texp)\n        oversample = int(oversample)\n        oversample += 1 - oversample % 2\n        dt = np.linspace(-texp / 2., texp / 2.,\n                         oversample)\n        tgrid = (dt + time.reshape(time.size, 1)).flatten()\n\n    multiflare_lc = multiflaremodelnp(tgrid, tpeaks, fwhms, ampls)\n\n    if texp is not None:\n        multiflare_lc = np.mean(\n            multiflare_lc.reshape(-1, oversample),\n            axis=1)\n\n    return multiflare_lc\n\n\ndef multiflaremodelnp(time, tpeaks, fwhms, ampls):\n    time = np.asarray(time, dtype=float)\n    tpeaks = np.atleast_1d(tpeaks)\n    fwhms = np.atleast_1d(fwhms)\n    ampls = np.atleast_1d(ampls)\n    multiflare_lc = np.zeros_like(time)\n    npeaks = tpeaks.shape[0]\n    for i in range(npeaks):\n        flare_lc = _flaremodelnp(time, tpeaks[i], fwhms[i], ampls[i])\n        multiflare_lc = multiflare_lc + flare_lc\n    return multiflare_lc\n\n\ndef _flaremodelnp(time, tpeak, fwhm, ampl):\n    # reuses some code from AltaiPony and Apaloosa\n    _fr = [1.00000, 1.94053, -0.175084, -2.24588, -1.12498]\n    _fd = [0.689008, -1.60053, 0.302963, -0.278318]\n    flare_lc = np.zeros_like(time)\n    flare_lc = np.where((time <= tpeak) * ((time - tpeak) / fwhm > -1.),\n                        (_fr[0] +\n                         _fr[1] * ((time - tpeak) / fwhm) +\n                         _fr[2] * ((time - tpeak) / fwhm)**2. +\n                         _fr[3] * ((time - tpeak) / fwhm)**3. +\n                         _fr[4] * ((time - tpeak) / fwhm)**4.) *\n                        ampl,\n                        flare_lc\n                        )\n    flare_lc = np.where((time > tpeak) * ((time - tpeak) / fwhm < 20.),\n                        (_fd[0] * np.exp(((time - tpeak) / fwhm) * _fd[1]) +\n                         _fd[2] * np.exp(((time - tpeak) / fwhm) * _fd[3])) *\n                        ampl,\n                        flare_lc\n                        )\n    return flare_lc\n\n\ndef get_flare_integral(time, tpeak, fwhm, ampl, texp=None, oversample=7):\n    \"\"\"\n    calculates the integral of flares. Useful for calculating\n    equivalent width and energy\n\n    this is designed to be a post processing step, not run within the\n    HMC model. This is mostly because it is slightly awkward to implement\n    and integral in theano\n    \"\"\"\n    feval = get_light_curvenp(time,\n                              tpeak,\n                              fwhm,\n                              ampl,\n                              texp,\n                              oversample)\n    tstart, tend = time[feval > 0][0], time[feval > 0][-1]\n    integral = integrate.quad(get_light_curvenp, tstart, tend,\n                              points=tpeak,\n                              args=(tpeak, fwhm, ampl, texp, oversample))\n    return integral\n","sub_path":"xoflares/xoflares.py","file_name":"xoflares.py","file_ext":"py","file_size_in_byte":8279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"440220615","text":"import multiprocessing\nimport socket\nimport uuid\nfrom queue import Empty\n\nfrom ipykernel.comm import Comm\nimport sys\n\n\nclass StdErrorQueue(object):\n    def __init__(self):\n        self.queue = multiprocessing.Queue()\n\n    def write(self, v):\n        self.queue.put(v)\n\n    def flush(self):\n        sys.__stderr__.flush()\n\n\nclass AppViewer(object):\n    _dash_comm = Comm(target_name='dash_viewer')\n\n    def __init__(self, host='localhost', port=8050):\n        self.server_process = None\n        self.uid = str(uuid.uuid4())\n        self.host = host\n        self.port = port\n        self.stderr_queue = StdErrorQueue()\n\n    def show(self, app, *args, **kwargs):\n        def run(*args, **kwargs):\n            # Serve App\n            sys.stdout = self.stderr_queue\n            sys.stderr = self.stderr_queue\n            app.run_server(debug=False, *args, **kwargs)\n\n        # Terminate any existing server process\n        self.terminate()\n\n        # precedence host and port\n        launch_kwargs = {'host': self.host, 'port': self.port}\n        launch_kwargs.update(kwargs)\n\n        # Start new server process in separate process\n        self.server_process = multiprocessing.Process(target=run, args=args, kwargs=launch_kwargs)\n        self.server_process.daemon = True\n        self.server_process.start()\n\n        # Wait for server to start\n        started = False\n        retries = 0\n        while not started and retries < 100:\n            try:\n                out = self.stderr_queue.queue.get(timeout=.1)\n                try:\n                    out = out.decode()\n                except AttributeError:\n                    pass\n\n                if 'Running on' in out:\n                    started = True\n            except Empty:\n                retries += 1\n                pass\n\n        if started:\n            # Update front-end extension\n            hostname = launch_kwargs['host']\n            resolved_host = socket.getfqdn() if hostname != '127.0.0.1' and hostname != 'localhost' else hostname\n            self._dash_comm.send({\n                'type': 'show',\n                'uid': self.uid,\n                'url': 'http://{}:{}'.format(resolved_host, launch_kwargs['port'])\n            })\n        else:\n            # Failed to start development server\n            raise ConnectionError('Unable to start Dash server')\n\n    def terminate(self):\n        if self.server_process:\n            self.server_process.terminate()\n","sub_path":"jupyterlab_dash/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"115305207","text":"import configparser\nimport psycopg2\nfrom sql_queries import copy_table_queries, insert_table_queries\n\n\ndef load_staging_tables(cur, conn):\n    \"\"\" \n    Iterates over the queries in the copy_table_queries list to load data from s3 to staging tables\n  \n    Parameters: \n    cur (psycopg2.cursor): psycopg2 cursor connected to the database\n    conn (psycopg2.connection): the database connection\n    \n    \"\"\"        \n    for query in copy_table_queries:\n        cur.execute(query)\n        conn.commit()\n\n\ndef insert_tables(cur, conn):\n    \"\"\" \n    Iterates over the queries in the insert_table_queries list to insert data in analytics tables from the staging tables\n  \n    Parameters: \n    cur (psycopg2.cursor): psycopg2 cursor connected to the database\n    conn (psycopg2.connection): the database connection\n    \n    \"\"\"        \n    for query in insert_table_queries:\n        cur.execute(query)\n        conn.commit()\n\n\ndef main():\n    config = configparser.ConfigParser()\n    config.read('dwh.cfg')\n\n    conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n    cur = conn.cursor()\n    \n    load_staging_tables(cur, conn)\n    insert_tables(cur, conn)\n\n    conn.close()\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"Data-Warehouse-with-Redshift/etl.py","file_name":"etl.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"130168679","text":"# program to find a triple (a,b,c) such that 1<a+b+c<2 from a given array\nimport itertools\na = int(input(\"enter size of array:-\"))\nb = []\nfor i in range(a):\n    c = float(input(\"elements of the array :-\"))\n    try:\n        b.append(c)\n    except:\n        break\nd = itertools.permutations(b , 3)\nfor j in list(d):\n    c = list(j)\n    for k in range(3):\n        if 1 < c[0] + c[1] + c[2] < 2:\n            print(c)\n        else:\n            continue\n","sub_path":"day6/program6.py","file_name":"program6.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"426500948","text":"import numpy as np\norange = np.array([1,0])\napple = np.array([0,1])\napple_value = 1\norange_value =- 1\nweight_1=0.3\nweight_2=0.2\ncoef=0.5\nthreshold=0.1\nfunction1=0\nresultA=-1\nresultO=1\nweights=np.array([weight_1,weight_2])\nfor i in range(0,5):\n\n    if resultO!=orange_value:\n        function1=weights*(np.transpose(orange))+threshold\n        if(function1[1]>0):\n            resultO=1\n\n        else:\n            resultO=-1\n        if resultO !=orange_value:\n            E=orange_value-resultO\n            E=coef*E\n            weights=weights+(E*orange)\n            threshold=threshold+(E)\n\n\n        if (resultA != apple_value):\n            function1 = weights * (np.transpose(apple)) + threshold\n            if function1[1] > 0:\n                resultA = 1\n            else:\n                resultA = -1\n            if(resultA !=apple_value):\n                E=apple_value-resultA\n                E=coef*E\n                weights=weights+(E*apple)\n                threshold=threshold+(E)\n        print(weights)\n        if(resultO==orange_value & resultA==apple_value):\n            break\n\n\n\n\n\n\n\n\n\n","sub_path":"ADALINE_Model/model1.py","file_name":"model1.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"568719042","text":"import sys\nimport math\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\n\nimport itertools\nimport torchvision.models as m\n\nimport models.pretrained as pretrained\nfrom detection import box\n\nfrom models.common import Conv, Cascade, UpCascade, Residual, Parallel, Shared,  \\\n            DecodeAdd, Decode,  basic_block, reduce_features, replace_batchnorms\n\nimport torch.nn.init as init\nfrom tools import Struct\n\nfrom tools.parameters import param, choice, parse_args, parse_choice, make_parser\n\n\ndef image_size(inputs):\n\n    if torch.is_tensor(inputs):\n        assert(inputs.dim() == 3)\n        inputs = inputs.size(1), inputs.size(0)\n\n    assert (len(inputs) == 2)\n    return inputs\n\n\nclass Encoder:\n    def __init__(self, start_layer, box_sizes):\n        self.anchor_cache = {}\n\n        self.box_sizes = box_sizes\n        self.start_layer = start_layer\n\n\n    def anchors(self, input_size, crop_boxes=False):\n        def layer_size(i):\n            stride = 2 ** i\n            return (max(1, math.ceil(input_size[0] / stride)), max(1, math.ceil(input_size[1] / stride)))\n\n        input_args = (input_size, crop_boxes)\n\n        if not (input_args in self.anchor_cache):\n            layer_dims = [layer_size(self.start_layer + i) for i in range(0, len(self.box_sizes))]\n            self.anchor_cache[input_args] = box.make_anchors(self.box_sizes, layer_dims, input_size, crop_boxes=crop_boxes)\n\n        return self.anchor_cache[input_args]\n\n\n    def encode(self, inputs, boxes, labels, crop_boxes=False, match_thresholds=(0.4, 0.5)):\n        inputs = image_size(inputs)\n        return box.encode(boxes, labels, self.anchors(inputs, crop_boxes), match_thresholds)\n\n\n    def decode(self, inputs, loc_pred, class_pred):\n        assert loc_pred.dim() == 2 and class_pred.dim() == 2\n\n        inputs = image_size(inputs)\n        anchor_boxes = self.anchors(inputs).type_as(loc_pred)\n\n        return box.decode(loc_pred, class_pred, anchor_boxes)\n\n    def nms(self, boxes, labels, confs, nms_params=box.nms_defaults):\n        return box.filter_nms(boxes, labels, confs, **nms_params)\n\n    #\n    # def decode_batch(self, inputs, loc_pred, class_pred, nms_params=box.nms_defaults):\n    #     assert loc_pred.dim() == 3 and class_pred.dim() == 3\n    #\n    #     if torch.is_tensor(inputs):\n    #         assert(inputs.dim() == 4)\n    #         inputs = inputs.size(2), inputs.size(1)\n    #\n    #     assert len(inputs) == 2\n    #     return [self.decode(inputs, l, c, nms_params=nms_params) for l, c in zip(loc_pred, class_pred)]\n\n\n\n\n\nclass FCN(nn.Module):\n\n    def __init__(self, trained, extra, box_sizes, features=32, num_classes=2, shared=False, square=False):\n        super().__init__()\n\n        self.encoder = pretrained.make_cascade(trained + extra)\n        self.box_sizes = box_sizes\n\n        encoded_sizes = pretrained.encoder_sizes(self.encoder)\n        self.reduce = Parallel([Conv(size, features, 1) for size in encoded_sizes])\n        self.square = square\n\n        def make_decoder():\n            decoder = Residual(basic_block(features, features))\n            return Decode(features, decoder)\n\n        self.decoder = UpCascade([make_decoder() for i in encoded_sizes])\n\n        assert len(trained + extra) == len(box_sizes), \"FCN: layers and box sizes differ in length\"\n\n        def output(n):\n            return nn.Sequential(\n                Residual(basic_block(features, features)),\n                Residual(basic_block(features, features)),\n                Conv(features, n, 1, bias=True))\n\n        self.num_classes = num_classes\n\n        if shared:\n            num_boxes = len(box_sizes[0])\n            assert all(len(boxes) == num_boxes for boxes in box_sizes), \"FCN: for shared heads, number of boxes must be equal on all layers\"\n            self.classifiers = Shared(output(num_boxes * self.num_classes))\n        else:\n            self.classifiers = Parallel([output(len(boxes) * self.num_classes) for boxes in self.box_sizes])\n\n        self.localisers = Parallel([output(len(boxes) * (3 if square else 4)) for boxes in self.box_sizes])\n\n\n        self.trained_modules = nn.ModuleList(trained)\n        self.new_modules = nn.ModuleList(extra + [self.reduce, self.decoder, self.classifiers, self.localisers])\n\n        def set_momentum(m):\n            if isinstance(m, nn.BatchNorm2d):\n                m.momentum = 0.2\n\n\n        self.apply(set_momentum)\n\n\n        def set_weights(m):\n            # b = -math.log((1 - prior_prob)/prior_prob)\n            if isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d) or isinstance(m, nn.Linear):\n                init.normal_(m.weight, std=0.01)\n            if hasattr(m, 'bias') and m.bias is not None:\n                init.constant_(m.bias, 0)\n\n        self.new_modules.apply(set_weights)\n\n        #replace_batchnorms(self, 16)\n\n\n\n    def forward(self, input):\n        layers = self.decoder(self.reduce(self.encoder(input)))\n        def join(layers, n):\n\n            def permute(layer):\n              out = layer.permute(0, 2, 3, 1).contiguous()\n              return out.view(out.size(0), -1, n)\n\n            return torch.cat(list(map(permute, layers)), 1)\n\n        # def make_rect(layer):\n        #     # Duplicate the size output for x and y together\n        #     layers = [layer, layer.narrow(1, 2, 1)]\n        #     print(layer.size(), layer.narrow(1, 2, 1).size())\n        #\n        #     return torch.cat(layers, dim=1)\n\n        conf = torch.sigmoid(join(self.classifiers(layers), self.num_classes))\n        locs = join(self.localisers(layers), 3 if self.square else 4)\n\n        if self.square:\n            locs = torch.cat([locs, locs.narrow(2, 2, 1)], dim=2)\n\n\n        return (locs, conf)\n\n    def parameter_groups(self, lr, fine_tuning=0.1):\n        return [\n            {'params': self.trained_modules.parameters(), 'lr':lr, 'modifier': fine_tuning},\n            {'params': self.new_modules.parameters(), 'lr':lr, 'modifier': 1.0}\n        ]\n\n\nbase_options = '|'.join(pretrained.models.keys())\n\nparameters = Struct(\n        base_name = param (\"resnet18\", help = \"name of pretrained resnet to use options: \" + base_options),\n        features  = param (64, help = \"fixed size features in new conv layers\"),\n        first     = param (3, help = \"first layer of anchor boxes, anchor size = anchor_scale * 2^n\"),\n        last      = param (5, help = \"last layer of anchor boxes\"),\n\n        anchor_scale = param (4, help = \"anchor scale relative to box stride\"),\n        shared    = param (False, help = \"share weights between network heads at different levels\"),\n        square    = param (False, help = \"restrict box outputs (and anchors) to square\"),\n\n    )\n\ndef extra_layer(inp, features):\n    return nn.Sequential(\n        *([Conv(inp, features, 1)] if inp != features else []),\n        Residual(basic_block(features, features)),\n        Residual(basic_block(features, features)),\n        Conv(features, features, stride=2)\n    )\n\ndef split_at(xs, n):\n    return xs[:n], xs[n:]\n\n\ndef anchor_sizes(start, end, anchor_scale=4, square=False):\n\n    aspects = [1] if square else [1/2, 1, 2]\n    scales = [1, pow(2, 1/3), pow(2, 2/3)]\n\n    return [box.anchor_sizes(anchor_scale * (2 ** i), aspects, scales) for i in range(start, end + 1)]\n\n\ndef extend_layers(layers, start, end, features=32):\n    features_in = pretrained.layer_sizes(layers)[-1]\n\n    num_extra = max(0, end + 1 - len(layers))\n    extra_layers = [extra_layer(features_in if i == 0 else features, features) for i in range(0, num_extra)]\n\n    initial, rest =  layers[:start + 1], layers[start + 1:end + 1:]\n    return [nn.Sequential(*initial), *rest], [*extra_layers]\n\n\ndef create_fcn(args, dataset_args):\n    assert dataset_args.input_channels == 3\n\n    num_classes = len(dataset_args.classes)\n    assert num_classes >= 1\n\n    assert args.first <= args.last\n    assert args.base_name in pretrained.models, \"base model not found: \" + args.base_name + \", options: \" + base_options\n\n    base_layers = pretrained.models[args.base_name]()\n    args.first = min(len(base_layers) - 1, args.first)\n\n    backbone, extra = extend_layers(base_layers, args.first, args.last, features=args.features)\n    box_sizes = anchor_sizes(args.first, args.last, anchor_scale=args.anchor_scale, square=args.square)\n\n    return FCN(backbone, extra, box_sizes, num_classes=num_classes, features=args.features, shared=args.shared, square=args.square), \\\n           Encoder(args.first, box_sizes)\n\nmodels = {\n    'fcn' : Struct(create=create_fcn, parameters=parameters)\n  }\n\n\n\n\nif __name__ == '__main__':\n\n    _, *cmd_args = sys.argv\n\n    model_params = {k: v.parameters for k, v in models.items()}\n    parameters = Struct(model= choice('fcn', model_params))\n\n    parser = make_parser('Object detection', parameters)\n    args = Struct(**parser.parse_args().__dict__)\n\n    model_args = parse_choice(\"model\", parameters.model, args.model)\n\n    classes = {\n        0 : {'shape':'BoxShape'},\n        1 : {'shape':'BoxShape'}\n    }\n\n    model, _ = create_fcn(model_args.parameters, Struct(classes = classes, input_channels = 3))\n\n    x = Variable(torch.FloatTensor(4, 3, 400, 400))\n    out = model.cuda()(x.cuda())\n\n    [print(y.size()) for y in out]\n","sub_path":"models/fcn.py","file_name":"fcn.py","file_ext":"py","file_size_in_byte":9194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"427210231","text":"import sys\nimport traceback\n\nfrom flask import request\nfrom flask import jsonify\n\nfrom functools import wraps\nfrom NinjaUtil import LoggingDaemon\n\ndef BrainiacApi (wants = []):\n    def decorate (func):\n        @wraps(func)\n        def decorated_function (*args, **kwargs):\n            headers = \"\"\n            for i in request.headers:\n                headers += i [0] + \":\" + i [1] + '; '\n            headers.strip ()\n            LoggingDaemon.debug (\">>> [%s] [%s] [%s] [%s]\" \\\n                                 % (request.method, request.path,\n                                    func.__name__, headers))\n            resp = None\n            try:\n                for i in wants:\n                    if i [1] not in request.headers ['Content-Type']:\n                        resp = jsonify ({'error':'rest api expects %s' % i [1]})\n                        resp.status_code = 400\n                        raise Exception ('Rest Call expects %s' % i [1])\n                resp = func (*args, **kwargs)\n            except KeyError:\n                resp = jsonify ({'error':'rest call expected key [%s]' % sys.exc_info () [1]})\n                resp.status_code = 400\n            except:\n                LoggingDaemon.error (\"%s\" % traceback.format_exc ())\n                LoggingDaemon.error (\"%s\" % sys.exc_info () [0])\n                LoggingDaemon.error (\"Caught api call exception\")\n            if resp is None:\n                resp = jsonify ({'error':str (sys.exc_info ())})\n                resp.status_code = 400\n            LoggingDaemon.debug (\"<<< [%s] [%s] [%s] [%s]\" \\\n                                 % (request.method, request.path,\n                                    func.__name__, headers))\n            return resp\n        return decorated_function\n    return decorate\n","sub_path":"Brainiac/BrainiacUtil.py","file_name":"BrainiacUtil.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"149532023","text":"# Copyright 2017 BBVA\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nfrom datetime import datetime, timedelta\nfrom enum import Enum\nfrom sqlalchemy.orm import Session\nfrom deeptracy_core.dal.scan.model import Scan\n\n\nclass ScanState(Enum):\n    PENDING = 'PENDING'\n    RUNNING = 'RUNNING'\n    DONE = 'DONE'\n    INVALID_REPO = 'INVALID_REPO'\n    INVALID_BRANCH = 'INVALID BRANCH'\n    NO_PLUGINS_FOR_LANGUAGE = 'NO_PLUGINS_FOR_LANGUAGE'\n    INVALID_YML_ON_PROJECT = 'INVALID_YML_ON_PROJECT'\n    CANT_GET_LANGUAGE = 'CANT_GET_LANGUAGE'\n    SAME_DEPS_AS_PREVIOUS = 'SAME_DEPS_AS_PREVIOUS'\n\n\ndef add_scan(project_id: str, session: Session, lang=None, branch=None) -> Scan:\n    \"\"\"Adds a scan related to a project\"\"\"\n    assert type(project_id) is str\n\n    scan = Scan(project_id=project_id, lang=lang, branch=branch)\n    session.add(scan)\n    return scan\n\n\ndef get_scan(scan_id: str, session: Session) -> Scan:\n    \"\"\"Get a project from its id\"\"\"\n    assert type(scan_id) is str\n\n    scan = session.query(Scan).get(scan_id)\n    return scan\n\n\ndef update_scan_state(scan: Scan, state: ScanState, session: Session) -> Scan:\n    \"\"\"Updates a scan state\"\"\"\n    if scan.id is None:\n        raise ValueError('Cant create scans')\n\n    scan.state = state.name\n    session.add(scan)\n    return scan\n\n\ndef get_previous_scan_for_project(project_id: str, scan_id: str, session: Session) -> bool:\n    \"\"\"\n    Given a project and a scan id, return a the previous scan for the project\n\n    :param project_id:\n    :param scan_id:\n    :param session:\n    :return:\n    \"\"\"\n    scan = get_scan(scan_id, session)\n\n    previous_scan = session.query(Scan) \\\n        .filter(Scan.project_id == project_id) \\\n        .filter(Scan.created < scan.created) \\\n        .order_by(Scan.created.desc()) \\\n        .first()\n\n    return previous_scan\n\n\ndef get_num_scans_in_last_minutes(project_id: str, minutes: int, session: Session) -> int:\n    \"\"\"\n    Return the number of scans ran in the last N minutes for a project\n\n    :param project_id:\n    :param minutes:\n    :param session:\n    :return:\n    \"\"\"\n\n    assert type(project_id) is str\n    assert type(minutes) is int\n\n    from_time = datetime.now() - timedelta(minutes=minutes)\n    number = session.query(Scan) \\\n        .filter(Scan.created > from_time) \\\n        .filter(Scan.project_id == project_id) \\\n        .count()\n\n    return number\n\n\ndef get_scan_vulnerabilities(scan_id: str, session: Session):\n    \"\"\"\n    Get scan vulnerabilities\n\n    :param scan_id: (str) scan id to asociate the dependency\n    :param session: (Session) database session to add objects\n    :return vulnerabilities: (Array) Array of ScanVulnerability\n    \"\"\"\n\n    assert type(scan_id) is str\n    scan = session.query(Scan).get(scan_id)\n    if scan is None:\n        raise AssertionError('Scan {} not found in database'.format(scan_id))\n    return scan.scan_vulnerabilities\n\n\n__all__ = ('ScanState', 'add_scan', 'get_scan', 'update_scan_state', 'get_previous_scan_for_project',\n           'get_scan_vulnerabilities')\n","sub_path":"deeptracy_core/dal/scan/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":3511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"578287612","text":"import random\nplay_again = \"y\"\nwhile play_again == \"y\":\n    random_number = random.randint(1, 10)  # Number 1 - 10\n    user_guess = int(input(\"Guess a number between 1 and 10: \"))\n    while user_guess != random_number:\n        if user_guess < random_number:\n            print(\"Your guess is too low!\")\n            user_guess = int(input())\n        elif user_guess > random_number:\n            print(\"Your guess is too high!\")\n            user_guess = int(input())\n    print(\"Your guess is correct, the number is: {}\".format(random_number))\n    play_again = input(\"Do you want to play again? [y/n] \")\n\n# handle user guesses\n#   if they guess correctly, tell them they won\n#   otherwise, tell them if they are too high or too low\n\n# Bonus - Let player play again if they want\n","sub_path":"guessing_game/guessing.py","file_name":"guessing.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"240196917","text":"\n# -*- coding: utf-8 -*-\n\nimport matplotlib.pyplot as plt\n\ntime=[]\nnote=[]\n\nreadfile=open('tr.log','r')\n\nsepfile=readfile.read().split('\\n')\nprint (\"sepfile : \",sepfile)\nreadfile.close()\n\nfor line in sepfile:\n\t#print line\n\ta = line.split()\n\t#print (a)\n\tif not a:\n\t\tbreak\n\t\t\n\ttime.append(a[0])\n\tnote.append(a[3])\n\n\n#print (\"Time : \",time)\n#print (\"Note : \",note)\n\nnumNote = len(note)\n#print (\"колечество записей : \",numNote)\n\n#sepfile=readfile.read().split('\\n')\n#print sepfile\n\nplt.plot(time,note,'ro')\n\nplt.title('matplotlib title')\nplt.xlabel('matplot Time label')\nplt.ylabel('matplot Note label')\nplt.grid(True)\n\nplt.show()\n","sub_path":"2016/Plotting/задачи/t_4.py","file_name":"t_4.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"254013585","text":"from logger import log\n\n\nclass AntSolver:\n    \"\"\"Класс, моделирующий решение задачи\"\"\"\n    def __init__(self, strat, ant):\n        self.S = strat\n        self.p_ant = ant\n        self.ants = None\n\n    def make_ants(self, num):\n        if self.ants is None:\n            self.ants = [self.p_ant.clone() for i in range(num)]\n        else:\n            for a in self.ants:\n                a.reset()\n        return self.ants\n\n    def solve(self, iters=1, ants_n=20):\n        best_paths = []\n        worst_paths = []\n        avg_paths = []\n        i = 1\n        while i <= iters:\n            ants = self.make_ants(ants_n)\n            log()['SOLVER'].log(f'Iter {i}')\n            best, worst, avg = self.S.generate_solutions(ants)\n            if best is None:\n                log()['SOLVER'].log(f'No results on iter {i}, restarting')\n            else:\n                best_paths.append(best)\n                worst_paths.append(worst)\n                avg_paths.append(avg)\n                self.S.update_pheromone(ants)\n                self.S.daemon_actions()\n                i = i + 1\n\n        return best_paths, worst_paths, avg_paths, self.S.result()\n","sub_path":"solver/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"27891536","text":"import scrapy\nfrom snbook.items import SnbookItem\n\n\nclass BookSpider(scrapy.Spider):\n    name = 'book'\n    allowed_domains = ['suning.com']\n    start_urls = ['https://list.suning.com/1-502322-0.html']\n\n    def parse(self, response):\n        list1 = response.xpath('//ul[@class=\"clearfix\"]')\n        for i in list1:\n            item = SnbookItem()\n            item['bookname'] = i.xpath('.//p[@class=\"sell-point\"]/a/text()').get()\n            item['price'] = i.xpath('.//em[@class=\"prive price\"]/text()').get()\n            #半截网址进行拼接\n            item['bookurl'] = i.xpath('.//p[@class=\"sell-point\"]/a/@href').getall()\n            item['bookurl'] = ['https:' + i for i in item['bookurl']]\n            for url in item['bookurl']:\n                yield scrapy.Request(url, meta={'item': item}, callback=self.parse_detail)\n\n\n    def parse_detail(self, reponse):\n        item = reponse.meta['item']\n        # price = reponse.xpath('//div[@class=\"wrapper proinfo\"]//span[@class=\"mainprice\"]/text()')\n        price = reponse.xpath('//div[@class=\"wrapper proinfo\"]//li[2]/text()').getall()\n        item['price'] = price\n        print(item)\n","sub_path":"错误代码.py","file_name":"错误代码.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"385388356","text":"# -*- coding: utf-8 -*-\n\nfrom api.api import API\nfrom pages.android.common.super_page import SuperPage\nfrom pages.android.ffan.store_info_page_configs import StoreInfoPageConfigs as SIPC\n\n\nclass StoreInfoPage(SuperPage):\n    '''\n    作者 宋波\n    首页=>搜索页=>门店信息\n    '''\n    def __init__(self, testcase, driver, logger):\n        super(StoreInfoPage, self).__init__(testcase=testcase, driver=driver, logger=logger);\n\n    def validSelf(self):\n        '''\n        usage : 检查是否加载出来\n        '''\n        API().assertElementByText(self.testcase,\n                                  self.driver,\n                                  self.logger,\n                                  SIPC.text_store_detail,\n                                  SIPC.assert_view_timeout)\n\n    def validKeywords(self, keywords):\n        '''\n        usage: 验证关键字\n        '''\n        API().assertElementByContainsText(self.testcase,\n                                          self.driver,\n                                          self.logger,\n                                          keywords,\n                                          SIPC.assert_view_timeout)\n\n    def clickOnStoreImageTextDetails(self):\n        '''\n        usage: 点击门店图片\n        '''\n\n        API().clickElementByXpath(self.testcase,\n                                  self.driver,\n                                  self.logger,\n                                  SIPC.xpath_store_image_text_details_button,\n                                  SIPC.click_on_button_timeout)\n\n    def validHotWordTitle(self, title = \"default\"):\n        '''\n        usage: 验证热词检索结果详细页标题\n        '''\n        API().assertElementByContentDesc(self.testcase,\n                                         self.driver,\n                                         self.logger,\n                                         title,\n                                         SIPC.assert_view_timeout)\n","sub_path":"pages/android/ffan/store_info_page.py","file_name":"store_info_page.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"309522386","text":"#!/usr/bin/env python\n\nimport signal\nimport sys\n\nfrom base.ioloop import IOLoop\nfrom base.acceptor import Acceptor\nfrom base.connector import Connector\nfrom sentinel.protocol import Protocol\n\n\ndef pingHandler(mvdata):\n    return \"*1\\r\\n$4\\r\\npong\\r\\n\"\n\ndef getHandler(mvdata):\n    return \"*1\\r\\n$4\\r\\nbert\\r\\n\"\n\nhandlers = {'ping' : pingHandler, 'get' : getHandler }\n\n\ndef signalHandler(signal, frame):\n    print(\"\\r\\nExiting\\r\\n\")\n    sys.exit(0)\n\nloop = None\n\nif __name__ == \"__main__\":\n    signal.signal(signal.SIGINT, signalHandler)\n\n    import logging\n    import logging.config\n\n    mylog = logging.getLogger(\"qedis_sentinel\")\n    try:\n        logging.config.fileConfig('conf/logging.conf')\n    except Exception as e: \n        logging.basicConfig(filename='qedis_sentinel.log', level=logging.DEBUG, format='[%(asctime)s]%(levelname)s: %(message)s')\n\n    #logging.basicConfig(filename = 'sentinel.log', level = logging.DEBUG, format='[%(asctime)s]%(levelname)s: %(message)s')\n\n    from base.handler import  MessageCallback, OnConnectCallback, NewConnectionCallback\n\n    class MsgHandler(MessageCallback):\n        def __init__(self):\n            self.parser = Protocol()\n\n        def __call__(self, conn, mv):\n            parser = self.parser\n            parser.parse(mv)\n            if parser.ready():\n                parser.debug()\n                params = parser.params()\n                hdl = handlers.get(params[0])\n                if hdl:\n                    response = hdl(mv)\n                    conn.send(response)\n\n                parser.reset()\n            else:\n                mylog.debug(\"not complete protocol recv, please wait\")\n\n            return parser.consumed()\n\n    class ConnectHandler(OnConnectCallback):\n        def __call__(self, conn):\n            OnConnectCallback.__call__(self, conn)\n            loop._sched.enter(delay = 2.1, count = -1, action = lambda conn : conn.send(\"*1\\r\\n$4\\r\\nping\\r\\n\"), argument = [conn])\n\n    class NewConnHandler(NewConnectionCallback):\n        def __call__(self, conn):\n            NewConnectionCallback.__call__(self, conn)\n            conn.setMsgCallback(MsgHandler())\n            conn.setOnConnectCallback(ConnectHandler())\n\n\n    loop = IOLoop()\n    connector = Connector(loop = loop)\n    connector.setNewConnCallback(NewConnHandler())\n    connector.connect(port = 6379)\n    mylog.debug(\"start\")\n\n    loop.start()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"180766589","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\n\n\n\n# 图片按钮\nclass PicButton(QAbstractButton):\n    def __init__(self, pixmap, pixmap_hover, pixmap_pressed,parent=None):\n        super(PicButton, self).__init__(parent)\n        self.pixmap = pixmap\n        self.pixmap_hover = pixmap_hover\n        self.pixmap_pressed = pixmap_pressed\n        self.pressed.connect(self.update)\n        self.released.connect(self.update)\n\n    def paintEvent(self, event):\n        pix = self.pixmap_hover if self.underMouse() else self.pixmap\n        if self.isDown():\n            pix = self.pixmap_pressed\n        painter = QPainter(self)\n        painter.drawPixmap(event.rect(), pix)\n\n    def enterEvent(self, event):\n        self.update()\n\n    def leaveEvent(self, event):\n        self.update()\n\n    def sizeHint(self):\n        return self.pixmap.size()\n\n\n# 文本添加区\nclass AddWidget(QWidget):\n    def tag(self):\n        yellow = QColor(255, 255, 0)\n        fmt = QTextCharFormat()\n        fmt.setBackground(yellow)\n        cursor = self.note_add.textCursor()\n        cursor.mergeCharFormat(fmt)\n        self.note_add.mergeCurrentCharFormat(fmt)\n\n    def __init__(self, *args, **kwargs):\n        super(AddWidget, self).__init__(*args, **kwargs)\n        self.main_layout = QVBoxLayout(self)\n        self.main_layout.setSpacing(0)\n        # 工具栏\n        self.edit_tool_Layout = QHBoxLayout(self)\n        self.edit_tool_Layout.setAlignment(Qt.AlignRight)\n        self.note_tag_button = QPushButton(\"着重\")\n        self.note_tag_button.setFixedWidth(40)\n        self.note_tag_button.setFixedHeight(20)\n        self.note_tag_button.clicked.connect(self.tag)\n        self.edit_tool_Layout.addWidget(self.note_tag_button)\n        self.main_layout.addLayout(self.edit_tool_Layout)\n        # 编辑区\n        self.note_add = QTextEdit()\n        self.main_layout.addWidget(self.note_add)\n        self.note_cancel_button = QPushButton(\"取消\")\n        self.note_cancel_button.setFixedWidth(40)\n        self.note_cancel_button.setFixedHeight(20)\n        self.note_add_button = QPushButton(\"确定\")\n        self.note_add_button.setFixedWidth(40)\n        self.note_add_button.setFixedHeight(20)\n        self.edit_bottom_Layout = QHBoxLayout(self)\n        self.edit_bottom_Layout.setAlignment(Qt.AlignRight)\n        self.edit_bottom_Layout.addWidget(self.note_cancel_button)\n        self.edit_bottom_Layout.addWidget(self.note_add_button)\n        self.main_layout.addLayout(self.edit_bottom_Layout)\n\n\n# 文本详情区\nclass DetailWidget(QWidget):\n    def __init__(self, *args, **kwargs):\n        super(DetailWidget, self).__init__(*args, **kwargs)\n        self.main_layout = QVBoxLayout(self)\n        # 详情区\n        self.note_content = QLabel()\n        self.note_content.setWordWrap(True)\n        self.main_layout.addWidget(self.note_content)\n        self.note_edit_button = QPushButton(\"编辑\")\n        self.note_edit_button.setFixedWidth(40)\n        self.note_edit_button.setFixedHeight(20)\n        self.note_del_button = QPushButton(\"删除\")\n        self.note_del_button.setFixedWidth(40)\n        self.note_del_button.setFixedHeight(20)\n        self.edit_bottom_Layout = QHBoxLayout(self)\n        self.edit_bottom_Layout.setAlignment(Qt.AlignRight)\n        self.edit_bottom_Layout.addWidget(self.note_edit_button)\n        self.edit_bottom_Layout.addWidget(self.note_del_button)\n        self.main_layout.addLayout(self.edit_bottom_Layout)\n\n    def sizeHint(self):\n        lineNums = self.parent().getTextLineNumbers()\n        if lineNums>0:\n            width = self.main_layout.sizeHint().width()\n            fontHeight=17\n            return QSize(width, lineNums* fontHeight+60)\n        return self.main_layout.sizeHint()\n\n# 文本块\nclass NoteWidget(QWidget):\n    def setTextLineNumbers(self,lineNums):\n        self.lineNums=lineNums\n    # 获取编辑框文本行数,默认直接从编辑框取值\n    def getTextLineNumbers(self,getByWidget=False):\n        if (not getByWidget) and self.lineNums>0:\n            return self.lineNums\n        linenums = 0\n        tb = self.addWidget.note_add.document().begin()\n        while (tb.isValid()):\n            layout = tb.layout()\n            if layout:\n                linenums = linenums + layout.lineCount()\n            tb = tb.next()\n        return linenums\n    #删除自身\n    def deleteSelf(self):\n        self.main_layout.removeWidget(self.addWidget)\n        self.main_layout.removeWidget(self.detailWidget)\n        self.addWidget.deleteLater()\n        self.detailWidget.deleteLater()\n        self.parentWidget.delete_note(self)\n\n    # 点击取消，编辑框消失，详情框出现，或者直接删除这个部件\n    def cancel(self):\n        self.addWidget.hide()\n        self.detailWidget.show()\n        self.parentWidget.parentWidget.setSizeHint(self.parentWidget.sizeHint())\n        if str(self.id) == \"-1\":\n            self.deleteSelf()\n\n    # 点击添加,编辑框消失,详情框出现\n    def add(self):\n        text = self.addWidget.note_add.toHtml()\n        if not text:\n            QMessageBox.information(self, \"提示\", \"内容不能为空\")\n            return\n        listWidgetItem = self.parentWidget.parentWidget\n        # info = listWidgetItem.whatsThis()\n        maskId = listWidgetItem.data(Qt.UserRole + 1)\n        # maskId=info.split(\"|\")[0]\n\n        self.detailWidget.note_content.setText(text)\n        self.addWidget.hide()\n        self.detailWidget.show()\n        lineNums = self.getTextLineNumbers(True)\n        self.setTextLineNumbers(lineNums)\n        listWidgetItem.setSizeHint(self.parentWidget.sizeHint())\n        if str(self.id)==\"-1\":\n            self.id =self.parentWidget.record.addNote(maskId,text,lineNums)\n        else:\n            self.parentWidget.record.editNote(self.id , text,lineNums)\n\n\n    # 点击编辑,详情框关闭,编辑框出现\n    def edit(self):\n        text=self.detailWidget.note_content.text()\n        self.addWidget.note_add.setText(text)\n        self.detailWidget.hide()\n        self.addWidget.show()\n        self.parentWidget.parentWidget.setSizeHint(self.parentWidget.sizeHint())\n\n    def setId(self,_id):\n        self.id=_id\n\n    def __init__(self, _parentWidget,*args, **kwargs):\n        super(NoteWidget, self).__init__(*args, **kwargs)\n        self.parentWidget=_parentWidget\n        self.id=-1\n        self.lineNums=0\n        self.main_layout = QVBoxLayout(self)\n        self.addWidget=AddWidget()\n        self.detailWidget=DetailWidget()\n        self.addWidget.note_cancel_button.clicked.connect(self.cancel)\n        self.addWidget.note_add_button.clicked.connect(self.add)\n        self.detailWidget.note_edit_button.clicked.connect(self.edit)\n        self.detailWidget.note_del_button.clicked.connect(self.deleteSelf)\n        self.main_layout.addWidget(self.addWidget)\n        self.main_layout.addWidget(self.detailWidget)\n    def sizeHint(self):\n        if self.lineNums==0:\n            return self.main_layout.sizeHint()\n        else:\n            if self.detailWidget.isVisible():\n                return self.detailWidget.sizeHint()\n            return self.main_layout.sizeHint()\n\nclass CustomWidget(QWidget):\n    # 高度累加\n    def addQsize(self,qsize_a,qsize_b):\n        return QSize(qsize_a.width(),qsize_a.height()+qsize_b.height())\n    # 删除自己的网页标注\n    def delete_mask(self):\n        command = '\\n delete_mask({});'.format(self.mask_id)\n        self.browser.page().runJavaScript(command)\n\n    # 删除标注下的一个笔记\n    def delete_note(self,noteWidget):\n        noteId= noteWidget.id\n        self.note_list.remove(noteWidget)\n        self.main_layout.removeWidget(noteWidget)\n        noteWidget.deleteLater()\n        listWidgetItem = self.parentWidget\n        listWidgetItem.setSizeHint(self.sizeHint())\n        if str(noteId)!=\"-1\":\n            # info = listWidgetItem.whatsThis()\n            maskId = listWidgetItem.data(Qt.UserRole + 1)\n            # maskId=info.split(\"|\")[0]\n            self.record.delNote(noteId)\n\n    # 手动点击添加\n    def add_note(self):\n        noteWidget = NoteWidget(self)\n        noteWidget.detailWidget.hide()\n        self.main_layout.addWidget(noteWidget)\n        self.note_list.append(noteWidget)\n        self.parentWidget.setSizeHint(self.sizeHint())\n\n    # 自动load节点，从json文本中获取数据\n    def load_note(self,text):\n        noteWidget = NoteWidget(self)\n        noteWidget.setId(text[\"noteId\"])\n        if \"lineNums\" in text.keys():\n            noteWidget.setTextLineNumbers(int(text[\"lineNums\"]))\n        noteWidget.addWidget.hide()\n        noteWidget.detailWidget.note_content.setText(text[\"text\"])\n        self.main_layout.addWidget(noteWidget)\n        self.note_list.append(noteWidget)\n        self.parentWidget.setSizeHint(self.sizeHint())\n    # 删除自己\n    def del_self(self):\n        reply = QMessageBox.question(self, '询问', '这将删除此标注及其所有笔记，确定删除吗?',\n                                     QMessageBox.Yes | QMessageBox.No, QMessageBox.No)\n        if reply == QMessageBox.Yes:\n            self.delete_mask()\n            for noteWidget in self.note_list:\n                noteWidget.deleteSelf()\n            self.deleteLater()\n            listWid=self.parentWidget.listWidget()\n            listWid.takeItem(listWid.row(self.parentWidget))\n            self.record.delSection(self.mask_id)\n\n    def edit_self(self):\n        pass\n    def set_maskId(self,maskId):\n        self.mask_id=maskId\n\n\n    def set_browser(self, _browser):\n        self.browser = _browser\n\n    def __init__(self, _parentWidget,type,_title, _record,*args, **kwargs):\n        super(CustomWidget, self,).__init__(*args, **kwargs)\n        self.mask_id=None\n        self.browser=None\n        self.main_layout = QVBoxLayout(self)\n        self.note_list=list()\n        self.parentWidget=_parentWidget\n        self.record=_record\n        self.main_layout.setContentsMargins(0, 10, 0, 0)\n        # 标题区域\n        self.note_title=QLabel(_title)\n        self.note_title.setContentsMargins(0,0,0,10)\n        if type==1:\n            self.note_title.setStyleSheet('QLabel { border:none; border-left:5px solid rgb(255,255,0);border-bottom:2px dashed gray;}')\n        else:\n            self.note_title.setStyleSheet(\n                'QLabel { border:none; border-left:5px solid rgb(255,122,122);border-bottom:2px dashed gray;}')\n        self.note_title.setWordWrap(True)\n        self.main_layout.addWidget(self.note_title)\n        # 工具按钮\n        self.tool_Layout = QHBoxLayout(self)\n        self.tool_Layout.setAlignment(Qt.AlignRight)\n        addButton = PicButton(QPixmap(\"add.png\"),QPixmap(\"add_hover.png\"),QPixmap(\"add_hover.png\"))\n        addButton.setFixedWidth(25)\n        addButton.setFixedHeight(25)\n        addButton.clicked.connect(self.add_note)\n        delButton = PicButton(QPixmap(\"del.png\"),QPixmap(\"del_hover.png\"),QPixmap(\"del_hover.png\"))\n        delButton.setFixedWidth(25)\n        delButton.setFixedHeight(25)\n        delButton.clicked.connect(self.del_self)\n        editButton = PicButton(QPixmap(\"edit.png\"),QPixmap(\"edit_hover.png\"),QPixmap(\"edit_hover.png\"))\n        editButton.setFixedWidth(25)\n        editButton.setFixedHeight(25)\n        editButton.clicked.connect(self.edit_self)\n        self.tool_Layout.addWidget(addButton)\n        self.tool_Layout.addWidget(editButton)\n        self.tool_Layout.addWidget(delButton)\n        self.main_layout.addLayout(self.tool_Layout)\n\n    def sizeHint(self):\n        size_init = self.addQsize(self.note_title.sizeHint(),self.tool_Layout.sizeHint())\n        # 对未计算到的高度进行补偿\n        offset_size=QSize(0,30)\n        size_init=self.addQsize(size_init,offset_size)\n        for noteWidget in self.note_list:\n            size_init=self.addQsize(size_init,noteWidget.sizeHint())\n        return size_init","sub_path":"note_widget.py","file_name":"note_widget.py","file_ext":"py","file_size_in_byte":11909,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"39220678","text":"\n# coding: utf-8\n\n# In[1]:\n\nfrom __future__ import division,unicode_literals\n# get_ipython().magic('matplotlib inline')\nimport numpy as np\nimport pandas as pd\nimport json\nimport runProcs\nfrom urllib.request import urlopen\n\nimport matplotlib.pyplot as plt\n\n\n# In[2]:\n\n# 0. State abbreviations\n\n# 0.1 dictionary:\nstateAbbr = {\nu'Alabama':u'AL',\nu'Alaska':u'AK',\nu'Arizona':u'AZ',\nu'Arkansas':u'AR',\nu'California':u'CA',\nu'Colorado':u'CO',\nu'Connecticut':u'CT',\nu'Delaware':u'DE',\nu'District of Columbia':u'DC',\nu'Florida':u'FL',\nu'Georgia':u'GA',\nu'Hawaii':u'HI',\nu'Idaho':u'ID',\nu'Illinois':u'IL',\nu'Indiana':u'IN',\nu'Iowa':u'IA',\nu'Kansas':u'KS',\nu'Kentucky':u'KY',\nu'Louisiana':u'LA',\nu'Maine':u'ME',\nu'Maryland':u'MD',\nu'Massachusetts':u'MA',\nu'Michigan':u'MI',\nu'Minnesota':u'MN',\nu'Mississippi':u'MS',\nu'Missouri':u'MO',\nu'Montana':u'MT',\nu'Nebraska':u'NE',\nu'Nevada':u'NV',\nu'New Hampshire':u'NH',\nu'New Jersey':u'NJ',\nu'New Mexico':u'NM',\nu'New York':u'NY',\nu'North Carolina':u'NC',\nu'North Dakota':u'ND',\nu'Ohio':u'OH',\nu'Oklahoma':u'OK',\nu'Oregon':u'OR',\nu'Pennsylvania':u'PA',\nu'Rhode Island':u'RI',\nu'South Carolina':u'SC',\nu'South Dakota':u'SD',\nu'Tennessee':u'TN',\nu'Texas':u'TX',\nu'Utah':u'UT',\nu'Vermont':u'VT',\nu'Virginia':u'VA',\nu'Washington':u'WA',\nu'West Virginia':u'WV',\nu'Wisconsin':u'WI',\nu'Wyoming':u'WY'\n}\n\n# 0.2 List of states in the US\nstateList = [s for s in stateAbbr]\n\n\n# In[3]:\n\n# 1. Construct series for price deflator\n\n# 1.1 Obtain data from BEA\ngdpDeflator = urlopen('http://bea.gov/api/data/?UserID=3EDEAA66-4B2B-4926-83C9-FD2089747A5B&method=GetData&datasetname=NIPA&TableID=13&Frequency=A&Year=X&ResultFormat=JSON&')\n\n# result = gdpDeflator.readall().decode('utf-8')\nresult = gdpDeflator.read().decode('utf-8')\njsonResponse = json.loads(result)\n\n\n# In[4]:\n\n# 1.2 Construct the data frame for the deflator series\nvalues = []\nyears = []\nfor element in jsonResponse['BEAAPI']['Results']['Data']:\n#     if element['LineDescription'] == 'Personal consumption expenditures':\n    if element['LineDescription'] == 'Gross domestic product':\n        years.append(element['TimePeriod'])\n        values.append(float(element['DataValue'])/100)\n\nvalues = np.array([values]).T\ndataP = pd.DataFrame(values,index = years,columns = ['price level'])\n\n# 1.3 Display the data\nprint(dataP)\n\n\n# In[5]:\n\n# 2. Construct series for per capita income by state, region, and the entire us\n\n# 2.1 Obtain data from BEA\nstateYpc = urlopen('http://bea.gov/api/data/?UserID=3EDEAA66-4B2B-4926-83C9-FD2089747A5B&method=GetData&datasetname=RegionalData&KeyCode=PCPI_SI&Year=ALL&GeoFips=STATE&ResultFormat=JSON&')\n# result = stateYpc.readall().decode('utf-8')\nresult = stateYpc.read().decode('utf-8')\njsonResponse = json.loads(result)\n# jsonResponse['BEAAPI']['Results']['Data'][0]['GeoName']\n\n\n# In[6]:\n\n# 2.2 Construct the data frame for the per capita income series\n\n\n# 2.2.1 Initialize the dataframe\nregions = []\nyears = []\nfor element in jsonResponse['BEAAPI']['Results']['Data']:\n    if element['GeoName'] not in regions:\n        regions.append(element['GeoName'])\n    if element['TimePeriod'] not in years:\n        years.append(element['TimePeriod'])\n\ndf = np.zeros([len(years),len(regions)])\ndataY = pd.DataFrame(df,index = years,columns = regions)\n# 2.2.2 Populate the dataframe with values\nfor element in jsonResponse['BEAAPI']['Results']['Data']:\n    try:\n        dataY[element['GeoName']][element['TimePeriod']] = np.round(float(element[u'DataValue'])/float(dataP.loc[element['TimePeriod']]),2)# real\n    except:\n        dataY[element['GeoName']][element['TimePeriod']] = np.nan\n        \n# 2.2.3 Replace the state names in the index with abbreviations\ncolumns=[]\nfor r in regions:\n    if r in stateList:\n        columns.append(stateAbbr[r])\n    else:\n        columns.append(r)\n        \ndataY.columns=columns\n\n# 2.2.4 Display the data obtained from the BEA\ndataY\n\n\n# In[7]:\n\n# 3. State income data for 1840, 1880, and 1900\n\n# 3.1.1 Import Easterlin's income data\neasterlin_data = pd.read_csv('Historical Statistics of the US - Easterlin State Income Data.csv',index_col=0)\n\n# 3.1.2 Import historic CPI data\nhistoric_cpi_data=pd.read_csv('Historical Statistics of the US - cpi.csv',index_col=0)\nhistoric_cpi_data = historic_cpi_data/historic_cpi_data.loc[1929]*float(dataP.loc['1929'])\n\n\n# In[8]:\n\n# Const\ndf_1840 = easterlin_data['Income per capita - 1840 - A [cur dollars]']/float(historic_cpi_data.loc[1840])\ndf_1880 = easterlin_data['Income per capita - 1880 [cur dollars]']/float(historic_cpi_data.loc[1890])\ndf_1900 = easterlin_data['Income per capita - 1900 [cur dollars]']/float(historic_cpi_data.loc[1900])\n\ndf = pd.DataFrame({'1840':df_1840,'1880':df_1880,'1900':df_1900}).transpose()\n\n\n# In[9]:\n\ndf = pd.concat([dataY,df]).sort_index()\n\n\n# In[17]:\n\ndf.loc['1880'].sort_values()\n\n\n# In[10]:\n\n# 3. Export data to csv\nseries = dataY.sort_index()\nseries = df.sort_index()\ndropCols = [u'AK', u'HI', u'New England', u'Mideast', u'Great Lakes', u'Plains', u'Southeast', u'Southwest', u'Rocky Mountain', u'Far West']\nfor c in dropCols:\n    series = series.drop([c],axis=1)\n\nseries.to_csv('stateIncomeData.csv',na_rep='NaN')\n\n\n# In[11]:\n\nlen(dataY.columns)\n\n\n# In[12]:\n\n# 4. Export notebook to .py\nrunProcs.exportNb('stateIncomeData')\n\n","sub_path":"usConvergenceData/stateIncomeData.py","file_name":"stateIncomeData.py","file_ext":"py","file_size_in_byte":5246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"60511043","text":"import json\r\n\r\n\r\nbuffer = {\"0\":\"NoPeg\"\r\n\t,\"1\":\"No. Akun\"\r\n\t,\"2\":\"No.\"\r\n\t,\"3\":\"Nama\"\r\n\t,\"4\":\"Auto-Assign\"\r\n\t,\"5\":\"Tanggal\"\r\n\t,\"6\":\"Jam Kerja\"\r\n\t,\"7\":\"Awal tugas\"\r\n\t,\"8\":\"Akhir tugas\"\r\n\t,\"9\":\"Masuk\"\r\n\t,\"10\":\"Keluar\"\r\n\t,\"11\":\"Normal\"\r\n\t,\"12\":\"Waktu real\"\r\n\t,\"13\":\"Telat\"\r\n\t,\"14\":\"Plg Awal\"\r\n\t,\"15\":\"Bolos\"\r\n\t,\"16\":\"Waktu Lembur\"\r\n\t,\"17\":\"Waktu Kerja\"\r\n\t,\"18\":\"Status\"\r\n\t,\"19\":\"Hrs C/In\"\r\n\t,\"20\":\"Hrs C/Out\"\r\n\t,\"21\":\"Departemen\"\r\n\t,\"22\":\"NDays\"\r\n\t,\"23\":\"Akhir Pekan\"\r\n\t,\"24\":\"Hari Libur\"\r\n\t,\"25\":\"Lama Hadir\"\r\n\t,\"26\":\"NDays_OT\"\r\n\t,\"27\":\"Lembur A.Pekan\"\r\n\t,\"28\":\"Libur Lembur\"}\r\n\t\r\n\t\r\nif __name__=='__main__':\r\n\tmain()","sub_path":"absensi/test/buff_test.py","file_name":"buff_test.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"249206320","text":"\"\"\"The twisted average combinator\"\"\"\n\n\nfrom base import BaseCombinator\n\nclass TwistedAverageCombinator(BaseCombinator):\n    \"\"\"A combinator using weighted average\n    \"\"\"\n    \n    def _combine(self, combination_elements, ResultClass):\n        \"\"\"See the base class for documentation\n        See the thesis text for the explanation of twisted average\n        \"\"\"\n                        \n        # number of positive elements\n        num_positive = len(filter(lambda ce: ce.get_positiveness(), combination_elements))\n        \n        # the difference between the number of positive and negative\n        pos_dif = abs(len(combination_elements) - 2 * num_positive)\n        \n        # the average expectancy\n        avgexp = sum([ce.get_expectancy() for ce in combination_elements]) / len(combination_elements)\n        \n        # select the formula according to the expectancy\n        if avgexp <= 0.5:\n            res_exp = pow(2, pos_dif) * pow(avgexp, pos_dif + 1)\n        else:\n            res_exp = 1 - abs(pow(2, pos_dif) * pow((avgexp - 1), pos_dif + 1))\n\n        # concat the description\n        desc = self._concat_descriptions(combination_elements)\n        \n        # return the resulting class\n        return ResultClass(expectancy=res_exp, description=desc)\n","sub_path":"UnresystCD/code/adapter/unresyst/combinator/twisted_average.py","file_name":"twisted_average.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"581272228","text":"#-*- coding :utf-8 -*-\n'''\n近来，跳一跳这款小游戏风靡全国，受到不少玩家的喜爱。\n　　简化后的跳一跳规则如下：玩家每次从当前方块跳到下一个方块，如果没有跳到下一个方块上则游戏结束。\n　　如果跳到了方块上，但没有跳到方块的中心则获得1分；跳到方块中心时，若上一次的得分为1分或这是本局游戏的第一次跳跃则此次得分为2分，否则此次得分比上一次得分多两分（即连续跳到方块中心时，总得分将+2，+4，+6，+8...）。\n　　现在给出一个人跳一跳的全过程，请你求出他本局游戏的得分（按照题目描述的规则）。\n\n输入格式\n\n　　输入包含多个数字，用空格分隔，每个数字都是1，2，0之一，1表示此次跳跃跳到了方块上但是没有跳到中心，2表示此次跳跃跳到了方块上并且跳到了方块中心，0表示此次跳跃没有跳到方块上（此时游戏结束）。\n\n输出格式\n\n　　输出一个整数，为本局游戏的得分（在本题的规则下）。\n\n样例输入\n\n1 1 2 2 2 1 1 2 2 0\n\n样例输出\n\n22\n\n'''\n\nscore = 0\nlast = 1\nli = list(map(int,input().split()))\nfor v in li:\n    if v==0: #结束退出循环\n        break\n    elif v==1: #未跳到中心得1分\n        last = 1\n        score += v\n    else:\n        if last==1: #上一次的得分为1分或这是本局游戏的第一次\n            last=2\n        else:#连续跳到中心\n            last=last+2\n        score += last\nprint(score)\n","sub_path":"201700301307-wuzixuan/C/tiao.py","file_name":"tiao.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"228181616","text":"#!/usr/bin/env python3\n\nimport argparse\nimport parse_qp_output\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Parse QP values from ffmpeg-debug-qp\")\n    parser.add_argument(\"video\", type=str, help=\"Video file to generate output for\")\n    parser.add_argument(\"output\", help=\"Output file\")\n    parser.add_argument(\"-f\", \"--force\", action=\"store_true\", help=\"Overwrite output\")\n    parser.add_argument(\n        \"-of\",\n        \"--output-format\",\n        default=\"ld-json\",\n        help=\"Output format, one of: ld-json (default), json or csv\",\n    )\n    parser.add_argument(\n        \"-p\",\n        \"--path-to-tool\",\n        required=False,\n        default=\"/usr/local/bin/\",\n        help=\"Path to ffmpeg-debug-qp (defaults to /usr/local/bin/)\",\n    )\n\n    group = parser.add_mutually_exclusive_group()\n    group.add_argument(\n        \"-m\",\n        \"--include-macroblock-data\",\n        action=\"store_true\",\n        help=\"Include macroblock-level data, such as: type, interlaced and segmentation\",\n    )\n    group.add_argument(\n        \"-a\",\n        \"--compute-averages-only\",\n        action=\"store_true\",\n        help=\"Only compute the frame-average QPs\",\n    )\n\n    args = parser.parse_args()\n    parse_qp_output.set_path(args.path_to_tool)\n    if parse_qp_output.extract_qp_data(\n        args.video,\n        args.output,\n        compute_averages_only=args.compute_averages_only,\n        macroblock_data=args.include_macroblock_data,\n        force=args.force,\n        output_format=args.output_format,\n    ):\n        print(\"Data extracted to: {0}\".format(args[\"output\"]))\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"221584778","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nwdaily\n\nGeneration of daily weather from seasonal weather data produced by wgen.\nIncludes ETo and SSF calculation.\n\nMutahar Chalmers, RMS, 2018-9\n\"\"\"\n\nimport sys\nimport json\nimport numpy as np\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nfrom wgen import load_tfile, detrend\nimport wanalytics\n\nsys.path.append('/mnt/gpfs/backup/agri/code/eto/')\nimport eto\n\n\ndef qidelev(qid, elev_fpath):\n    \"\"\"Retrieve qid mean elevation from csv.\"\"\"\n\n    elevs = pd.read_csv(elev_fpath, header=None, names=['z'], index_col=0)\n    return elevs.loc[qid,'z']\n\n\ndef season2day(sttgs, histpath, stocpath, qid):\n    \"\"\"Calculate stochastic daily weather data.\"\"\"\n\n    slen = 12 // len(sttgs['seasons'])\n    asttgs, ssttgs, qsttgs = sttgs['analysis'], sttgs['synthesis'], sttgs['qtree']\n\n    # Extract latitude from qid for ETo calculation\n    _ = qsttgs.pop('qidsfile')\n    _, lat = eto.qtid2ll(int(qid), **qsttgs)\n\n    # Determine elevation above sea level if applicable\n    if len(ssttgs['elev_file']) > 0:\n        elev = qidelev(int(qid), ssttgs['elev_file'])\n    else:\n        elev = 0\n\n    # Make season map - assume standard season naming convention\n    variables, seasons = sttgs['variables'], np.array(sttgs['seasons'])\n    m2s = seasons[(np.digitize(range(1, 13), seasons, right=True) % len(seasons))]\n    smap = dict(zip(range(1, 13), m2s))\n\n    # Load analogue mapping and generate unique historic season identifier\n    anal = pd.read_parquet(stocpath+'/analogue.parquet').sort_values(['histyear','season'])\n    offset = anal.iloc[0]['histyear']*12 + anal.iloc[0]['season']\n    anal['key'] = offset + anal['season'].diff().abs().cumsum().fillna(0)\n\n    # Load historic daily data\n    histdailypath = ssttgs['histdailypath']\n    hist_daily = pd.read_parquet(histdailypath+f'/{qid}.parquet').sort_index()\n\n    # Restrict temporal extent consistently across all variables\n    #start, end = asttgs['start'], asttgs['end']\n    #yrs = hist_daily.index.get_level_values('year')\n    #mths = hist_daily.index.get_level_values('month')\n    #hist_daily = hist_daily[(yrs>=start[0])&(yrs<=end[0])&(mths>=start[1])&(mths<=end[1])]\n\n    # Load trend file and detrend\n    trends = {v: load_tfile(sttgs['analysis'], v) for v in variables}\n    hist_daily = pd.concat([detrend(hist_daily[v], trends[v], asttgs['pivot_year'], qid)\n                           for v in variables], axis=1)\n\n    # Calculate reference evapotranspiration (ETo) for detrended data\n    hist_daily['eto'] = eto.calc_ETo(hist_daily, lat, elevation=elev)\n    hist_daily = hist_daily[variables+['eto']]\n\n    # Generate unique historic season identifiers\n    hist_daily = hist_daily.reset_index().rename(columns={'year':'histyear'})\n    seas = hist_daily['month'].map(smap.get)\n    offset = hist_daily['histyear'][0]*12 + seas[0]\n    key = offset + seas.diff().abs().cumsum().fillna(0)\n    hist_daily['key'] = key.values\n    hist_daily['QID'] = int(qid)\n\n    # Load historic and stochastic seasonal data\n    hist_seas = pq.read_pandas(histpath+'/hist_seas.parquet', columns=[qid])\n    hist_seas = hist_seas.to_pandas()[qid].unstack('variable').reset_index().rename(columns={'year':'histyear'})\n    stoc_seas = pq.read_pandas(stocpath+'/stoc_seas.parquet', columns=[qid])\n    stoc_seas = stoc_seas.to_pandas()[qid].unstack('variable').reset_index()\n\n    # Allocate historic daily data to corresponding stochastic year\n    hist_anal_daily = anal.merge(hist_daily, on='key', suffixes=('_anal',''))\n\n    # Allocate historic seasonal data to corresponding stochastic year\n    hist_anal = anal.merge(hist_seas, on=['histyear','season'])\n\n    # Calculate Seasonal Scaling Factors (SSFs)\n    ssfs = hist_anal.merge(stoc_seas, on=['year','season'], suffixes=('_hist','_stoc'))\n    for v in variables:\n        ssfs[v] = ssfs[v+'_stoc']/ssfs[v+'_hist']\n    ssfs = ssfs[['year','key']+variables]\n\n    # Combine historic daily data with SSFs to obtain daily stochastic data\n    hist_ssf = hist_anal_daily.merge(ssfs, on=['year','key'], suffixes=('_hist','_ssf'))\n\n    # Adjust stochastic year for those seasons which straddle calendar years\n    hist_ssf['year'] = hist_ssf['year']+hist_ssf['histyear']-hist_ssf['histyear_anal']\n    hist_ssf = hist_ssf.set_index(['year','month','day','doy','key'])\n\n    # Calculate main stochastic daily weather DataFrame\n    stoc = pd.concat({v: (hist_ssf[v+'_hist']*hist_ssf[v+'_ssf']) for v in variables},\n                     axis=1).sort_index()\n\n    # Calculate reference evapotranspiration (ETo)\n    _, lat = eto.qtid2ll(int(qid), **qsttgs)\n    stoc['eto'] = eto.calc_ETo(stoc, np.deg2rad(lat), elevation=elev)\n\n    # Final format for stochastic daily data\n    stoc = stoc[variables+['eto']].astype(np.float32)\n\n    # Calculate SSFs for ET0\n    hist_eto_seas = hist_ssf['eto'].sum(level=['year','key'])\n    stoc_eto_seas = stoc['eto'].sum(level=['year','key'])\n    eto_ssf = stoc_eto_seas/hist_eto_seas\n\n    # Add QID and ETo to ssfs, and make 32-bit\n    ssfs['QID'] = int(qid)\n    ssfs = ssfs.set_index(['year','key','QID'])\n    ssfs['eto'] = eto_ssf\n    ssfs[variables+['eto']] = ssfs[variables+['eto']].astype(np.float32)\n    ssfs = ssfs.sort_index().reset_index()\n    ssfs[['year','key','QID']] = ssfs[['year','key','QID']].astype(np.int32)\n\n    # Make hist_daily 32-bit\n    cols = ['histyear','month','day','doy','key','QID']\n    hist_daily[cols] = hist_daily[cols].astype(np.int32)\n    hist_daily[variables+['eto']] = hist_daily[variables+['eto']].astype(np.float32)\n\n    # Check that seasonal aggregation of daily data equals seasonal data for all years\n    stoc_seas = stoc_seas.set_index(['year','season']).sort_index()\n    stoc_seas_fromdaily = agg_season(stoc, sttgs)\n    test = np.allclose(stoc_seas, stoc_seas_fromdaily)\n\n    return stoc, ssfs, hist_daily, test\n\n\ndef histSSF2stoc(hist_daily, ssfs, sttgs):\n    \"\"\"\n    Combine historic daily data with Stochastic Seasonal Factors (SSFs) to get\n    daily stochastic data.\n    \"\"\"\n\n    variables, seasons = list(sttgs['variables']), np.array(sttgs['seasons'])\n\n    # Add ETo to list of variables\n    variables.append('eto')\n\n    # Make season map - assume standard season naming convention\n    m2s = seasons[(np.digitize(range(1, 13), seasons, right=True) % len(seasons))]\n    smap = dict(zip(range(1, 13), m2s))\n\n    # Combine historic daily data with SSFs to obtain daily stochastic data\n    ssf_hist = ssfs.merge(hist_daily, on=['key','QID'], suffixes=('_ssf','_hist'))\n\n    # Subtract 1 from stochastic year for those seasons which straddle calendar years\n    ssf_hist['season'] = ssf_hist['month'].map(smap)\n    straddle = ssf_hist['month'] > ssf_hist['season']\n    ssf_hist.loc[straddle, 'year'] = ssf_hist.loc[straddle, 'year'] - 1\n    ssf_hist = ssf_hist.set_index(['year','month','day','doy'])\n\n    # Calculate main stochastic daily weather DataFrame\n    stoc = pd.concat({v: (ssf_hist[v+'_hist']*ssf_hist[v+'_ssf']) for v in variables},\n                     axis=1).sort_index()\n    return stoc\n\n\ndef agg_season(stoc, sttgs):\n    \"\"\"Seasonally aggregate daily data.\"\"\"\n\n    seasagg = {}\n    slen = 12 // len(sttgs['seasons'])\n    for v in sttgs['variables']:\n        if sttgs['agg'][v] == 'sum':\n            seasagg[v] = stoc[v].sum(level=['year','month']).rolling(slen).sum()\n        else:\n            seasagg[v] = stoc[v].mean(level=['year','month']).rolling(slen).mean()\n    seasagg = pd.concat(seasagg, axis=1).sort_index()\n    return seasagg[seasagg.index.get_level_values('month').isin(sttgs['seasons'])]\n\n\ndef agg_month(wdata, sttgs):\n    \"\"\"Aggregate daily data by month.\"\"\"\n\n    # First, generate monthly aggregated DataFrames from daily data\n    wdata_m_agg = wdata.mean(level=['year','month'])\n    for v in sttgs['variables']:\n        if sttgs['agg'][v] == 'sum':\n            wdata_m_agg[v] = wdata[v].sum(level=['year','month'])\n\n    wdata_m_max = wdata.max(level=['year','month'])\n    wdata_m_min = wdata.min(level=['year','month'])\n    wdata_m_var = wdata.var(level=['year','month'])\n\n    # Handle ETo explicitly since it won't be in settings file\n    wdata_m_agg['eto'] = wdata['eto'].sum(level=['year','month'])\n    wdata_m_max['eto'] = wdata['eto'].max(level=['year','month'])\n    wdata_m_min['eto'] = wdata['eto'].min(level=['year','month'])\n    wdata_m_var['eto'] = wdata['eto'].var(level=['year','month'])\n\n    return wdata_m_agg, wdata_m_max, wdata_m_min, wdata_m_var\n\n\ndef run_analytics(wdata, sttgs, qid, histflag):\n    \"\"\"Generate analytics on monthly weather data.\"\"\"\n\n    # Load climate indices and combine with wdata\n    clim = wanalytics2.load_climate(sttgs, hist=histflag).reindex(wdata.index)\n    wdata = pd.concat([wdata, clim], axis=1).dropna(axis=1)\n\n    # Calculate climatology and quantiles\n    wdata_clima, wdata_quant = wanalytics2.climquant(wdata, qid)\n\n    # Calculate cross-correlations\n    wdata_xcorr = wanalytics2.xcorr(wdata, qid)\n    return wdata_clima, wdata_quant, wdata_xcorr\n\n\nif __name__ == '__main__':\n    _, sttgsfile, histpath, stocpath, qid = sys.argv\n\n    # Load settings file from JSON to dict\n    with open(sttgsfile, 'r') as f:\n        sttgs = json.load(f)\n    seasons = sttgs['seasons']\n\n    # Generate SSFs and historic daily files and write to disk\n    stoc, ssfs, hist_daily, test = season2day(sttgs, histpath, stocpath, qid)\n    ssfs.to_parquet(stocpath+f'/ssfs/{qid}.parquet')\n    hist_daily.to_parquet(stocpath+f'/histdaily/{qid}.parquet')\n\n    # Write QID-level results only if qid is in list of reference QIDs\n    if int(qid) in sttgs['analytics']['qids_ref']:\n        stoc.to_parquet(stocpath+f'/daily/{qid}.parquet')\n\n    # Run historic and stochastic analytics and write to file\n    # Need to modify format of hist_daily to 'standard' format\n    hist = hist_daily.rename(columns={'histyear':'year'}\n                             ).set_index(['year','month','day','doy']\n                                         ).drop(['key','QID'], axis=1)\n\n    # Calculate daily quantiles before aggregating by month\n    hist_m_day_quant = wanalytics2.dailyquant(hist, int(qid))\n    stoc_m_day_quant = wanalytics2.dailyquant(stoc, int(qid))\n\n    # Calculate monthly aggregates\n    hist_m_agg, hist_m_max, hist_m_min, hist_m_var = agg_month(hist, sttgs)\n    stoc_m_agg, stoc_m_max, stoc_m_min, stoc_m_var = agg_month(stoc, sttgs)\n\n    # Calculate climatology, quantiles and cross-correlations for historic\n    hist_m_agg_clima, hist_m_agg_quant, hist_m_agg_xcorr = run_analytics(hist_m_agg, sttgs, qid, histflag=True)\n    hist_m_max_clima, hist_m_max_quant, hist_m_max_xcorr = run_analytics(hist_m_max, sttgs, qid, histflag=True)\n    hist_m_min_clima, hist_m_min_quant, hist_m_min_xcorr = run_analytics(hist_m_min, sttgs, qid, histflag=True)\n    hist_m_var_clima, hist_m_var_quant, hist_m_var_xcorr = run_analytics(hist_m_var, sttgs, qid, histflag=True)\n\n    # Calculate climatology, quantiles and cross-correlations for stochastic\n    stoc_m_agg_clima, stoc_m_agg_quant, stoc_m_agg_xcorr = run_analytics(stoc_m_agg, sttgs, qid, histflag=False)\n    stoc_m_max_clima, stoc_m_max_quant, stoc_m_max_xcorr = run_analytics(stoc_m_max, sttgs, qid, histflag=False)\n    stoc_m_min_clima, stoc_m_min_quant, stoc_m_min_xcorr = run_analytics(stoc_m_min, sttgs, qid, histflag=False)\n    stoc_m_var_clima, stoc_m_var_quant, stoc_m_var_xcorr = run_analytics(stoc_m_var, sttgs, qid, histflag=False)\n\n    # Calculate two-sample K-S p-values\n    m_agg_ks = wanalytics2.similarity_KS(hist_m_agg, stoc_m_agg, qid)\n    m_max_ks = wanalytics2.similarity_KS(hist_m_max, stoc_m_max, qid)\n    m_min_ks = wanalytics2.similarity_KS(hist_m_min, stoc_m_min, qid)\n    m_var_ks = wanalytics2.similarity_KS(hist_m_var, stoc_m_var, qid)\n\n    # Write to file\n    hist_m_day_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_day_quant')\n\n    hist_m_agg_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_agg_clima')\n    hist_m_max_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_max_clima')\n    hist_m_min_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_min_clima')\n    hist_m_var_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_var_clima')\n\n    hist_m_agg_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_agg_quant')\n    hist_m_max_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_max_quant')\n    hist_m_min_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_min_quant')\n    hist_m_var_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_var_quant')\n\n    hist_m_agg_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_agg_xcorr')\n    hist_m_max_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_max_xcorr')\n    hist_m_min_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_min_xcorr')\n    hist_m_var_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'hist_m_var_xcorr')\n\n    stoc_m_day_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_day_quant')\n\n    stoc_m_agg_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_agg_clima')\n    stoc_m_max_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_max_clima')\n    stoc_m_min_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_min_clima')\n    stoc_m_var_clima.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_var_clima')\n\n    stoc_m_agg_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_agg_quant')\n    stoc_m_max_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_max_quant')\n    stoc_m_min_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_min_quant')\n    stoc_m_var_quant.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_var_quant')\n\n    stoc_m_agg_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_agg_xcorr')\n    stoc_m_max_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_max_xcorr')\n    stoc_m_min_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_min_xcorr')\n    stoc_m_var_xcorr.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'stoc_m_var_xcorr')\n\n    m_agg_ks.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'm_agg_ks')\n    m_max_ks.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'm_max_ks')\n    m_min_ks.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'm_min_ks')\n    m_var_ks.to_hdf(stocpath+f'/../analytics/raw/{qid}.h5', 'm_var_ks')\n\n    # Identify QIDs which don't have a perfect match between stochastic seasonal\n    # and seasonally aggregated stochastic daily values\n    if not test:\n        with open(stocpath+f'/checks/{qid}.MISMATCH', 'w') as f:\n            f.write('Mismatch between aggregated daily and seasonal data.')\n\n","sub_path":"wgen/wgen/wdaily.py","file_name":"wdaily.py","file_ext":"py","file_size_in_byte":14557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"361861162","text":"import ST7789\nimport spidev as SPI\nimport RPi.GPIO as GPIO\nimport time\nimport getwords\n\nfrom embit import script\nfrom embit import bip32\nfrom embit import bip39\nfrom embit.networks import NETWORKS\nfrom embit import psbt\nfrom binascii import unhexlify, hexlify\n\nfrom PIL import Image\nfrom PIL import ImageDraw\nfrom PIL import ImageFont\nimport qrcode\n\n#GPIO define\nRST_PIN        = 25\nCS_PIN         = 8\nDC_PIN         = 24\n\nKEY_UP_PIN     = 6\nKEY_DOWN_PIN   = 19\nKEY_LEFT_PIN   = 5\nKEY_RIGHT_PIN  = 26\nKEY_PRESS_PIN  = 13\n\nKEY1_PIN       = 21\nKEY2_PIN       = 20\nKEY3_PIN       = 16\n\n# Raspberry Pi pin configuration:\nRST = 27\nDC = 25\nBL = 24\nbus = 0\ndevice = 0\n\n#init GPIO\nGPIO.setmode(GPIO.BCM)\nGPIO.setup(KEY_UP_PIN,      GPIO.IN, pull_up_down=GPIO.PUD_UP)    # Input with pull-up\nGPIO.setup(KEY_DOWN_PIN,    GPIO.IN, pull_up_down=GPIO.PUD_UP)  # Input with pull-up\nGPIO.setup(KEY_LEFT_PIN,    GPIO.IN, pull_up_down=GPIO.PUD_UP)  # Input with pull-up\nGPIO.setup(KEY_RIGHT_PIN,   GPIO.IN, pull_up_down=GPIO.PUD_UP) # Input with pull-up\nGPIO.setup(KEY_PRESS_PIN,   GPIO.IN, pull_up_down=GPIO.PUD_UP) # Input with pull-up\nGPIO.setup(KEY1_PIN,        GPIO.IN, pull_up_down=GPIO.PUD_UP)      # Input with pull-up\nGPIO.setup(KEY2_PIN,        GPIO.IN, pull_up_down=GPIO.PUD_UP)      # Input with pull-up\nGPIO.setup(KEY3_PIN,        GPIO.IN, pull_up_down=GPIO.PUD_UP)      # Input with pull-up\n\n# 240x240 display with hardware SPI:\ndisp = ST7789.ST7789(SPI.SpiDev(bus, device),RST, DC, BL)\ndisp.Init()\n\n# Create blank image for drawing.\n# Make sure to create image with mode '1' for 1-bit color.\nwidth = 240\nheight = 240\nimage = Image.new('RGB', (width, height))\n\n# Get drawing object to draw on image.\ndraw = ImageDraw.Draw(image)\n\nimpact18 = ImageFont.truetype('/usr/share/fonts/truetype/msttcorefonts/Impact.ttf', 18)\nimpact22 = ImageFont.truetype('/usr/share/fonts/truetype/msttcorefonts/Impact.ttf', 22)\n\ndef showqrcodes(xpubstring):\n    localxpubstring = xpubstring\n\n    # draw.rectangle((0, 0, width, height), outline=0, fill=0)\n    # draw.text((20, 110), \"Creating Animated QR Code...\", fill=\"ORANGE\", font=impact18)\n    # disp.ShowImage(image, 0, 0)\n\n    qrdata = localxpubstring\n\n    stringlength = len(qrdata)\n\n    # print(\"The length of the string is: \" + str(stringlength))\n    numofchunks = (stringlength // 100) + 1\n    staticnumofchunks = numofchunks\n    listcounter = 1\n\n    start = 0\n    stop = 99\n    chunklist = [\"p\" + str(listcounter) + \"of\" + str(staticnumofchunks) + \" \" + qrdata[start:stop]]\n    # print(\"The start is: \" + str(start) + \" and the end is: \" + str(stop))\n    print(chunklist[0])\n    qrimagelist = [qrcode.make(chunklist[0]).resize((240, 240)).convert('RGB')]\n\n    while numofchunks > 1:\n        start = start + 100\n        stop = stop + 100\n        if stop > stringlength:\n            stop = stringlength  # - 1\n        chunklist.append(\"p\" + str(listcounter + 1) + \"of\" + str(staticnumofchunks) + \" \" + qrdata[start - 1:stop])\n        print(chunklist[listcounter])\n        qrimagelist.append(qrcode.make(chunklist[listcounter]).resize((240, 240)).convert('RGB'))\n        listcounter = listcounter + 1\n        numofchunks = numofchunks - 1\n\n    showingqrs = True\n    displayedqr = 0\n\n    while showingqrs == True:\n        disp.ShowImage(qrimagelist[displayedqr], 0, 0)\n        time.sleep(0.3)\n        if displayedqr < staticnumofchunks - 1:\n            displayedqr = displayedqr + 1\n            time.sleep(0.3)\n        else:\n            displayedqr = 0\n            time.sleep(0.3)\n        if GPIO.input(KEY_RIGHT_PIN) == GPIO.LOW:  # button is released\n            time.sleep(0.2)\n            showingqrs = False\n        else:\n            time.sleep(0.3)\n\ndef make_xpub(runstate):\n    localrunstate = getwords.gather_24_words(runstate)\n\n    draw.rectangle((0, 0, width, height), outline=0, fill=0)\n    draw.text((20, 110), \"Creating Animated QR Code\", fill=\"ORANGE\", font=impact18)\n    disp.ShowImage(image, 0, 0)\n\n    seed = bip39.mnemonic_to_seed(localrunstate[1])\n    root = bip32.HDKey.from_seed(seed)  # removed: version=NETWORKS[\"main\"][\"xprv\"]\n\n    # first let's get the root fingerprint\n    fingerprint = root.child(0).fingerprint\n    hardened_derivation = \"m/48h/0h/0h/2h\"\n\n    # derive account according to bip84\n    bip48_xprv = root.derive(hardened_derivation)\n\n    # corresponding master public key:\n    bip48_xpub = bip48_xprv.to_public()\n\n    xpubstring = \"[%s%s]%s\" % (\n        hexlify(fingerprint).decode('utf-8'),\n        hardened_derivation[1:],\n        bip48_xpub.to_base58(NETWORKS[\"main\"][\"Zpub\"]))\n\n    print(xpubstring)\n\n    showqrcodes(xpubstring)\n\n    localrunstate[0] = 0\n    localrunstate[1] = \"\"\n\n    print(localrunstate)\n\n    return localrunstate","sub_path":"xpubmake.py","file_name":"xpubmake.py","file_ext":"py","file_size_in_byte":4707,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"451351872","text":"from django.contrib.auth.models import User\nfrom django.utils.text import slugify\nfrom rest_framework import viewsets, permissions, mixins\nfrom todo.models import Todo\nfrom todo.serializers import TodoSerializer, UserSerializer\nfrom todo.permissions import IsOwnerOrAdminUser, IsSelfOrAdminUser\n\n# Create your views here.\n\nclass TodoViewSet(viewsets.ModelViewSet):\n    serializer_class = TodoSerializer\n    permission_classes = (\n        IsOwnerOrAdminUser,\n    )\n    lookup_field = 'slug'\n\n    def perform_create(self, serializer):\n        slug_target_value = serializer.validated_data['task']\n        slug = slugify(slug_target_value, allow_unicode=True)\n        serializer.save(owner=self.request.user, slug=slug)\n\n    def perform_update(self, serializer):\n        slug_target_value = serializer.validated_data['task']\n        slug = slugify(slug_target_value, allow_unicode=True)\n        serializer.save(slug=slug)\n\n    def get_queryset(self):\n        # get object that object owner is authenticated (logged in) user\n        current_user = self.request.user\n        if current_user.is_staff:\n            return Todo.objects.all()\n\n        return Todo.objects.filter(owner=current_user)\n\nclass UserViewSet(viewsets.ModelViewSet):\n    serializer_class = UserSerializer\n    permission_classes = (\n        IsSelfOrAdminUser,\n    )\n    lookup_field = 'username'\n\n    def get_queryset(self):\n        current_user = self.request.user\n        if current_user.is_staff:\n            return User.objects.all()\n\n        return User.objects.filter(username=current_user.username)","sub_path":"src/todo/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"141925032","text":"import time\nimport math\nfrom PI_ADC import *\n\nadc = PI_ADC_MONITOR()\n\nwhile True:\n    for i in range(adc.num_channels):\n        print(\"ADC:\",i)\n        print(\"Value:\",round(adc.get_adc_avg(i),2))\n        if (adc.channel_triggered(i)):\n            print(\"Stop Squeezin\")\n            \n        \n        time.sleep(0.3)","sub_path":"OLD/Sensor_new/test_adc.py","file_name":"test_adc.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"618102219","text":"from app.models import TimestampMixin, db\n\n\nclass Url(db.Model, TimestampMixin):\n    __tablename__ = 'url'\n\n    def __init__(self, **kwargs):\n        for k, v in kwargs.items():\n            setattr(self, k, v)\n\n    id = db.Column(db.Integer, primary_key=True, autoincrement=True)\n    data_id = db.Column(db.Integer, nullable=False)\n    entered_url = db.Column(db.String(255), unique=True)\n","sub_path":"app/models/url.py","file_name":"url.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"87111328","text":"import json\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.exceptions import ValidationError\nfrom django.db.models import Count\nfrom django.http import Http404, HttpResponse\nfrom django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.views.decorators.http import require_safe, require_http_methods\n\nfrom concepts.models import Concept, ItemDependency\nfrom equations.models import RenderedEquation\nfrom keywords.models import Keyword, ItemKeyword\nfrom main.elasticsearch import index_item, item_search, get_item_source\nfrom main.item_helpers import get_refs_and_render, item_to_markup, delete_item\nfrom main.views.helpers import prepare_item_view_list, LIST_PAGE_SIZE\nfrom mathitems.models import ItemTypes, MathItem, IllegalMathItem\nfrom project.paginator import QuerySetPaginator, Paginator, PaginatorError\nfrom validations.models import ItemValidation, Source\nfrom userdata.permissions import has_perm, require_perm, Perms\n\n# import logging\n# logger = logging.getLogger(__name__)\n\n\ndef decode_document(node, eqn_set, concept_set):\n    if 'concept' in node:\n        concept_set.add(node['concept'])\n    if 'eqn' in node:\n        eqn_set.add(node['eqn'])\n    if node.get('children'):\n        children = [decode_document(child, eqn_set, concept_set)\n                    for child in node['children']]\n        return dict(node, children=children)\n    return node\n\n\ndef item_render(item):\n    eqn_set = set()\n    concept_set = set()\n    document = decode_document(json.loads(item.body), eqn_set, concept_set)\n    eqn_map = {reqn.pk: {'html': reqn.html}\n               for reqn in RenderedEquation.objects.filter(pk__in=eqn_set)}\n    concept_map = {concept.id: concept.name\n                   for concept in Concept.objects.filter(id__in=concept_set)}\n    result = get_refs_and_render(item.item_type, document, eqn_map, concept_map)\n    if result['errors']:\n        raise IllegalMathItem('Error in published item {}: {}'.format(item.id, result['error']))\n    return result\n\n\n@require_safe\ndef show_item(request, id_str):\n    try:\n        item = MathItem.objects.get_by_name(id_str)\n    except MathItem.DoesNotExist:\n        raise Http404('Item does not exist')\n    item_data = item_render(item)\n    context = {\n        'title': str(item),\n        'item': item,\n        'item_data': item_data,\n        'keywords': Keyword.objects.filter(itemkeyword__item=item).order_by('name').all(),\n        'validations': item.itemvalidation_set.all(),\n        'can_add_validation': has_perm(Perms.VALIDATION, request.user),\n        'kw_edit_link': has_perm(Perms.KEYWORD, request.user) and reverse('edit-item-keywords', args=[item.get_name()]),\n        'can_add_to_doc': has_perm(Perms.DOCUMENT, request.user),\n    }\n    if item.item_type == ItemTypes.THM:\n        context['can_add_proof'] = True\n        context['proofs'] = list(MathItem.objects.filter(item_type=ItemTypes.PRF, parent=item).order_by('id'))\n    if item.item_type == ItemTypes.PRF:\n        context['subtitle'] = 'of {}'.format(item.parent)\n        context['parent_item'] = item.parent\n        context['parent_item_data'] = item_render(item.parent)\n    return render(request, 'mathitems/show.html', context)\n\n\n@require_http_methods(['GET', 'POST'])\n@login_required\n@require_perm('keyword')\ndef edit_item_keywords(request, id_str):\n    try:\n        item = MathItem.objects.get_by_name(id_str)\n    except MathItem.DoesNotExist:\n        raise Http404('Item does not exist')\n    if request.method == 'POST':\n        if 'delete' in request.POST:\n            itemkw = ItemKeyword.objects.get(pk=int(request.POST['delete']))\n            itemkw.delete()\n        else:\n            if request.POST['kw']:\n                keyword, _ = Keyword.objects.get_or_create(name=request.POST['kw'])\n                itemkw, _ = ItemKeyword.objects.get_or_create(\n                                item=item, keyword=keyword, defaults={'created_by': request.user})\n        index_item(item)\n    item_data = item_render(item)\n    context = {\n        'title': str(item),\n        'item': item,\n        'item_data': item_data,\n        'itemkeywords': ItemKeyword.objects.filter(item=item).order_by('keyword__name').all(),\n    }\n    if item.item_type == ItemTypes.PRF:\n        context['subtitle'] = 'of {}'.format(item.parent)\n        context['parent_item'] = item.parent\n        context['parent_item_data'] = item_render(item.parent)\n    return render(request, 'mathitems/edit-item-keywords.html', context)\n\n\n@require_http_methods(['GET', 'POST'])\n@login_required\n@require_perm('validation')\ndef add_item_validation(request, id_str):\n    try:\n        item = MathItem.objects.get_by_name(id_str)\n    except MathItem.DoesNotExist:\n        raise Http404('Item does not exist')\n    item_data = item_render(item)\n    if request.method == 'POST':\n        source = Source.objects.get(id=int(request.POST['source']))\n        ItemValidation.objects.create(created_by=request.user,\n                                      item=item,\n                                      source=source,\n                                      location=request.POST['location'])\n        return redirect('show-item', id_str)\n    context = {\n        'title': str(item),\n        'item': item,\n        'item_data': item_data,\n        'types': Source.SOURCE_TYPE_CHOICES\n    }\n    if 'type' in request.GET:\n        type_slug = request.GET['type']\n        type_display = dict(Source.SOURCE_TYPE_CHOICES).get(type_slug)\n        if type_display:\n            context['type_slug'] = type_slug\n            context['type_display'] = type_display\n            if 'value' in request.GET:\n                value = request.GET['value']\n                try:\n                    source, _ = Source.objects.get_or_create(source_type=type_slug, source_value=value)\n                    context['source'] = source\n                except ValidationError as ve:\n                    context['error'] = '; '.join(ve.messages)\n        else:\n            context['error'] = 'Illegal validation type'\n    return render(request, 'mathitems/add-item-validation.html', context)\n\n\ndef get_latest_mathitems(item_type, no_vals=False, no_proofs=False):\n    query_set = MathItem.objects.filter(item_type=item_type).order_by('-created_at')\n    if no_vals:\n        query_set = query_set.annotate(vals=Count('itemvalidation')).filter(vals=0)\n    if no_proofs:\n        query_set = query_set.annotate(proofs=Count('mathitem')).filter(proofs=0)\n    return query_set\n\n\ndef get_first_elements_check_more(query, count):\n    items = query[:(count+1)]\n    return items[:count], len(items) > count\n\n\n@require_safe\ndef def_home(request):\n    latest, more_latest = get_first_elements_check_more(get_latest_mathitems(ItemTypes.DEF), 5)\n    no_vals, more_no_vals = get_first_elements_check_more(get_latest_mathitems(ItemTypes.DEF, no_vals=True), 1)\n\n    return render(request, 'mathitems/definitions-home.html', {\n        'title': 'Definitions',\n        'latest': prepare_item_view_list(latest),\n        'no_vals': prepare_item_view_list(no_vals),\n        'no_defs': Concept.objects.filter(conceptmeta__def_count=0, conceptmeta__ref_count__gt=0)\n                                  .order_by('-conceptmeta__ref_count', 'name'),\n        'latest_link': more_latest and reverse('def-list'),\n        'no_vals_link': more_no_vals and reverse('def-no-vals'),\n    })\n\n\n@require_safe\ndef thm_home(request):\n    latest, more_latest = get_first_elements_check_more(get_latest_mathitems(ItemTypes.THM), 5)\n    wo_proof, more_wo_proof = get_first_elements_check_more(get_latest_mathitems(ItemTypes.THM, no_proofs=True), 5)\n    no_vals, more_no_vals = get_first_elements_check_more(get_latest_mathitems(ItemTypes.THM, no_vals=True), 5)\n\n    return render(request, 'mathitems/theorems-home.html', {\n        'title': 'Theorems',\n        'latest': prepare_item_view_list(latest),\n        'wo_proof': prepare_item_view_list(wo_proof),\n        'no_vals': prepare_item_view_list(no_vals),\n        'latest_link': more_latest and reverse('thm-list'),\n        'wo_proof_link': more_wo_proof and reverse('thm-wo-proof'),\n        'no_vals_link': more_no_vals and reverse('thm-no-vals'),\n    })\n\n\n@require_safe\ndef prf_home(request):\n    latest, more_latest = get_first_elements_check_more(get_latest_mathitems(ItemTypes.PRF), 5)\n    no_vals, more_no_vals = get_first_elements_check_more(get_latest_mathitems(ItemTypes.PRF, no_vals=True), 5)\n\n    return render(request, 'mathitems/proofs-home.html', {\n        'title': 'Proofs',\n        'latest': prepare_item_view_list(latest),\n        'no_vals': prepare_item_view_list(no_vals),\n        'latest_link': more_latest and reverse('prf-list'),\n        'no_vals_link': more_no_vals and reverse('prf-no-vals'),\n    })\n\n\ndef item_list_page(request, title, query):\n    try:\n        paginator = QuerySetPaginator(request, query, LIST_PAGE_SIZE)\n    except PaginatorError as pe:\n        return redirect(pe.url)\n\n    return render(request, 'mathitems/item-list-page.html', {\n        'title': title,\n        'items': prepare_item_view_list(paginator.get_items()),\n        'paginator': paginator\n    })\n\n\n@require_safe\ndef def_list(request):\n    return item_list_page(request, 'Latest Definitions', get_latest_mathitems(ItemTypes.DEF))\n\n\n@require_safe\ndef def_no_vals(request):\n    return item_list_page(request, 'Definitions Without Validations', get_latest_mathitems(ItemTypes.DEF, no_vals=True))\n\n\n@require_safe\ndef thm_list(request):\n    return item_list_page(request, 'Latest Theorems', get_latest_mathitems(ItemTypes.THM))\n\n\n@require_safe\ndef thm_wo_proof(request):\n    return item_list_page(request, 'Theorems Without Proof', get_latest_mathitems(ItemTypes.THM, no_proofs=True))\n\n\n@require_safe\ndef thm_no_vals(request):\n    return item_list_page(request, 'Theorems Without Validations', get_latest_mathitems(ItemTypes.THM, no_vals=True))\n\n\n@require_safe\ndef prf_list(request):\n    return item_list_page(request, 'Latest Proofs', get_latest_mathitems(ItemTypes.PRF))\n\n\n@require_safe\ndef prf_no_vals(request):\n    return item_list_page(request, 'Proofs Without Validations', get_latest_mathitems(ItemTypes.PRF, no_vals=True))\n\n\ndef item_search_helper(request, type_name):\n    query = request.GET['q']\n    try:\n        paginator = Paginator(request, LIST_PAGE_SIZE)\n        results, total = item_search(query, type_name, paginator.per_page * (paginator.page - 1), paginator.per_page)\n        paginator.set_count(total)\n    except PaginatorError as pe:\n        return redirect(pe.url)\n\n    name = type_name.capitalize()\n    return render(request, 'mathitems/item-search-page.html', {\n        'title': '{} Search'.format(name),\n        'query': query,\n        'items': prepare_item_view_list(MathItem.objects.get_by_name(name) for name in results),\n        'paginator': paginator\n    })\n\n\n@require_safe\ndef def_search(request):\n    return item_search_helper(request, 'definition')\n\n\n@require_safe\ndef thm_search(request):\n    return item_search_helper(request, 'theorem')\n\n\n@require_safe\ndef prf_search(request):\n    return item_search_helper(request, 'proof')\n\n\n@require_safe\ndef dump_item(request, id_str):\n    try:\n        item = MathItem.objects.get_by_name(id_str)\n    except MathItem.DoesNotExist:\n        raise Http404('Item does not exist')\n    markup = item_to_markup(item)\n    return render(request, 'mathitems/dump.txt', {\n        'item': item,\n        'markup': markup,\n        'validations': item.itemvalidation_set.all()\n    }, content_type='text/plain')\n\n\n@require_http_methods(['GET', 'POST'])\ndef item_meta(request, id_str):\n    try:\n        item = MathItem.objects.get_by_name(id_str)\n    except MathItem.DoesNotExist:\n        raise Http404('Item does not exist')\n    if request.method == 'POST':\n        res = delete_item(item)\n        return HttpResponse(res, content_type='text/plain')\n    elastic = get_item_source(id_str)\n    return render(request, 'mathitems/meta.html', {\n        'title': str(item),\n        'elastic': json.dumps(elastic, indent='  '),\n        'can_delete': has_perm(Perms.DELETE, request.user) and not ItemDependency.objects.filter(uses=item).exists(),\n        'dependents': MathItem.objects.filter(itemdep_item__uses=item).order_by('id')\n    })\n","sub_path":"main/views/mathitems.py","file_name":"mathitems.py","file_ext":"py","file_size_in_byte":12191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"281487438","text":"class Solution(object):\n    def twoSumV1(self, nums):\n        # print \"nums\", nums\n        target = int (-1 * nums[0])\n        r = []\n        lo = 1\n        high = len(nums) - 1\n        while lo < high:\n            # print \"lo, high\", lo, high\n            # print target, nums[lo], nums[high]\n            if nums[lo] + nums[high]  == target:\n                r.append([nums[lo], nums[high]])\n                while lo < high and nums[lo] == nums[lo+1]:\n                    lo += 1\n                while lo < high and nums[high] == nums[high-1]:\n                    high -= 1\n                lo += 1\n                high -= 1\n            elif nums[lo] + nums[high] < target:\n                lo += 1\n            else:\n                high -= 1\n        return r\n\n    def twoSumV2(self, nums):\n        target = int (-1 * nums[0])\n        pool = nums[1:]\n        r = []\n        remain = {}\n        result = {}\n        for n in pool:\n            if n in remain and (remain[n],n) not in result:\n                r.append([remain[n],n])\n                result[(remain[n],n)] = 1\n            remain[target-n] = n\n        return r\n\n    def threeSum(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        nums.sort()\n        r = []\n        for index,n in enumerate(nums):\n            if n > 0:\n                break\n            if index > 0 and n == nums[index-1]:\n                continue\n            # twoSumList = self.twoSumV1(nums[index:])\n            twoSumList = self.twoSumV2(nums[index:]) # faster\n            for tResult in twoSumList:\n                tResult.append(n)\n                r.append(tResult)\n        return r\nsol = Solution()\nprint(sol.threeSum([0,0,0]))\n","sub_path":"2018_google/015_sol.py","file_name":"015_sol.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"449926789","text":"#!/usr/bin/env python3\nimport os\nimport tornado.ioloop\nimport tornado.web\nimport tornado.log\nimport json\nimport requests\n\nfrom jinja2 import \\\n  Environment, PackageLoader, select_autoescape\n\nENV = Environment(\n  loader=PackageLoader('weather', 'templates'),\n  autoescape=select_autoescape(['html', 'xml'])\n)\n\nclass TemplateHandler(tornado.web.RequestHandler):\n  def render_template (self, tpl, context):\n    template = ENV.get_template(tpl)\n    self.write(template.render(**context))\n\nclass MainHandler(TemplateHandler):\n  def get (self):\n    # render input form\n    self.render_template(\"weather_form.html\", {})\n\n\n  def post (self):\n    # get city name\n    city = self.get_body_argument('city')\n\n    r= requests.get('http://api.openweathermap.org/data/2.5/weather?q={}&APPID=e4e42da8e12b99936239ca5d5c23ad25'.format(city))\n    data = json.loads(r.text)\n    print(data)\n\n    # lookup the weather\n\n    # render the weather data\n    self.render_template(\"weather_form.html\", {'data':data})\n\ndef make_app():\n  return tornado.web.Application([\n    (r\"/\", MainHandler),\n    (r\"/static/(.*)\",\n      tornado.web.StaticFileHandler, {'path': 'static'}),\n  ], autoreload=True)\n\nif __name__ == \"__main__\":\n  tornado.log.enable_pretty_logging()\n  app = make_app()\n  app.listen(int(os.environ.get('PORT', '8080')))\n  tornado.ioloop.IOLoop.current().start()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"362197710","text":"\"\"\"\nThis python script contains commonly used functions to help with parsing in the openelections project.\nOriginal Author: Tiffany Xiao\nEdited By: Karen Santamaria\nDate: 11/19/2019\n\"\"\"\n\nimport csv\n\n\ndef standardize_office_name(office_to_check):\n    ''' \n    Function to change office name into the standard office name as requested \n    https://github.com/openelections/docs/blob/master/standardization.md\n    \n    Sample Input: 'United States Representative District 8'\n    Sample Output: U.S. House\n    '''\n\n    formatted_office = None \n\n    not_accepted_offices = [\"city\"]\n    office_to_check = office_to_check.lower().strip()\n\n    lieutenant_governor_list = [\"lt.\",\"lieutenant\", \"lt governor\"]\n    governor_list = [\"governor\"]\n    attorney_general_list = [\"attorney general\"]\n    us_senate_list = [\"us senator\", \"united states senator\", \"us senate\"]\n    state_senate_list = [\"state senate\", \"state senator\"]\n    state_assembly_list = [\"state assembly\", \"member of the state assembly\", \"assembly\"]\n    secretary_of_state_list = [\"secretary of state\"]\n    controller_list = [\"controller\"]\n    state_treasurer_list = [\"treasurer of state\"]\n    insurance_commissionar_list = [\"insurance commissioner\"]\n    public_instruction_list = [\"public instruction\"]\n    us_house_list = [\"us house\",\n                        \"u.s. house\",\n                        \"united state house\",\n                        \"us rep\",\n                        \"u s rep\",\n                        \"u.s. rep\",\n                        \"united states rep\", \n                        \"representative in congress\"]\n    state_house_list = [\"representative in state legislature\",\n                         \"state representative\"]\n    auditor_of_state_list = ['auditor of state']\n\n\n    office_names_master = {\n        'Lieutenant Governor' : lieutenant_governor_list,\n        'Governor' : governor_list,\n        'Attorney General' : attorney_general_list,\n        'U.S. Senate' : us_senate_list,\n        'State Senate' : state_senate_list,\n        'State Assembly' : state_assembly_list,\n        'State Secretary' : secretary_of_state_list,\n        'Controller' : controller_list,\n        'State Treasurer' : state_treasurer_list,\n        'Insurance Commisioner' : insurance_commissionar_list,\n        'Public Instruction' : public_instruction_list,\n        'U.S. House' : us_house_list,\n        'State House' : state_house_list,\n        'State Auditor' : auditor_of_state_list\n    }\n\n    # for each office check if any are a match\n    for key in office_names_master:\n        if(is_this_office(office_names_master[key], key, office_to_check)):\n            formatted_office = key # if match change to desired office format\n            break\n\n    return formatted_office\n\n\n\ndef is_this_office(accepted_office_lst, office_name, office_to_check):\n    '''\n    Find if office_to_check is a match to given office_name\n    SAMPLE INPUT:\n        accepted_office_lst = [\"us senator\", \"united states senator\", \"us senate\"]\n        office_name = U.S. Senator\n        office_to_check = United States Senator\n    SAMPLE OUTPUT: True \n    '''\n    is_this_office_type = False\n\n    offices_with_us = ['U.S. Senate', 'U.S. House'] # offices that require US explicitly stated\n    offices_with_lt = ['Lieutenant Governor'] # offices that require Lt. explicitly stted\n\n    us_in_name = True if (office_name in offices_with_us) else False #check is US has to be in office name\n    lt_in_name = True if (office_name in offices_with_lt) else False #check if Lt. has to be in office name\n\n    us_names = ['u.s.', 'us', 'united state', 'congress'] # ways US is writted\n    lt_names = ['lt', 'lieutenant' ] # ways Lt. is written\n\n    for office in accepted_office_lst:\n        if (us_in_name): \n            if(office in office_to_check):\n                for us_name in us_names:\n                    if (us_name in office_to_check):\n                        is_this_office_type = True\n        elif (lt_in_name):\n            if(office in office_to_check):\n                for lt_name in lt_names:\n                    if (lt_name in office_to_check):\n                        is_this_office_type = True\n        else:\n            if (office in office_to_check):\n                is_this_office_type = True\n\n\n    return is_this_office_type","sub_path":"2018-general-parser/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"261153912","text":"from airflow import DAG\nimport pandas as pd\nimport numpy as np\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.operators.python_operator import PythonOperator\nfrom datetime import datetime, timedelta\nfrom sqlalchemy import create_engine\nimport logging\n\n\n# 爬取产生数据，存入数据库 >> 取出原始数据，清洗处理至指定格式 >> ��入正式数据库\n#\n# 也可能还需要一个校验的环节？\n# 产生数据，存入原始数据库\n#       >> 取出原始数据，处理至指定格式\n#               >> 校验对比     >> 存入正式数据库\n#       >> 取出聚源数据，处理至相同格式\n\n'''\n['index_quot_day', # 每日指数\n 'company_announcement_information_ori',\n 'company_announcement_information_TS',\n 'violation_handling',  # 违规处罚\n 'ownership_hierarchy', \n 'cominv_interactive',  # 投资者互动\n 'institution_shares',  # 机构持股\n 'financial_target_reportdata',\n 'hold_shares_total',\n 'equity_hold_company', \n 'indicator_change_description',  # 指标变动\n 'DuPont_analysis',  # 杜邦分析\n]\n'''\n\ndef raw_data(n_row):\n    # get some test dataset，后面也可以是爬虫产生的\n    df = pd.DataFrame(\n        index = range(1, n_row+1),\n        data = np.random.randint(0,111,size=(n_row, 4)),\n        columns=['aaa','bbb','ccc', 'dd'])\n    df['time'] = pd.period_range(start='2018-01-02', periods=n_row)\n    logging.info('data generated')\n    return df\n\ndef data_to_mysql(data, table_name, con=None):\n    # put data to mysql\n    if not con:\n        # logging.info('data_to_mysql need a mysql connection')\n        con = create_engine(\n            'mysql://rootb:3x870OV649AMSn*@' +\\\n            '14.152.49.155:8998/zyr?charset=gbk').connect()\n    df.to_sql(\n        table_name, con, if_exists='append', index=False,\n        chunksize=np.ceil(df.size/1000)*1000)\n    logging.info('data has saved to mysql')\n    return True\n\ndef gen_raw_data(n_rows):\n    data = raw_data(n_rows)\n    data_to_mysql(data, 'test_db_1')\n    return True\n\n\ndef compare_data(db, table, raw_db=None, raw_table=None):\n    # 对比拿到最后一个 id\n    last_of_raw = pd.read_sql_query(\n        f'SELECT * FROM {raw_table} ORDER BY id DESC LIMIT 1',\n        con=raw_db)['id'][0]\n\n    try:\n        last = pd.read_sql_query(\n            f'SELECT * FROM {table} ORDER BY id DESC LIMIT 10',\n            con=db)['id'][0]\n        if last > last_of_raw:\n            raise Exception(\"异常！数据库末尾id大于原始数据库末尾id\")\n    except ProgrammingError:\n        last = 0\n    logging.info('comparing data sets')\n    return (last, last_of_raw)\n\ndef process_method(data):\n    data['bbb'] = data['bbb']/20\n    return data\n\ndef process_raw_data(db, table, raw_db=None, raw_table=None, process=None):\n    logging.info('processing data')\n    if raw_table and (raw_db is None):\n        raw_db = db\n    elif raw_db and (raw_table is None):\n        raw_table = table\n    else:\n        raise Exception(\"请提供原始数据库和表名\")\n\n    last, last_of_raw = compare_data(db, table, raw_db, raw_table)\n    query = f'SELECT * FROM {raw_table} WHERE id>{last}'\n    data_chunks = pd.read_sql(query, raw_db, chunksize=2000)\n\n    for data in data_chunks:\n        new_data = process(data)\n        data_to_mysql(new_data, table, con=db)\n    logging.info('data processed')\n    return True\n\n\ndefault_args = {\n    \"owner\": \"airflow\",\n    \"depends_on_past\": False,\n    \"start_date\": datetime(2019, 1, 9),\n    \"email\": [\"airflow@airflow.com\"],\n    \"email_on_failure\": False,\n    \"email_on_retry\": False,\n    \"retries\": 2,\n    \"retry_delay\": timedelta(minutes=1),\n    # 'queue': 'bash_queue',\n    # 'pool': 'backfill',\n    # 'priority_weight': 10,\n    'end_date': datetime(2019, 1, 14),\n}\n\ndag = DAG(\n    \"sample_sql_process\",\n    default_args=default_args,\n    schedule_interval=timedelta(minutes=2)\n)\n\n# t1, t2 and t3 are examples of tasks created by instantiating operators\nt1 = PythonOperator(\n    task_id=\"gen_data\",\n    python_callable=gen_raw_data,\n    op_kwargs = {'n_rows': 30},\n    dag=dag)\n\ncon = create_engine(\n    'mysql://rootb:3x870OV649AMSn*@' +\\\n    '14.152.49.155:8998/zyr?charset=gbk').connect()\n\nt2 = PythonOperator(\n    task_id = 'process_data',\n    python_callable = process_raw_data,\n    op_kwargs = {'db':con, 'table':'test_db_2', 'raw_table':'test_db_1',\n                 'process':process_method},\n    dag=dag\n)\n\nt1 >> t2\n","sub_path":"dags/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":4401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"76034491","text":"import pytest\n\nfrom hrmcompiler.parser import IfOp, OutboxOp, LabelStmt, JumpOp, AssignOp\nfrom hrmcompiler.conversion import convert_ifnz_to_ifez\n\ndef test_convert_nz_to_ez():\n    start_ast = [IfOp(\"nz\", [OutboxOp()], [])]\n    expected_end_ast = [IfOp(\"ez\", [], [OutboxOp()])]\n\n    end_ast = convert_ifnz_to_ifez(start_ast)\n    assert end_ast == expected_end_ast\n\ndef test_convert_nz_dont_touch_ez():\n    start_ast = expected_end_ast = [IfOp(\"ez\", [OutboxOp()], [])]\n\n    end_ast = convert_ifnz_to_ifez(start_ast)\n    assert end_ast == expected_end_ast\n\ndef test_convert_nz_biggerprogram():\n    start_ast = [\n            LabelStmt(\"test\"),\n            AssignOp(src=\"inbox\", dst=\"emp\"),\n            IfOp(\"nz\", [OutboxOp()], [AssignOp(src=\"inbox\", dst=\"emp\")]),\n            JumpOp(\"test\")\n    ]\n\n    expected_end_ast = [\n            LabelStmt(\"test\"),\n            AssignOp(src=\"inbox\", dst=\"emp\"),\n            IfOp(\"ez\", [AssignOp(src=\"inbox\", dst=\"emp\")], [OutboxOp()]),\n            JumpOp(\"test\")\n    ]\n\n    end_ast = convert_ifnz_to_ifez(start_ast)\n    assert end_ast == expected_end_ast\n\ndef test_convert_nz_nestedif():\n    start_ast = [\n            IfOp(\"ez\",\n                [IfOp(\"nz\", [OutboxOp()], [JumpOp(\"test\")])],\n                [IfOp(\"ez\", [JumpOp(\"test\")], [OutboxOp()])]\n            )]\n    expected_end_ast = [\n            IfOp(\"ez\",\n                [IfOp(\"ez\", [JumpOp(\"test\")], [OutboxOp()])],\n                [IfOp(\"ez\", [JumpOp(\"test\")], [OutboxOp()])]\n            )]\n\n    end_ast = convert_ifnz_to_ifez(start_ast)\n    assert end_ast == expected_end_ast\n","sub_path":"tests/test_conversionphase_nz.py","file_name":"test_conversionphase_nz.py","file_ext":"py","file_size_in_byte":1573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"356793499","text":"#!/usr/bin/env python3\n\"\"\"\nThis file is for the enjoy of the AGENT_ONE,\nAuthor: FFAI_WD\nRights Reserved\n\"\"\"\nimport gym\nimport torch\nimport argparse\nimport numpy as np\nfrom tqdm import tqdm as tqdm\n\nfrom a2c_agent import RL_AGENT_A3C\nfrom grid_world import Grid_World\n\n\"\"\" check the device \"\"\"\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\nparser = argparse.ArgumentParser(description='init the par')\nparser.add_argument('-game_num', '--game_num', nargs='?', default=30000)\nparser.add_argument('-max_episode_num', '--max_episode_num', nargs='?', default=300)\nparser.add_argument('-device', '--device', nargs='?', default=device)\nparser.add_argument('-model_path', '--model_path', nargs='?', default='model/')\nparser.add_argument('-model_load', '--model_load', nargs='?', default=True)\nparser.add_argument('-old_model_name', '--o_model_name', nargs='?', \\\n        default='0706020520.pt')\nparser.add_argument('-r_memory_Fname', '--r_memory_Fname', nargs='?', \\\n        default='r_memory.pkl')\nparser.add_argument('-gamma', '--gamma', nargs='?', default=0.999)\nparser.add_argument('-learning_rate', '--lr', nargs='?', default=0.001)\nparser.add_argument('-game', '--game_name', nargs='?', default=\"FFAI-3-v1\")\nargs = parser.parse_args()\n\n\"\"\" switch the board size \"\"\"\nboard_size_dict = {'FFAI-3-v1': (7, 14)}\n\n\"\"\" Smaller variants \"\"\"\nstep_now = 0 # record the global steps\nreward_num = 0\nenv = Grid_World(7, 14) \nagent = RL_AGENT_A3C(args.lr,\n                        args.gamma,\n                        board_size_dict[args.game_name],\n                        args.device,\n                        args.model_path,\n                        args.r_memory_Fname,\n                        args.o_model_name,\n                        args.model_load)\n\n\"\"\" Set seed for reproducibility \"\"\"\nseed = 0\n\n\"\"\" Play 10 games \"\"\"\nfor i in range(args.game_num):\n        \"\"\" Reset environment \"\"\"\n        obs = env.reset()\n        episode_steps_cnt = 0\n        done = False\n\n        \"\"\" Take actions as long as game is not done \"\"\"\n        while(1):\n            episode_steps_cnt += 1\n            step_now += 1 # update the steps\n\n            \"\"\" interact with the env and render\"\"\"\n            action = agent.act_enjoy(obs)\n            obs_new, reward, done = env.step(action) # Gym step function\n\n            \"\"\" print the reward info \"\"\"\n            if reward > 0:\n                reward_num += 1\n                print('find food for', reward_num)\n                print('used time_step for ', episode_steps_cnt)\n\n            env.render()\n\n            \"\"\" if the game is done \"\"\"\n            obs = obs_new\n            \n            \"\"\" control the game \"\"\"\n            input_str = input()\n            if input_str == 'q': break\n            if done or episode_steps_cnt > args.max_episode_num: break\n","sub_path":"a2c_gridworld/enjoy.py","file_name":"enjoy.py","file_ext":"py","file_size_in_byte":2805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"470680204","text":"import unittest\nfrom rdflib import Graph, URIRef\nfrom etk.ontology_namespacemanager import OntologyNamespaceManager\nfrom etk.ontology_namespacemanager import PrefixNotFoundException, PrefixAlreadyUsedException\n\n\nclass TestOntologyNamespaceManager(unittest.TestCase):\n    def test_ontology_namespace_parse_uri(self):\n        nm = OntologyNamespaceManager(Graph())\n        uri = URIRef('http://dig.isi.edu/ontologies/dig/Event')\n        self.assertEqual(uri, nm.parse_uri(uri))\n        for case in ('http://dig.isi.edu/ontologies/dig/Event', 'dig:Event'):\n            self.assertEqual(uri, nm.parse_uri(case))\n        with self.assertRaises(PrefixNotFoundException):\n            nm.parse_uri(':Event')\n        nm.bind('', 'http://dig.isi.edu/ontologies/dig/')\n        self.assertEqual(uri, nm.parse_uri(':Event'))\n        self.assertEqual(uri, nm.parse_uri('Event'))\n\n    def test_ontology_namespace_bind(self):\n        correct_content = '@prefix : <http://w.org/> . @prefix schema: <http://schema.org/> .'\n        wrong_content = '@prefix schema: <http://dig.schema.org/> .'\n        nm = OntologyNamespaceManager(Graph())\n        namespace = {x[0] for x in nm.namespaces()}\n        self.assertNotIn('', namespace)\n        for name in ('owl', 'rdfs', 'rdf', 'schema', 'xsd', 'xml', 'dig'):\n            self.assertIn(name, namespace)\n        with self.assertRaises(PrefixAlreadyUsedException):\n            nm.graph.parse(data=wrong_content, format='ttl')\n        nm.graph.parse(data=correct_content, format='ttl')\n        namespace = {x[0] for x in nm.namespaces()}\n        self.assertIn('', namespace)\n","sub_path":"etk/unit_tests/test_ontology_namespacemanager.py","file_name":"test_ontology_namespacemanager.py","file_ext":"py","file_size_in_byte":1600,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"109750437","text":"from random import choice, randint\nfrom string import ascii_lowercase as az\n\n\nclass Caesar():\n    def __init__(self, key=3):\n        try:\n            self.key = int(key) % 26\n        except ValueError:\n            self.key = 3\n\n    def decode(self, text, flip_key=False):\n        key = self.key if not flip_key else self.key * -1\n        text = ''.join(a.lower() for a in text if a.isalpha())\n        return ''.join(az[(az.index(b) - key) % 26] for b in text)\n\n    def encode(self, text):\n        return self.decode(text, flip_key=True)\n\n\nclass Cipher():\n    def __init__(self, key=''):\n        if not key:\n            self.key = ''.join(choice(az) for _ in range(randint(105, 150)))\n        elif len(set(key)) == 1:\n            self.key = key[0]\n        else:\n            self.key = key\n        self.key_length = len(self.key)\n\n    def decode(self, decode_text, positive=True):\n        dex = 0\n        result = list()\n        for char in decode_text:\n            key_dex = az.index(self.key[dex])\n            key_dex = key_dex if positive else key_dex * -1\n            result.append(az[(az.index(char) - key_dex) % 26])\n            dex = (dex + 1) % self.key_length  # adjust key index\n        return ''.join(result)\n\n    def encode(self, encode_text):\n        return self.decode(encode_text, positive=False)\n","sub_path":"python/simple-cipher/cipher.py","file_name":"cipher.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"6244159","text":"def get_arguments(functions):\n    parenthesis = 0\n    functions = functions[4 if functions.startswith('AND') else 3:-1]\n    for i in range(len(functions)):\n        c = functions[i]\n        if c == '(':\n            parenthesis += 1\n        elif c == ')':\n            parenthesis -= 1\n        elif parenthesis == 0 and c == ',':\n            return (functions[:i], functions[i+1:])\n \ndef evaluar(expr):\n    if len(expr) == 1:\n        return expr\n    if expr.startswith('NOT'):\n        dentro = evaluar(expr[4:-1]) # Do quarto index ate o ultimo sem contar com o ultimo\n        if dentro == 'F':\n            return 'T'\n        else:\n            return 'F'\n    a1, a2 = get_arguments(expr)\n    a1 = evaluar(a1)\n    a2 = evaluar(a2)\n    if expr.startswith('AND'):\n        if a1 == 'T' and a2 == 'T':\n            return 'T'\n        else:\n            return 'F'\n    if expr.startswith('OR'):\n        if a1 == 'T' or a2 == 'T':\n            return 'T'\n        else:\n            return 'F'\n \nfor _ in range(int(input())):\n    print(evaluar(input().replace(' ', ''))) \n","sub_path":"NATALIAS.py","file_name":"NATALIAS.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"510156347","text":"import numpy as np\nimport pandas as pd\nimport KNNLearner as knn\nimport datetime as dt\nfrom marketsim import compute_portvals\nfrom util import get_portfolio_stats, get_data\nfrom analysis import assess_portfolio\nimport matplotlib.pyplot as plt\nimport os\nimport csv\n\n'''\nCompute upper and lower Bollinger Bands\n\nTODO: I will need to return a value for every element in the price\n      array. This is currently being done, but I can't use NaN\n\nReturns a value that is (usually) between -1.0 and 1.0\n'''\ndef compute_BB(prices, window_size = 20):\n    sma = moving_average(prices, window_size)\n        \n    # Calculate standard deviation of SMA\n    std_dev = standard_deviation(prices, window_size)\n        \n    bb_value = (prices - sma)/(2*std_dev)\n\n    return bb_value\n#end compute_BB\n\n''' Compute price momentum'''\ndef compute_momentum(prices, N=5):\n    momentum = np.zeros(prices.shape)\n    \n    for i in range(N, prices.shape[0]):\n        momentum[i] = (prices[i]/prices[i-N]) - 1\n    #end for\n    \n    # Fill data in backwards\n    momentum[0:N] = momentum[N]\n    \n    return momentum\n#end compute_momentum\n\n''' Compute volatility of price '''\ndef compute_volatility(prices, window_size=5):\n    return standard_deviation(prices, window_size)\n#end compute_volatility\n\n\n''' Computes the moving average of data over the specified window '''\ndef moving_average(data, window_size):\n    output = np.zeros((data.shape[0]))\n\n    for i in range(window_size-1, data.shape[0]):\n        output[i] = data[i-window_size+1:i+1].mean()\n    #end for\n    \n    # Fill data in backwards\n    output[0:window_size-1] = output[window_size]\n    return output\n#end moving_average\n\n''' Computes standard deviation of data over the specified window''' \ndef standard_deviation(data, window_size):\n    output = np.zeros( (data.shape[0]) )\n\n    for i in range(window_size-1, data.shape[0]):\n        output[i] = np.std( data[i-window_size+1:i+1], ddof=1 )\n    #end for\n    \n    # Fill data in backwards\n    output[0:window_size-1] = output[window_size]\n\n    return output\n#end standard_deviation\n\n\n'''\nComputes the return N days in the future \n\nprices is a row vector\nN is an integer for how many days in the future to look\n\n'''\ndef compute_Nday_return(prices, N): \n    prices_Nday = np.copy(prices)\n    \n    np.roll(prices_Nday, N)\n    prices_Nday = prices_Nday[N:]\n    \n    return ((prices_Nday/prices[:-N]) - 1.0)\n#end def\n\n''' Plots price, training, and predicted output '''\ndef plot_price_and_samples(price, Ytrain, Ysample):\n    \n    plot_handle = price.plot()\n    \n    dates = price.index[20:-5]\n\n    plot_handle.plot(dates, Ytrain, 'r', label=\"Training\" )\n    plot_handle.plot(dates, Ysample, 'g--', label=\"Sample\" )\n    \n    plt.legend()\n    plt.show()\n    \n# end def\n\n'''Plots data. Returns handle to plot'''\ndef plot_data(df, title=\"Stock prices\", xlabel=\"Date\", ylabel=\"Prices\"):\n    \n    ax = df.plot()\n    ax.set_xlabel(xlabel)\n    ax.set_ylabel(ylabel)\n    \n    return ax\n#end plot_data\n\n\n'''Adds vertical lines to specified plot'''\ndef add_orders_to_plot(plot_handle, orders, syms, min=60, max=130):\n    '''\n    Since I can only have one order outstanding at a time, I am going to assume\n    the incoming data is correct. That means that every other order should be\n    an exit. \n    '''\n    long = False\n    short = False\n    color = 'k' #black\n    for i in range(0,orders.shape[0]):\n        current_date = orders.index[i]\n        current_order = orders.iloc[i][syms[0]]\n\n        # Determine what position (short, long) we are in and set the color\n        # If we are in neither a long nor a short position\n        if (not long) and (not short):\n            # If a sell order comes in, we are short\n            if current_order < 0:\n                color = 'r' #red\n                short = True\n            # If a buy order comes in, we are long\n            elif current_order > 0:\n                color = 'g' #green\n                long = True\n            #endif\n        # Every other order will be an exit, so just set color to black\n        elif long or short:\n            color = 'k'\n            long = short = False\n        #end if\n\n        plot_handle.plot([current_date, current_date], [min, max], color) \n    #end for\n#end add_orders_to_plot\n\n\n'''\nRuns strategy using KNN learner\n'''\ndef process_KNN_strategy(syms, sd, ed, learner, percent_changed=0.01):\n    # Get the price data\n    prices_dates = pd.date_range(sd, ed)\n    prices_all = get_data(syms, prices_dates) # Includes SPY\n    prices_SPY = prices_all['SPY']\n    prices = prices_all[syms] \n    prices_orig = prices.copy()\n    \n    # Plot data frame\n    plot_handle = plot_data(prices)\n    \n    #****************************\n    # Generate orders\n    #****************************\n    pvals = prices.values\n    price_min = pvals.min()\n    price_max = pvals.max()\n    orders = prices.copy()\n    orders[syms] = np.NaN\n    \n    # parse data\n    long_active = False\n    short_active = False\n    entry_idx = 0\n    # Compute technical indicators iteratively\n    for i in range(20, pvals.shape[0]):\n        # Compute BB over last 20 days\n        avg_20days = pvals[i-20:i].mean()\n        std_dev_20days = np.std(pvals[i-20:i], ddof=1)\n        bb_val = (pvals[i]-avg_20days)/(2*std_dev_20days)\n        \n        # Compute momentum over the last 5 days\n        momentum_5day = (pvals[i]/pvals[i-5])-1\n        \n        # Compute volatility over the last 5 days\n        volatility_5day = np.std(pvals[i-5:i], ddof=1)\n        \n        # Use these indicators to query the learner\n        Xquery = np.array([ [bb_val[0], momentum_5day[0], volatility_5day] ])\n        Yquery = learner.query(Xquery)\n        \n        if not long_active and not short_active:\n            # If Y value (5 day future return) is greater than percent changed, go long\n            if Yquery >= percent_changed:\n                orders.iloc[i][syms[0]] = 100\n                long_active = True\n                entry_idx = i\n            # If Y value is less than percent changed, go short\n            elif Yquery <= -1*percent_changed:\n                orders.iloc[i][syms[0]] = -100\n                short_active = True\n                entry_idx = i\n        elif long_active:\n            # Sell 5 days later\n            if i == entry_idx + 5:\n                orders.iloc[i][syms[0]] = -100\n                long_active = False        \n        elif short_active:\n            # Buy 5 days later\n            if i == entry_idx + 5:\n                orders.iloc[i][syms[0]] = 100\n                short_active = False\n        #end if\n    # end for\n    \n    # Drop all the entries that don't have values\n    orders.dropna(inplace=True)\n    \n    # Save orders to CSV file\n    write_csv_file(orders, syms)\n    \n    # Add the orders to the plot\n    add_orders_to_plot(plot_handle, orders, syms, min=price_min-10, max=price_max+10)\n    \n    plt.show()\n\n#end def\n\ndef write_csv_file(orders, syms, filename='orders/Myorders.csv'):\n    with open(filename, 'wb') as csvfile:\n        orderswriter = csv.writer(csvfile, delimiter=',',\n                                  quotechar='|', quoting=csv.QUOTE_MINIMAL)\n\n        # Write header\n        orderswriter.writerow(['Date'] + ['Symbol'] + ['Order'] + ['Shares'])\n        \n        # For each order, write to file\n        for i in range(0,orders.shape[0]):\n            # Get the date\n            current_dt = str(orders.index[i])[0:10]\n            \n            # Get the order type\n            order_type = 'BUY'\n            if orders.iloc[i][syms[0]] <= 0:\n                order_type = 'SELL'\n\n            # This is all we need to know. The other data is constant\n            orderswriter.writerow([current_dt] + [syms[0]] + [order_type] + ['100'])            \n        #end for\n    #end with\n#end write_csv_file\n\n\nif __name__ == \"__main__\":\n    start_date = dt.datetime(2007, 12, 31)\n    end_date   = dt.datetime(2009, 12, 31)\n    \n    dates = pd.date_range(start_date, end_date)\n    \n    # Get price data\n    syms = [\"ML4T-220\"]\n    #syms = [\"IBM\"]\n    prices = get_data(syms, dates, True)\n    price_val = prices[syms].values\n    \n    # Get technical indicators\n    bb_val = compute_BB(price_val[:,0])\n    momentum = compute_momentum(price_val[:,0])\n    volatility = compute_volatility(price_val[:,0])\n    \n    # Combine to create training inputs\n    # Note, I may need to trim some data off here with\n    # how I filled in gaps. \n    Xtrain = np.array([ bb_val[20:], momentum[20:], volatility[20:] ])\n    #Xtrain = np.array([ bb_val, momentum, volatility ])\n    Xtrain = np.transpose(Xtrain)\n    Xtrain = Xtrain[:-5, :]\n    \n    # Get the training outputs\n    Ytrain = compute_Nday_return(price_val[:,0], 5)\n    Ytrain = Ytrain[20:]          \n    \n    # Train the KNN Learner\n    learner = knn.KNNLearner(k=3, verbose=False)\n    learner.addEvidence(Xtrain, Ytrain)\n    \n    # I would expect to see 3 sine waves.  \n    # One of them is the original price data.  \n    # Another is your Y training data, which should be shifted back 5 days.  \n    # The last one should be your predicted Y data which should lie almost on top of the training data.\n    # For the sine wave, I need to construct an extra plot\n    if (\"ML4T-220\" in syms):\n        # Add normalized price to data frame\n        prices[\"Normalized\"] = (price_val/330.0) - 1.0\n        #prices[\"Normalized\"] = price_val/price_val[0]\n        Ysample = learner.query(Xtrain)\n        # Plot\n        plot_price_and_samples(prices[\"Normalized\"], Ytrain, Ysample)\n    # end if\n    \n    # Test on in sample\n    process_KNN_strategy(syms, start_date, end_date, learner)\n    \n    compute_portvals(\"./orders/Myorders.csv\", start_val=10000)\n    \n    # Test on out of sample\n    \n    start_date = dt.datetime(2009, 12, 31)\n    end_date   = dt.datetime(2011, 12, 31)\n    \n    process_KNN_strategy(syms, start_date, end_date, learner)\n    compute_portvals(\"./orders/Myorders.csv\", start_val=10000, sd=start_date, ed=end_date)\n    \n","sub_path":"ML4Trading/ml4t/mc3_p2/KNNTrader.py","file_name":"KNNTrader.py","file_ext":"py","file_size_in_byte":9908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"395353380","text":"from django.db import models\n\n# Create your models here.\n\nclass Community(models.Model):\n\t\"\"\"コミュニティを表すモデル\n\t\"\"\"\n\tcode = models.CharField(max_length=128, unique=True)\n\t\"\"\"str: コミュニティ ID ( id としたいところだが Django ではややこしすぎる )\"\"\"\n\tname = models.CharField(max_length=128)\n\t\"\"\"str: コミュニティ名\"\"\"\n\tclass Meta:\n\t\tverbose_name_plural = 'communities'\n\n\tdef __str__(self):\n\t\treturn self.name\n\n\t@classmethod\n\tdef get(cls, code):\n\t\t\"\"\"コミュニティ ID で取得する\n\n\t\tArgs:\n\t\t\tcode (str): コミュニティ ID\n\n\t\tReturns:\n\t\t\tCommunity: コミュニティ\n\n\t\tRaises:\n\t\t\tCommunity.DoesNotExist: 存在しな��場合に発生\n\t\t\"\"\"\n\t\treturn cls.objects.get(code=code)\n\nclass Cluster(models.Model):\n\t\"\"\"クラスタを表すモデル\n\t\"\"\"\n\tcommunity = models.ForeignKey(Community, on_delete=models.PROTECT)\n\t\"\"\"Community: コミュニティ\"\"\"\n\tcode = models.CharField(max_length=128)\n\t\"\"\"str: クラスタ ID ( id としたいところだが Django ではややこしすぎる )\"\"\"\n\tname = models.CharField(max_length=128)\n\t\"\"\"str: クラスタ名\"\"\"\n\tclass Meta:\n\t\tunique_together = ['community', 'code']\n\n\tdef __str__(self):\n\t\treturn '{} > {}'.format(self.community.name, self.name)\n\n\t@classmethod\n\tdef get(cls, community_code, code):\n\t\t\"\"\"コミュニティ ID とクラスタ ID で取得する\n\n\t\tArgs:\n\t\t\tcommunity_code (str): コミュニティ ID\n\t\t\tcode (str): クラスタ ID\n\n\t\tReturns:\n\t\t\tCluster: クラスタ\n\n\t\tRaises:\n\t\t\tCluster.DoesNotExist: 存在しない場合に発生\n\t\t\"\"\"\n\t\treturn cls.objects.get(community__code=community_code, code=code)\n\nclass Unit(models.Model):\n\t\"\"\"ユニットを表すモデル\n\t\"\"\"\n\tcluster = models.ForeignKey(Cluster, on_delete=models.PROTECT)\n\t\"\"\"Cluster: クラスタ\"\"\"\n\tcode = models.CharField(max_length=128)\n\t\"\"\"str: ユニット ID ( id としたいところだが Django ではややこしすぎる )\"\"\"\n\tname = models.CharField(max_length=128)\n\t\"\"\"str: ユニット名\"\"\"\n\tavailable_from = models.DateTimeField(blank=True, null=True)\n\t\"\"\"datetime: 有効期間 ( 開始 )\"\"\"\n\tavailable_to = models.DateTimeField(blank=True, null=True)\n\t\"\"\"datetime: 有効期間 ( 終了 )\"\"\"\n\tusers = models.ManyToManyField('core.User', blank=True)\n\t\"\"\"list of core.User: 居住者 ( 多対多 )\"\"\"\n\tclass Meta:\n\t\tunique_together = ['cluster', 'code']\n\n\tdef __str__(self):\n\t\treturn '{} > {} > {}'.format(self.cluster.community.name, self.cluster.name, self.name)\n\n\t@classmethod\n\tdef get(cls, community_code, cluster_code, code):\n\t\t\"\"\"コミュニティ ID とクラスタ ID とユニット ID で取得する\n\n\t\tArgs:\n\t\t\tcommunity_code (str): コミュニティ ID\n\t\t\tcluster_code (str): クラスタ ID\n\t\t\tcode (str): ユニット ID\n\n\t\tReturns:\n\t\t\tUnit: ユニット\n\n\t\tRaises:\n\t\t\tUnit.DoesNotExist: 存在しない場合に発生\n\t\t\"\"\"\n\t\treturn cls.objects.get(cluster__community__code=community_code, cluster__code=cluster_code, code=code)\n\n####\n\ndef outline(asof=None):\n\t\"\"\"指定した日時でのコミュニティ/クラスタ/ユニット階層構造を取得する\n\n\tArgs:\n\t\tasof (datetime, optional): 指定すれば有効期間外のユニットを含めない. Defaults to None.\n\n\tReturns:\n\t\tdict: コミュニティ/クラスタ/ユニットの階層構造\n\t\"\"\"\n\tcommunities_dict = dict()\n\tcommunity_cur = Community.objects.all().order_by('code')\n\tfor community in community_cur:\n\t\tclusters_dict = dict()\n\t\tcluster_cur = community.cluster_set.order_by('code')\n\t\tfor cluster in cluster_cur:\n\t\t\tunits_dict = dict()\n\t\t\tunit_cur = cluster.unit_set.order_by('code')\n\t\t\tfor unit in unit_cur:\n\t\t\t\tif asof:\n\t\t\t\t\tif unit.available_from and asof < unit.available_from: continue\n\t\t\t\t\tif unit.available_to and unit.available_to <= asof: continue\n\t\t\t\tunits_dict[unit.code] = {\n\t\t\t\t\t'communityId': community.code,\n\t\t\t\t\t'clusterId': cluster.code,\n\t\t\t\t\t'id': unit.code,\n\t\t\t\t\t'name': unit.name,\n\t\t\t\t\t'available_from': unit.available_from,\n\t\t\t\t\t'available_to': unit.available_to,\n\t\t\t\t}\n\t\t\tclusters_dict[cluster.code] = {\n\t\t\t\t'communityId': community.code,\n\t\t\t\t'id': cluster.code,\n\t\t\t\t'name': cluster.name,\n\t\t\t\t'units': units_dict,\n\t\t\t}\n\t\tcommunities_dict[community.code] = {\n\t\t\t'id': community.code,\n\t\t\t'name': community.name,\n\t\t\t'clusters': clusters_dict,\n\t\t}\n\toutline_dict = {\n\t\t'communities': communities_dict,\n\t}\n\treturn outline_dict\n\ndef resident_outline(user):\n\t\"\"\"指定したユーザから見えるコミュニティ/クラスタ/ユニットの階層構造を取得する\n\n\tArgs:\n\t\tuser (core.User): ユーザ\n\n\tReturns:\n\t\tdict: コミュニティ/クラスタ/ユニットの階層構造\n\t\"\"\"\n\tcommunities_dict = dict()\n\tfor unit in user.unit_set.all():\n\t\tcluster = unit.cluster\n\t\tcommunity = cluster.community\n\t\tcommunity_dict = communities_dict.get(community.code)\n\t\tif not community_dict:\n\t\t\tcommunity_dict = {\n\t\t\t\t'id': community.code,\n\t\t\t\t'name': community.name,\n\t\t\t\t'clusters': dict(),\n\t\t\t}\n\t\t\tcommunities_dict[community.code] = community_dict\n\t\tcluster_dict = community_dict['clusters'].get(cluster.code)\n\t\tif not cluster_dict:\n\t\t\tcluster_dict = {\n\t\t\t\t'communityId': community.code,\n\t\t\t\t'id': cluster.code,\n\t\t\t\t'name': cluster.name,\n\t\t\t\t'units': dict(),\n\t\t\t}\n\t\t\tcommunity_dict['clusters'][cluster.code] = cluster_dict\n\t\tcluster_dict['units'][unit.code] = {\n\t\t\t'communityId': community.code,\n\t\t\t'clusterId': cluster.code,\n\t\t\t'id': unit.code,\n\t\t\t'name': unit.name,\n\t\t\t'available_from': unit.available_from,\n\t\t\t'available_to': unit.available_to,\n\t\t}\n\toutline_dict = {\n\t\t'communities': communities_dict,\n\t}\n\treturn outline_dict\n","sub_path":"community/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"114057305","text":"import difflib\nimport logging\nimport re\nimport socket\nimport unicodedata\nfrom datetime import datetime\nfrom typing import Tuple, Union\n\nimport discord\nimport requests\nfrom discord import Colour, Embed, utils\nfrom discord.ext import commands\nfrom discord.ext.commands import (\n    BadArgument,\n    BucketType,\n    Cog,\n    Context,\n    clean_content,\n    command,\n    cooldown,\n    has_any_role,\n)\nfrom discord.utils import snowflake_time\n\nfrom utils.messages import send_denial\nfrom utils.paginator import LinePaginator\n\nDESCRIPTIONS = (\"Command processing time\", \"Discord API latency\")\nROUND_LATENCY = 3\n\nimport aiohttp\nfrom discord import AsyncWebhookAdapter, Webhook\n\nZEN_OF_PYTHON = \"\"\"\\\nBeautiful is better than ugly.\nExplicit is better than implicit.\nSimple is better than complex.\nComplex is better than complicated.\nFlat is better than nested.\nSparse is better than dense.\nReadability counts.\nSpecial cases aren't special enough to break the rules.\nAlthough practicality beats purity.\nErrors should never pass silently.\nUnless explicitly silenced.\nIn the face of ambiguity, refuse the temptation to guess.\nThere should be one-- and preferably only one --obvious way to do it.\nAlthough that way may not be obvious at first unless you're Dutch.\nNow is better than never.\nAlthough never is often better than *right* now.\nIf the implementation is hard to explain, it's a bad idea.\nIf the implementation is easy to explain, it may be a good idea.\nNamespaces are one honking great idea -- let's do more of those!\n\"\"\"\n\n\nclass Info(commands.Cog):\n    \"\"\" Commands related to information \"\"\"\n\n    def __init__(self, bot) -> None:\n        self.bot = bot\n\n    @commands.Cog.listener()\n    async def on_ready(self):\n        print(\"Info Cog Loaded Succesfully\")\n\n    @commands.command()\n    async def bot(sef, ctx):\n        \"\"\"\n        Shows info about bot\n        \"\"\"\n        e = discord.Embed(color=0x00FFCC)\n        e.set_author(name=\"Bot statistics\", icon_url=ctx.author.avatar_url)\n        e.add_field()\n\n    @commands.command()\n    async def ping(self, ctx: commands.Context) -> None:\n        \"\"\"\n        Gets different measures of latency within the bot.\n        Returns bot, Discord Protocol latency.\n        \"\"\"\n        # datetime.datetime objects do not have the \"milliseconds\" attribute.\n        # It must be converted to seconds before converting to milliseconds.\n        bot_ping = (datetime.utcnow() - ctx.message.created_at).total_seconds() * 1000\n        bot_ping = f\"{bot_ping:.{ROUND_LATENCY}f} ms\"\n\n        # Discord Protocol latency return value is in seconds, must be multiplied by 1000 to get milliseconds.\n        discord_ping = f\"{self.bot.latency * 1000:.{ROUND_LATENCY}f} ms\"\n\n        embed = Embed(title=\"Pong!\", color=0x00FFCC)\n\n        for desc, latency in zip(DESCRIPTIONS, [bot_ping, discord_ping]):\n            embed.add_field(name=desc, value=latency, inline=False)\n\n        await ctx.reply(embed=embed)\n\n    @commands.command()\n    async def charinfo(self, ctx, *, characters):\n        \"\"\"Shows you information on up to 50 unicode characters.\"\"\"\n        match = re.match(r\"<(a?):(\\w+):(\\d+)>\", characters)\n        if match:\n            return await send_denial(\n                ctx,\n                \"**Non-Character Detected**\\n\"\n                \"Only unicode characters can be processed, but a custom Discord emoji \"\n                \"was found. Please remove it and try again.\",\n            )\n\n        if len(characters) > 50:\n            return await send_denial(ctx, f\"Too many characters ({len(characters)}/50)\")\n\n        def get_info(char: str) -> Tuple[str, str]:\n            digit = f\"{ord(char):x}\"\n            if len(digit) <= 4:\n                u_code = f\"\\\\u{digit:>04}\"\n            else:\n                u_code = f\"\\\\U{digit:>08}\"\n            url = f\"https://www.compart.com/en/unicode/U+{digit:>04}\"\n            name = f\"[{unicodedata.name(char, '')}]({url})\"\n            info = f\"`{u_code.ljust(10)}`: {name} - {utils.escape_markdown(char)}\"\n            return info, u_code\n\n        char_list, raw_list = zip(*(get_info(c) for c in characters))\n        embed = Embed().set_author(name=\"Character Info\")\n\n        if len(characters) > 1:\n            # Maximum length possible is 502 out of 1024, so there's no need to truncate.\n            embed.add_field(\n                name=\"Full Raw Text\", value=f\"`{''.join(raw_list)}`\", inline=False\n            )\n\n        await LinePaginator.paginate(\n            char_list, ctx, embed, max_lines=10, max_size=2000, empty=False\n        )\n\n    @commands.command(hidden=True)\n    async def codeblock(self, ctx):\n        embed = discord.Embed(title=\"CodeBlocks\", color=0xFF0000)\n        embed.set_image(url=\"https://i.imgur.com/va1dpTv.png\")\n        embed.set_footer(text=\"**These are backticks**, not quotes.\")\n        await ctx.reply(embed=embed)\n\n    @commands.command()\n    async def zen(\n        self, ctx: Context, *, search_value: Union[int, str, None] = None\n    ) -> None:\n        \"\"\"\n        Show the Zen of Python.\n        Without any arguments, the full Zen will be produced.\n        If an integer is provided, the line with that index will be produced.\n        If a string is provided, the line which matches best will be produced.\n        \"\"\"\n        embed = Embed(\n            colour=Colour.blurple(),\n            title=\"The Zen of Python\",\n            description=ZEN_OF_PYTHON,\n        )\n\n        if search_value is None:\n            embed.title += \", by Tim Peters\"\n            await ctx.send(embed=embed)\n            return\n\n        zen_lines = ZEN_OF_PYTHON.splitlines()\n\n        # handle if it's an index int\n        if isinstance(search_value, int):\n            upper_bound = len(zen_lines) - 1\n            lower_bound = -1 * upper_bound\n            if not (lower_bound <= search_value <= upper_bound):\n                raise BadArgument(\n                    f\"Please provide an index between {lower_bound} and {upper_bound}.\"\n                )\n\n            embed.title += f\" (line {search_value % len(zen_lines)}):\"\n            embed.description = zen_lines[search_value]\n            await ctx.send(embed=embed)\n            return\n\n        # Try to handle first exact word due difflib.SequenceMatched may use some other similar word instead\n        # exact word.\n        for i, line in enumerate(zen_lines):\n            for word in line.split():\n                if word.lower() == search_value.lower():\n                    embed.title += f\" (line {i}):\"\n                    embed.description = line\n                    await ctx.send(embed=embed)\n                    return\n\n        # handle if it's a search string and not exact word\n        matcher = difflib.SequenceMatcher(None, search_value.lower())\n\n        best_match = \"\"\n        match_index = 0\n        best_ratio = 0\n\n        for index, line in enumerate(zen_lines):\n            matcher.set_seq2(line.lower())\n\n            # the match ratio needs to be adjusted because, naturally,\n            # longer lines will have worse ratios than shorter lines when\n            # fuzzy searching for keywords. this seems to work okay.\n            adjusted_ratio = (len(line) - 5) ** 0.5 * matcher.ratio()\n\n            if adjusted_ratio > best_ratio:\n                best_ratio = adjusted_ratio\n                best_match = line\n                match_index = index\n\n        if not best_match:\n            raise BadArgument(\n                \"I didn't get a match! Please try again with a different search term.\"\n            )\n\n        embed.title += f\" (line {match_index}):\"\n        embed.description = best_match\n        await ctx.send(embed=embed)\n\n\ndef setup(bot) -> None:\n    \"\"\"Load the Latency cog.\"\"\"\n    bot.add_cog(Info(bot))\n","sub_path":"cogs/info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":7685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"31857914","text":"import cv2\nimport numpy as np\nfrom http.server import BaseHTTPRequestHandler, HTTPServer\nfrom socketserver import ThreadingMixIn\n\nimport requests\nimport time\nfrom emoji.unicode_codes import UNICODE_EMOJI\nimport emoji\nfrom PIL import Image, ImageFont, ImageDraw\n\nfrom avatar_class import AvatarProcessor\n\nimport main_translate\nfrom main_translate import translate\n\nfrom threading import Thread\n\ncapture = None\n\nurlEmotions = \"http://ds.ikorsakov.com:60001\"\n\nclass CamHandler(BaseHTTPRequestHandler):\n\n    def do_GET(self):\n        if self.path.endswith('.mjpg'):\n            self.send_response(200)\n            self.send_header(\n                'Content-type',\n                'multipart/x-mixed-replace; boundary=--jpgboundary'\n            )\n            self.end_headers()\n            counter = time.perf_counter()\n            emoji = \"\"\n            prev = counter\n\n            avatar = AvatarProcessor(\"frozen_model.pb\")\n            prevTags = \"\"\n            while True:\n                try:\n\n                    rc, img = capture.read()\n\n                    if not rc:\n                        continue\n\n\n                    counter = time.perf_counter()\n                    if ((counter - prev) > 5):\n                        emoji = self.getEmotions(img)\n                        prev = counter\n\n\n\n\n                    #cv2.putText(img, \"TES1111111111111111T\", (100, 500), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255),\n                    #            2, cv2.LINE_AA)\n                    #img_str = cv2.imencode('.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, 75])[1].tostring()\n                    cv2.putText(img, prevTags, (40, 100), cv2.FONT_HERSHEY_SIMPLEX, 1,\n                                (255, 255, 255), 2, cv2.LINE_AA)\n\n                    img = avatar.process_frame(img)\n                    # draw Emoji\n                    img = self.drawEmoji(img, emoji)\n\n                    if (main_translate.globalTags != prevTags):\n                        print(main_translate.globalTags)\n                        print(' '.join([str(elem) for elem in main_translate.globalTags]))\n\n                        prevTags = main_translate.globalTags\n\n                    img = cv2.imencode('.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, 75])[1]\n                    img_str = img.tostring()\n\n                    self.send_header('Content-type', 'image/jpeg')\n                    self.send_header('Content-length', len(img_str))\n                    self.end_headers()\n\n                    self.wfile.write(img_str)\n                    self.wfile.write(b\"\\r\\n--jpgboundary\\r\\n\")\n\n\n                except KeyboardInterrupt:\n                    self.wfile.write(b\"\\r\\n--jpgboundary--\\r\\n\")\n                    break\n                except BrokenPipeError:\n                    continue\n            return\n\n        if self.path.endswith('.html'):\n            self.send_response(200)\n            self.send_header('Content-type', 'text/html')\n            self.end_headers()\n            self.wfile.write(b'<html><head></head><body>')\n            self.wfile.write(b'<img src=\"http://127.0.0.1:8081/cam.mjpg\"/>')\n            self.wfile.write(b'</body></html>')\n            return\n\n    def drawEmoji(self, img, stringEmoji):\n        if (stringEmoji == \"\"):\n            return img\n        im_p = Image.fromarray(img)\n        # Get a drawing context\n        draw = ImageDraw.Draw(im_p)\n        font = ImageFont.truetype(\"c:\\\\Windows\\\\Fonts\\\\seguiemj.ttf\", 72)\n        tick = str(emoji.emojize(UNICODE_EMOJI[stringEmoji]))\n        draw.text((5, 45), tick, (0, 192, 192), font=font)\n\n        # Convert back to OpenCV image and save\n        return np.array(im_p)\n\n    def getEmotions(self, img):\n        resultString = \"\"\n        # send req to emotionServ\n        reqEmotionsResponse = requests.post(urlEmotions, files={\n            'frame': cv2.imencode('.jpg', img, [cv2.IMWRITE_JPEG_QUALITY, 75])[1].tostring()})\n        dict = reqEmotionsResponse.json()[0][\"emotions\"]\n        emotion = sorted(dict, key=dict.get)[-1]\n        if (emotion == \"angry\"):\n            resultString = u'\\U0001f620'\n        elif (emotion == \"sad\"):\n            resultString = u'\\U0001f622'\n        elif (emotion == \"happy\"):\n            resultString = u'\\U0001f604'\n        elif (emotion == \"disgust\"):\n            resultString = u'\\U0001f62C'\n        elif (emotion == \"neutral\"):\n            resultString = u'\\U0001f610'\n        elif (emotion == \"fear\"):\n            resultString = u'\\U0001f628'\n        elif (emotion == \"surprise\"):\n            resultString = u'\\U0001f626'\n        return resultString\n\n\nclass ThreadedHTTPServer(ThreadingMixIn, HTTPServer):\n    \"\"\"Handle requests in a separate thread.\"\"\"\n\ndef camera():\n    global capture\n    capture = cv2.VideoCapture(0)\n    capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\n    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)\n    # thread.join()\n    global img\n    try:\n        server = ThreadedHTTPServer(('localhost', 9000), CamHandler)\n        print(\"server started at http://127.0.0.1:8081/cam.html\")\n        server.serve_forever()\n    except KeyboardInterrupt:\n        capture.release()\n        server.socket.close()\n\ndef main():\n\n    t1 = Thread(target=main_translate.translate, args=[])\n    t1.start()\n    camera()\n    t1.join()\n\n    #t1 = Thread(target=camera(), args=(1,), daemon=True)\n    #t1.start()\n\n    print(\"te\")\n    t2 = Thread(target=translate())\n    t2.start()\n\n\n    #thread1.start()\n'''\n    global capture\n    capture = cv2.VideoCapture(0)\n    capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1280)\n    capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 720)\n    thread = Thread(target=main_translate.main())\n    thread.start()\n    thread.\n    #thread.join()\n    global img\n    try:\n        server = ThreadedHTTPServer(('localhost', 9000), CamHandler)\n        print(\"server started at http://127.0.0.1:8081/cam.html\")\n        server.serve_forever()\n    except KeyboardInterrupt:\n        capture.release()\n        server.socket.close()\n'''\n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"matching + translate/serv.py","file_name":"serv.py","file_ext":"py","file_size_in_byte":5964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"467268438","text":"import shlex, subprocess,sys, math, argparse, time\nfrom sympy import *\n\n#p = subprocess.Popen(\"test.exe \\\"3*x^3+sin(x)-12\\\" \",shell=True)\n#x = \"3*x^3+sin(x)-12\"\n\ndef BisFunc():\n\t#cc = subprocess.Popen(\"cls\",shell=True)\n\tprint(\"\\n\\nMetodos Numericos\\n\")\n\tprint(\"Metodo de la bisección: \\n\")\n\tx = input(\"Introduce la ecuación a calcular: \\n \")\n\tprint(\"\\n Gráficar:\\n\")\n\ta = input(\"Introduce el limite superior: \\n\")\n\tb = input(\"Introduce el limite inferior: \\n\")\n\tp = subprocess.Popen(\"bisec.exe \\\"\"+ x +\"\\\"\",shell=True)\n\ndef PuntoFijoFunc():\n\tprint(\"Metodos Numericos\\n\")\n\tprint(\"Metodo del punto Fijo:\\n\")\n\tx = input(\"Introduce la ecuación a calcular: \\n \")\n\tp = subprocess.Popen(\"PuntoFijo.exe \\\"\"+ x +\"\\\"\",shell=True)\n\ndef FalsaPFunc():\n\tprint(\"Metodos Numericos\\n\")\n\tprint(\"Falsa Posicion: \\n\")\n\tx = input(\"Introduce la ecuación a calcular: \\n \")\n\tp = subprocess.Popen(\"FalsaP.exe \\\"\"+ x +\"\\\"\",shell=True)\n\ndef SecanteFunc():\n\tprint(\"\\n\\nMetodos de la Secante\\n\")\n\tx = input(\"Introduce la ecuación a calcular: \\n \")\n\t#print(\"\\n Gráficar:\\n\")\n\t#a = input(\"Introduce el limite superior: \\n\")\n\t#b = input(\"Introduce el limite inferior: \\n\")\n\tp = subprocess.Popen(\"Secante.exe \\\"\"+ x +\"\\\"\",shell=True)\n\ndef NRapson():\n\tx = input(\"Introduce la ecuación a calcular: \\n \")\n\tpres =input(\"Introduce la precisión: \\n\")\n\tstar = input(\"Introduce el punto de partida: \\n\")\n\t#x*log(x)-1.2\n\t#eq = \"python .\\Rapson.py -f \\\"x*log(x)-1.2\\\" -s \" +star+ \" -p \" +pres\n\teq = \"python .\\Rapson.py -f \\\"\"+x+\"\\\" -s \" +star+ \" -p \" +pres\n\tp = subprocess.Popen(eq,shell=False)\t\n\n\treturn\n\nprint(\"\\n Metodos numericos:  \\n\\n\")\nprint(\"1) Biseccion. \\n\")\nprint(\"2) Punto Fijo. \\n\")\nprint(\"3) Falsa posicion. \\n\")\nprint(\"4) Metodo Secante. \\n\")\nprint(\"5) Newton-Rapson.\\n\")\nprint(\"0) Salir.\\n\")\n\nans = int(input(\"Selecciona una opcion: \\n\"))\n\n\nif ans==1:\n\tBisFunc()\n\nelif ans==2:\n\tPuntoFijoFunc()\n\nelif ans==3:\n\tFalsaPFunc()\n\nelif ans==4:\n\tSecanteFunc()\n\nelif ans==5:\n\tNRapson()\n\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"230982618","text":"#11 #12\nwiek = 24\nif type(wiek) != int:\n    raise TypeError('Wiek powinien być liczbą całkowitą!')\nif wiek>120 or wiek <0:\n    raise ValueError(\"Niemożliwe!\")\nprint(f'Masz {wiek} lat')\n\n#13\ncena_netto = 16.2\nif (type(cena_netto) != float) and (type(cena_netto) !=int):\n    raise TypeError('Cena powinna być liczbą!')\nif cena_netto<0:\n    raise ValueError(\"Cena nie może być mniejsza od 0!\")\ncena_brutto=cena_netto*1.23\nprint(round(cena_brutto, 2), \"zł\")\n\n#14\ndef pole_prostokata():\n    try:\n        a=float(input(\"Wprowadź dł. pierwszego boku: \"))\n        b=float(input(\"Wprowadź dł. drugiego boku: \"))\n    except:\n        raise TypeError(\"Długości boków muszą być liczbami!\")\n        \n    if a<0 or b<0:\n        raise ValueError(\"Boki nie mogą mieć długość mniejszej od 0!\")\n    pole = a * b\n    return pole\nprint(pole_prostokata())","sub_path":"09-DefensiveProgramming/Zajecia9c.py","file_name":"Zajecia9c.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"210531435","text":"from django.contrib import admin\nfrom django.urls import path, include\nfrom .views import *\n\nurlpatterns = [\n    path('Index', Index, name=\"Index_page\"),\n    path('display_Laptops', display_Laptops, name=\"display_Laptops\"),\n    path('display_Desktops', display_Desktops, name=\"display_Desktops\"),\n    path('display_Mobiles', display_Mobiles, name=\"display_Mobiles\"),\n    path('add_laptop', add_laptop, name=\"add_laptop\"),\n    path('add_Desktop', add_Desktop, name=\"add_Desktop\"),\n    path('add_Mobile', add_Mobile, name=\"add_Mobile\"),\n    path('edit_Laptop/<pk>', edit_Laptop, name=\"edit_Laptop\"),\n    path('edit_Desktop/<pk>', edit_Desktop, name=\"edit_Desktop\"),\n    path('edit_Mobile/<pk>', edit_Mobile, name=\"edit_Mobile\"),\n    path('delete_Laptop/<pk>', delete_Laptop, name=\"delete_Laptop\"),\n    path('delete_Desktop/<pk>', delete_Desktop, name=\"delete_Desktop\"),\n    path('delete_Mobile/<pk>', delete_Mobile, name=\"delete_Mobile\"),\n\n]\n","sub_path":"Inventory/The_Inventory/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"541646056","text":"from apscheduler.schedulers.blocking import BlockingScheduler\nfrom apscheduler.triggers.cron import CronTrigger\n\nfrom log import save_log, init_log\nfrom other.visitors.mogu_visitor import MoGuRequest\nfrom utils.time_util import now_datetime\n\nscheduler = BlockingScheduler()\n\n\ndef job_brush_flow():\n    '''\n    访问博客\n    采集频率：1分整倍数，就执行这个函数\n    '''\n    init_log(_log_path='', _log_name='brush_flow.log', _filemode='a')\n    save_log(\"_________brush start_________\", now_datetime())\n    mogu = MoGuRequest()\n    mogu.start(_type='mogu')\n    save_log(\"_________brush end_________\\n\", now_datetime())\n\n\ndef start_scheduler():\n    trigger = CronTrigger(day_of_week='mon-sun', hour='9-18', minute='45')\n    scheduler.add_job(job_brush_flow, trigger)  # 根据需要进行设置\n    scheduler.start()\n\n\nif __name__ == '__main__':\n    print('任务启动')\n    start_scheduler()\n\n# pyinstaller -F  *.py\n# pyinstaller -F appium_demo/other/scheduler/go_brush_flow.py\n# .\\go_brush_flow.exe\n","sub_path":"appium_demo/other/scheduler/go_brush_flow.py","file_name":"go_brush_flow.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"569201022","text":"import sys\nsys.path.append(\"/usr/src/app/lib\")\n# Imports some required libraries\nimport RPi.GPIO as GPIO\nimport MFRC522\nimport signal\nimport regex\n\ncontinue_reading = True\n\n# Capture SIGINT for cleanup when the script is aborted\ndef end_read(signal,frame):\n    global continue_reading\n    print(\"Ctrl+C captured, ending read.\")\n    continue_reading = False\n    GPIO.cleanup()\n\n# Validate that PIN consist of 4 digit's\ndef validate_pin(pin):\n    if pin == \"\" or regex.fullmatch(\"\\d{4}\", pin):\n        return True\n    else:\n        return False\n\n# Print Welcome message\nprint(\"\\nWelcome to the IOTA debit card PIN tutorial\")\n\n# Ask for old PIN code\nold_pin = input(\"\\nWrite old 4 digit PIN code here or press Enter for default PIN: \")\nif validate_pin(old_pin) == False:\n        print(\"Invalid old PIN syntax\")\n        exit()\n\n# Ask for new PIN code\nnew_pin = input(\"\\nWrite new 4 digit PIN code here or press Enter for default PIN: \")\nif validate_pin(new_pin) == False:\n        print(\"Invalid new PIN syntax\")\n        exit()\n        \n        \n# Create a 16 element list inkl. new PIN to be written to autorization block\ndef make_pin(pin):\n    if pin == \"\":\n        pin_data = [255, 255, 255, 255, 255, 255, 255, 7, 128, 105, 255, 255, 255, 255, 255, 255]\n    else:\n        pin_data=[]\n        pin_letter_list = list(pin)\n        for letter in pin_letter_list:\n            pin_data.append(ord(letter))\n        pin_data = pin_data + [255, 255, 255, 7, 128, 105, 255, 255, 255, 255, 255, 255]\n\n    return pin_data\n\n# Hook the SIGINT\nsignal.signal(signal.SIGINT, end_read)\n\n# Create an object of the class MFRC522\nMIFAREReader = MFRC522.MFRC522()\n\n# Display scan IOTA debit card message\nprint(\"\\nHold IOTA debit card close to reader...\")\nprint(\"Press Ctrl+C to exit...\")\n\n# This loop keeps checking for near by RFID tag. If one is near it will get the UID and authenticate\nwhile continue_reading:\n    \n    # Scan for cards    \n    (status,TagType) = MIFAREReader.MFRC522_Request(MIFAREReader.PICC_REQIDL)\n\n    # If a card is found\n    if status == MIFAREReader.MI_OK:\n        print(\"Card detected\")\n    \n    # Get the UID of the card\n    (status,uid) = MIFAREReader.MFRC522_Anticoll()\n\n    # If we have the UID, continue\n    if status == MIFAREReader.MI_OK:\n\n        # Print UID\n        print(\"Card read UID: %s,%s,%s,%s\" % (uid[0], uid[1], uid[2], uid[3]))\n           \n        # Get old authentication key\n        key = make_pin(old_pin)[0:6]\n        \n        # Select the scanned tag\n        MIFAREReader.MFRC522_SelectTag(uid)\n\n        # Authenticate\n        status = MIFAREReader.MFRC522_Auth(MIFAREReader.PICC_AUTHENT1A, 8, key, uid)\n\n        print(\"\\n\")\n\n        # Check if authenticated\n        if status == MIFAREReader.MI_OK:\n            \n            # Get new athorization data\n            new_pin_data = make_pin(new_pin) \n\n            print(\"\\nWriting new PIN to IOTA debit card...\")\n            \n            # Write new authorization data to block 11\n            status = MIFAREReader.MFRC522_Auth(MIFAREReader.PICC_AUTHENT1A, 8, key, uid)\n            MIFAREReader.MFRC522_Write(11, new_pin_data)\n            \n            # Write new authorization data to block 15\n            status = MIFAREReader.MFRC522_Auth(MIFAREReader.PICC_AUTHENT1A, 12, key, uid)\n            MIFAREReader.MFRC522_Write(15, new_pin_data)\n\n            # Stop\n            MIFAREReader.MFRC522_StopCrypto1()\n            \n            # Print confirmation message\n            print(\"\\nThe following PIN code is now the active PIN: \" + new_pin)\n\n            # Make sure to stop reading for cards\n            continue_reading = False\n        else:\n            print(\"Authentication error\")\n","sub_path":"paymentsystem/src/iota_debit_card_pin.py","file_name":"iota_debit_card_pin.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"551229947","text":"from src.OC import csv_manager\nimport pandas as pd\nimport os\nfrom tqdm import tqdm\nfrom src.udf import read_env\n\npd.options.mode.chained_assignment = None\n\n'''\n|--------------------------------------------------------------------------\n| DESCRIPTION\n|--------------------------------------------------------------------------\n\nREQUIRED SCRIPT: /\n\n'''\n\nif __name__ == '__main__':\n    '''\n    |--------------------------------------------------------------------------\n    | INPUT VARIABLES\n    |--------------------------------------------------------------------------\n    '''\n\n    dict_variable = read_env.take_env_variables('../.env')\n\n    path_to_csv = os.path.join('..', dict_variable['output_directory'], 'OC', 'filtered')\n\n    path_to_output = os.path.join('..', dict_variable['output_directory'], 'OC', 'group_by_year')\n\n    '''\n    |--------------------------------------------------------------------------\n    | LOAD\n    |--------------------------------------------------------------------------\n    '''\n\n    # take all csv filtered by OpenCitations\n    all_csv = os.listdir(os.path.join(path_to_csv))\n\n    all_csv_already_groupBy = os.listdir(os.path.join(path_to_output, 'normal'))\n\n    all_csv = list(set(all_csv).difference(set(all_csv_already_groupBy)))\n\n    all_csv = [os.path.join(path_to_csv, path) for path in all_csv]\n\n    '''\n    |--------------------------------------------------------------------------\n    | EXECUTION\n    |--------------------------------------------------------------------------\n    '''\n    print('start with group by operation of filtered repository')\n    for csv in tqdm(all_csv):\n        name_file = csv.split('\\\\')[-1]\n\n        df = pd.read_csv(csv, dtype={\n            'oci': str,\n            'creation': str,\n            'citing': str,\n            'citing_journal': str,\n            'isDOAJ_citing': str,\n            'cited': str,\n            'cited_journal': str,\n            'isDOAJ_cited': str\n        })\n\n        # df = csv_manager.add_isDOAJ(df)\n\n        df = csv_manager.add_year(df, 'creation')\n\n        df, df_null, df_wrong = csv_manager.save_errors(df, name_file)\n\n        df_normal = csv_manager.groupBy_year(df)\n\n        df_by_journal = csv_manager.groupBy_year_and_journal(df)\n\n        '''\n        |--------------------------------------------------------------------------\n        | SAVE\n        |--------------------------------------------------------------------------\n        '''\n\n        df_normal.to_csv(os.path.join(path_to_output, 'normal', name_file))\n\n        df_by_journal.to_csv(os.path.join(path_to_output, 'by_journal', name_file))\n\n        df_null.to_csv(os.path.join('..', dict_variable['output_directory'], 'errors', 'null_dates', name_file))\n\n        df_wrong.to_csv(os.path.join('..', dict_variable['output_directory'], 'errors', 'wrong_dates', name_file))\n","sub_path":"run/3_groupBy_OC.py","file_name":"3_groupBy_OC.py","file_ext":"py","file_size_in_byte":2841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"180485533","text":"# load_db_data.py\n\nfrom loguru import logger\nimport os\nimport pathlib\nfrom typing import Optional\n\nfrom .helper import Shared, get_file_extension\n\nFIND_PART_PATH = 'select ParentID from Dirs where Path like :newPath;'\n\nFIND_EXACT_PATH = 'select DirID, Path from Dirs where Path = :newPath;'\n\nCHANGE_PARENT_ID = '''update Dirs set ParentID = :newId\n where ParentID = :currId and Path like :newPath and DirID != :newId;'''\n\nFIND_FILE = 'select * from Files where DirID = :dir_id and FileName = :file;'\n\nINSERT_DIR = 'insert into Dirs (Path, ParentID, FolderType) values (:path, :id, 0);'\n\nINSERT_FILE = 'insert into Files (DirID, FileName, ExtID) values (:dir_id, :file, :ext_id);'\n\nFIND_EXT = 'select ExtID from Extensions where Extension = ?;'\n\nINSERT_EXT = 'insert into Extensions (Extension, GroupID) values (:ext, 0);'\n\n\nclass LoadDBData:\n    \"\"\"\n    class LoadDBData\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        class LoadDBData\n        :param connection: - connection to database\n        \"\"\"\n        self.conn = Shared['DB connection']\n        self.cursor = self.conn.cursor()\n        self.updated_dirs = set()\n\n    def get_updated_dirs(self):\n        return self.updated_dirs\n\n    def load_data(self, path_, ext_):\n        \"\"\"\n        Load data in data base\n        :param data: - iterable lines of file names with full path\n        :return: None\n        \"\"\"\n        logger.debug(' | '.join((path_, ext_)))\n        files = LoadDBData._yield_files(path_, ext_)\n        for line in files:\n            logger.debug(' : '.join(('line', line)))\n            path = os.path.dirname(line)\n            idx = self.insert_dir(path)\n            self.updated_dirs.add(str(idx))\n            self.insert_file(idx, line)\n        self.conn.commit()\n\n    def insert_file(self, dir_id, full_file_name):\n        \"\"\"\n        Insert file into Files table\n        :param dir_id:\n        :param full_file_name:\n        :return: None\n        \"\"\"\n        logger.debug(' | '.join((str(dir_id), full_file_name)))\n        file_ = os.path.basename(full_file_name)\n\n        logger.debug(file_)\n\n        item = self.cursor.execute(FIND_FILE, {'dir_id': dir_id[0], 'file': file_}).fetchone()\n        if not item:\n            ext_id, _ = self.insert_extension(file_)\n            if ext_id:      # files with an empty extension are not handled\n                self.cursor.execute(INSERT_FILE, {'dir_id': dir_id,\n                                                  'file': file_,\n                                                  'ext_id': ext_id})\n\n    def insert_extension(self, file: str) -> (int, str):\n        '''\n        insert or find extension in DB\n        :param file - file name\n        returns (ext_id, extension_of_file)\n        '''\n        ext = get_file_extension(file)\n        if ext:\n            item = self.cursor.execute(FIND_EXT, (ext,)).fetchone()\n            if item:\n                idx = item[0]\n            else:\n                self.cursor.execute(INSERT_EXT, {'ext': ext})\n                idx = self.cursor.lastrowid\n                self.conn.commit()\n        else:\n            idx = 0\n        return idx, ext\n\n    def insert_dir(self, path: str) -> (int, bool):\n        '''\n        Insert directory into Dirs table\n        :param path:\n        :return: (dirID, is_created)\n        \"is_created = false\" doesn't mean error, dirID already exists\n        '''\n        idx, parent_path = self.search_closest_parent(path)\n        if parent_path == path:\n            return idx, False\n\n        self.cursor.execute(INSERT_DIR, {'path': path, 'id': idx})\n        idx = self.cursor.lastrowid\n\n        self.change_parent(idx, path)\n        self.conn.commit()\n        return idx, True\n\n    def change_parent(self, new_parent_id, path):\n        old_parent_id = self.parent_id_for_child(path)\n        if old_parent_id != -1:\n            self.cursor.execute(CHANGE_PARENT_ID, {'currId': old_parent_id,\n                                                       'newId': new_parent_id,\n                                                       'newPath': path + '%'})\n\n    def parent_id_for_child(self, path):\n        '''\n        Check the new file path:\n          if it can be parent for other directories\n        :param path:\n        :return: parent Id of first found child, -1 if not children\n        '''\n        item = self.cursor.execute(FIND_PART_PATH, {'newPath': path + '%'}).fetchone()\n        if item:\n            idx = item[0]\n        else:\n            idx = -1\n\n        return idx\n\n    def search_closest_parent(self, path: pathlib.PurePath):\n        '''\n        Search parent directory in DB\n        :param path:  file path\n        :return:  (ID of parent directory, path_of_parent_directory) or (0, '')\n        '''\n        res = (0, None)\n        for path_ in pathlib.Path(path).parents:\n            logger.debug(path_)\n            item = self.cursor.execute(FIND_EXACT_PATH, (str(path_),)).fetchone()\n            if item:\n                res = tuple(item)\n                break\n \n        return res\n\n    @staticmethod\n    def _yield_files(root, extensions):\n        \"\"\"\n        generator of file list\n        :param root: root directory\n        :param extensions: list of extensions\n        :return: generator\n        \"\"\"\n        logger.debug(' | '.join((\"extensions:\", extensions)))\n        ext_ = tuple(x.strip('. ') for x in extensions.split(','))\n        logger.debug(' | '.join((\"ext_:\", *ext_)))\n        for dir_name, _, file_names in os.walk(root):\n            logger.debug(dir_name)\n            if (not extensions) | (extensions == '*'):\n                for filename in file_names:\n                    logger.debug(filename)\n                    yield os.path.join(dir_name, filename)\n            else:\n                for filename in file_names:\n                    if get_file_extension(filename) in ext_:\n                        yield os.path.join(dir_name, filename)\n\n\nif __name__ == \"__main__\":\n    pass\n","sub_path":"cucumber/core/load_db_data.py","file_name":"load_db_data.py","file_ext":"py","file_size_in_byte":5910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"381575184","text":"import pytest\nfrom unittest.mock import Mock\nimport asyncio\nimport numpy as np\n\nfrom farmbot_controller.move_controller import MoveController\n\n\n@pytest.mark.parametrize(\n    \"current_position, wanted_position, expected_result, position_not_reached\",\n    [\n        ([0, 0, 0], [2, 3, 3], [1, 1, 1], [True, True, True]),\n        ([0, 0, 0], [0, 3, 3], [0, 1, 1], [False, True, True]),\n        ([0, 1.5, 0], [0, 2, 3], [0, 2, 1], [False, False, True]),\n    ],\n)\ndef test_increment_or_stop(\n    current_position,\n    wanted_position,\n    expected_result,\n    position_not_reached,\n):\n    m = MoveController(Mock())\n\n    new_position_not_reached, new_position = m.increment_or_stop(\n        np.array(current_position),\n        np.array(wanted_position),\n        np.array([1, 1, 1]),\n    )\n\n    print(new_position)\n    print(new_position_not_reached)\n\n    assert (new_position == expected_result).all()\n    assert (new_position_not_reached == position_not_reached).all()\n\n\n@pytest.mark.asyncio\nasync def test_go_to_destination():\n    m = MoveController(Mock())\n\n    m.go_to_destination(np.array([2, 1, 1]), np.array([1, 1, 1]))\n    await m.wait_ready()\n","sub_path":"test/test_move_controller.py","file_name":"test_move_controller.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"63077902","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCREATE SQL SCRIPT FROM PATIENT INFO PROVIDED BY CONSULTING REGISTER\n\"\"\"\n\nimport json\nimport pandas as pd\nimport numpy as np\n\nprefix = ''\njson_path =  prefix + '20kpatients.json'\n\n\n## load CR json, filter \n\n\nwith open(json_path, 'rb') as f:\n    patient_dict = json.load(f)\n    \npatients = pd.DataFrame.from_dict(patient_dict, orient=\"index\")\n\npatients = patients[patients.gender != 'Male']\npatients = patients[patients.firstName.notnull()]\npatients = patients[patients.lastName.notnull()]\npatients = patients[patients.NHISNumber.notnull()]\npatients = patients.fillna(value={\"gender\":\"\", \"tribe\":\"\", \"age\":0})\npatients['gender'] = patients['gender'].astype(np.str)\npatients['tribe'] = patients['tribe'].astype(np.str)\n\n\n\npatients['age'] = patients['age'].astype(np.int16)\npatients['NHISNumber'] = patients['NHISNumber'].astype(np.int32)\n\n\nn = len(patients)\n\n## define sql statements \n\n\nbuild_table = \"CREATE TABLE IF NOT EXISTS BasePatient \\n\" +\\\n            \"(entityId SERIAL PRIMARY KEY, firstName VARCHAR(255), \\n\" +\\\n            \"lastName VARCHAR(255), gender VARCHAR(10), tribe VARCHAR(255), \\n\"+\\\n            \"address VARCHAR(255), NHISnumber INT, age INT);\\n\"\n\ninsert_stmt = \"INSERT INTO BasePatient \\n\"+\\\n            \"(firstName, lastName, gender, tribe, address, NHISnumber, age) VALUES \\n\"\n            \n\n\n       \n    \nwith open(prefix + 'load_patients_v2.sql', 'w') as f:\n    f.write(build_table)\n    f.write(insert_stmt)\n    for i in range(n):\n        if i%100 == 0:\n            print(i)\n        obs = patients.iloc[i]\n        firstname = obs['firstName']\n        lastname = obs['lastName']\n        gender = obs['gender']\n        tribe = obs['tribe']\n        nhis = obs['NHISNumber']\n        address = obs['address']\n        age = obs['age']\n        \n        \n        keys = [k for k in address.keys()]\n        adstr = \"\"\n        for k in keys:\n            adstr += k + \":\" + address[k] + \" \"\n            \n        value = \"(\" + \"\\'\" + firstname + \"\\'\" + \",\" + \"\\'\" + lastname + \"\\'\" + \",\" + \"\\'\" + gender + \"\\'\" + \",\" + \"\\'\" + tribe + \"\\'\" + \",\" + \"\\'\" + adstr + \"\\'\" + \",\" + str(int(nhis)) + \",\" + str(int(age)) + \")\"\n        \n        if i == (n-1):\n            value += '\\n;'\n        else:\n            value += ',\\n'\n        f.write(value)\n            \n        \n          \n        \n            ","sub_path":"Antenatal-Care-2020-master/documentation/build-sql-scripts.py","file_name":"build-sql-scripts.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"13470013","text":"from torch.nn import Module, Linear\nfrom transformers import BertModel\nimport torch.nn.functional as F\nfrom torch.nn import CrossEntropyLoss, MSELoss\n\nclass BertWithLinearLayer(Module):\n    def __init__(self, bert_pretrained_path, num_labels=2):\n        super(BertWithLinearLayer, self).__init__()\n        self.num_labels = num_labels\n        self.bert = BertModel.from_pretrained(bert_pretrained_path, cache_dir=None)\n        self.fc1 = Linear(768, 512)\n        self.out = Linear(512, 2)\n        # self.dropout = Dropout(HIDDEN_DROPOUT)\n\n    def forward(self,\n        input_ids=None,\n        attention_mask=None,\n        token_type_ids=None,\n        position_ids=None,\n        head_mask=None,\n        inputs_embeds=None,\n        labels=None,):\n\n        outputs = self.bert(input_ids=input_ids, attention_mask=attention_mask,\n                      token_type_ids=token_type_ids)\n        x = F.relu(self.fc1(outputs[1]))\n        logits = self.out(x)\n\n        outputs = (logits,) + outputs[2:]  # add hidden states and attention if they are here\n\n        if labels is not None:\n            if self.num_labels == 1:\n                #  We are doing regression\n                loss_fct = MSELoss()\n                loss = loss_fct(logits.view(-1), labels.view(-1))\n            else:\n                loss_fct = CrossEntropyLoss()\n                loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1))\n            outputs = (loss,) + outputs\n\n        return outputs\n\n    @classmethod\n    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):\n        return cls(pretrained_model_name_or_path)\n\n    def save_pretrained(self, save_directory):\n        pass","sub_path":"examples/BertWithLinearLayer.py","file_name":"BertWithLinearLayer.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"523992804","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport torch\nfrom .point_utils import center_size, point_form\n\n\ndef decode(loc, dbox_list, variances=[0.1, 0.2]):\n    \"\"\"\n    dbox: (cx_d, cy_d, w_d, h_d)\n    loc: (delta_x, delta_y,delta_w, delta_h)\n    bbox: (cx, cy, w, h)\n    cx = cx_d + variances[0] * delta_x * w_d\n    cy = cy_d + variances[0] * delta_y * w_d\n    w = w_d * exp(variances[1] * delta_w)\n    h = h_d * exp(variances[1] * delta_w)\n\n    Parameters\n    ----------\n\n    loc : [8732, 4]\n    dbox_list : [8732, 4]\n\n    Returns\n    -------\n    bboxes : (tensor) (x_min, x_max, y_min, y_max)\n    \"\"\"\n    bboxes = torch.cat(\n        (\n            dbox_list[:, :2] + loc[:, :2] * variances[0] * dbox_list[:, 2:],\n            dbox_list[:, 2:] * torch.exp(variances[1] * loc[:, 2:]),\n        ),\n        dim=-1,\n    )\n\n    return point_form(bboxes)\n\n\ndef encode(bboxes, dbox_list, variances=[0.1, 0.2]):\n    \"\"\"\n    bboxes : (tensor) (x_min, x_max, y_min, y_max) (8732, 4)\n    dbox_list : (tensor) (8732, 4)\n    \"\"\"\n    center_points = center_size(bboxes)\n\n    g_cxcy = (center_points[:, :2] - dbox_list[:, :2]) / (\n        variances[0] * dbox_list[:, 2:]\n    )\n    g_wh = torch.log(center_points[:, 2:] / dbox_list[:, 2:]) / variances[1]\n\n    # (num default box, 4)\n    return torch.cat((g_cxcy, g_wh), dim=-1)\n","sub_path":"models/functions/decode_encode.py","file_name":"decode_encode.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"240189266","text":"import matplotlib.pyplot as plt\nplt.switch_backend('agg')\n\nimport numpy as np\nimport os\n\nimport argparse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('a', type=str) # log file path\nparser.add_argument('b', type=str) # output file name\nparser.add_argument('c', type=str) # graph title\n\n\n\nargs = parser.parse_args()\n\n\nwith open(os.path.abspath(args.a),'r') as f:\n    data = [l for l in f.readlines()]\n\ndata = [e.replace('/',' ').replace(':',' ').replace(',',' ').split() for e in data[:-2]] # the last two lines does not count for epochs and accuracies\n\ndata = np.array(data)[:,[1,5,8]].astype(float)\n\n\nplt.plot(data[:,0], data[:,1], 'r', label='training')\nplt.plot(data[:,0], data[:,2], 'g', label='validation')\nplt.legend(loc='lower right')\nplt.xlabel('epoch')\nplt.ylabel('loss')\nplt.title(args.c+('\\ntraining: %f' % np.min(data[:,1]))+(', validation:%f' % np.min(data[:,2]))+(', best validation: epoch %d' % np.argmin(data[:,2])))\nplt.savefig(os.path.join(os.path.abspath('./'),args.b)+'.loss.png')\n","sub_path":"Algorithms/shawn/loss/exp_VH/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"361703644","text":"\"\"\"empty message\n\nRevision ID: ef8a7e184062\nRevises: f91f8d4348ac\nCreate Date: 2020-01-12 15:47:31.570509\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'ef8a7e184062'\ndown_revision = 'f91f8d4348ac'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('garbagetype',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('type', sa.Integer(), nullable=True),\n    sa.PrimaryKeyConstraint('id'),\n    sa.UniqueConstraint('type')\n    )\n    op.create_table('garbages',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('filename', sa.String(length=64), nullable=True),\n    sa.Column('type_id', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['type_id'], ['garbagetype.id'], ),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_index(op.f('ix_garbages_filename'), 'garbages', ['filename'], unique=True)\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index(op.f('ix_garbages_filename'), table_name='garbages')\n    op.drop_table('garbages')\n    op.drop_table('garbagetype')\n    # ### end Alembic commands ###\n","sub_path":"garbage_front_end_rt_v3/migrations/versions/ef8a7e184062_.py","file_name":"ef8a7e184062_.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"592490896","text":"# -*- coding: utf-8 -*-\n\"\"\"\npoll meetup.com for a member_id and automatically rsvp yes to new events\nsend confirmation email with link and description\n\"\"\"\nimport time\nimport requests\nfrom datetime import datetime\nfrom xtools import send\nimport yaml\nimport os\nfrom logcon import log\n\nmeetups = yaml.load(open(os.path.join(\n            os.path.expanduser(\"~\"), \".meetups.yaml\")))\n\ndef get_events():        \n    \"\"\" return new events (rsvp=none) \"\"\"\n    r=requests.get(\"https://api.meetup.com/2/events\",\n                    params=dict(key=meetups[\"key\"], sign=True,\n                                rsvp=\"none\",\n                                fields=\"rsvpable\",\n                                member_id=meetups[\"member_id\"]))\n    try:\n        return r.json()[\"results\"]\n    except:\n        log.info(r.status_code)\n        log.info(r.text)\n        return []\n\ndef rsvp(events):\n    \"\"\" for each event, rsvp yes and send confirmation email \"\"\"\n    for event in events:\n        if not event[\"rsvpable\"]:\n            continue\n        \n        # rsvp yes\n        r=requests.post(\"https://api.meetup.com/2/rsvp\",\n                        params=dict(key=meetups[\"key\"], sign=True,\n                        event_id=event[\"id\"],\n                        rsvp=\"yes\"))\n        \n        # compile email\n        subject = \"auto_rsvp_yes to %s\"%event[\"name\"]       \n        content = event[\"description\"]\n        if r.status_code != 201:\n            subject = \"FAILED \" + subject\n            content = \"%s<br><br>%s\"%(r.status_code, r.text)\n        time = datetime.fromtimestamp(int(event[\"time\"])/1000) \n        time = time.strftime('%Y-%m-%d %H:%M')\n        url = \"\"\n        try:\n            url = \"https://www.meetup.com/{group}/events/{id}/\" \\\n                    .format(group=event[\"group\"][\"urlname\"], id=event[\"id\"])\n        except Exception:\n            log.exception(url)\n        try:\n            message = \"{content}\".format(content=content)\n        except Exception:\n            log.exception(content)\n\n        message = \"{url}<br><br>{time}<br><br>{content}\".format(**locals())\n\n        send(subject, message)\n    \ndef main():\n    \"\"\" periodic check for new events and automatically rsvp \"\"\"\n    minutes = 10\n    log.info(\"Every %s minutes, check for new meetups and automatically RSVP\"%minutes)\n    while True:\n        try:\n            events = get_events()\n            if \"rsvpgroups\" in meetups:\n                events = [e for e in events \n                      if e[\"group\"][\"urlname\"] in meetups[\"rsvpgroups\"]]\n            rsvp(events)\n            time.sleep(minutes * 60)\n        except Exception as e:\n            log.exception(e)\n            \nif __name__ == \"__main__\":\n    main()","sub_path":"meetups.py","file_name":"meetups.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"377956580","text":"#!/usr/bin/env python3\n# -*- coding: UTF-8 -*-\nimport json\nimport os\nimport platform\nimport pprint\nimport re\nimport sys\nimport time\nimport urllib.request\nfrom random import shuffle\nfrom shutil import copyfile\n\nimport spotipy\nfrom slugify import slugify\n\nfrom core import const\nfrom core import handle\nfrom core import internals\nfrom core import metadata\nfrom core import downloader\nfrom core import spotify_tools\n\n__version__ = '0.9.3'\n\nduration_debug = True if os.getenv('DURATION_DEBUG') else False\n\n\ndef check_exists(music_file, raw_song, meta_tags, folder):\n    \"\"\" Check if the input song already exists in the given folder. \"\"\"\n    log.debug('Cleaning any temp files and checking '\n              'if \"{}\" already exists'.format(music_file))\n    songs = os.listdir(folder)\n    for song in songs:\n        if song.endswith('.temp'):\n            os.remove(os.path.join(folder, song))\n            continue\n        # check if any song with same name is already present in the given folder\n        if os.path.splitext(song)[0] == music_file:\n            log.debug('Found an already existing song: \"{}\"'.format(song))\n            if internals.is_spotify(raw_song):\n                # check if the already downloaded song has correct metadata\n                # if not, remove it and download again without prompt\n                already_tagged = metadata.compare(os.path.join(folder, song), meta_tags)\n                log.debug('Checking if it is already tagged correctly? {}'.format(already_tagged))\n                if not already_tagged:\n                    os.remove(os.path.join(folder, song))\n                    return False\n\n            log.debug('Skipping \"{}\"'.format(song))\n            return True\n    return False\n\n\ndef download_list(text_file, folder=None):\n    \"\"\" Download all songs from the list. \"\"\"\n    log.debug('Python version: {}'.format(sys.version))\n    log.debug('Platform: {}'.format(platform.platform()))\n    with open(text_file, 'r') as listed:\n        lines = (listed.read()).splitlines()\n    # ignore blank lines in text_file (if any)\n    try:\n        lines.remove('')\n    except ValueError:\n        pass\n\n    shuffle(lines)\n    log.info('Preparing to download {} songs from {}\\n'.format(len(lines), str(text_file).rsplit('/')[-1]))\n    downloaded_songs = []\n    timeout = time.time() + int(os.getenv('MAX_DOWNLOAD_TIME_PLAYLIST_MIN', '45')) * 60\n\n    dead_tracks = []\n    dead_tracks_filename = '_dead_tracks.json'\n    try:\n        with open(os.path.join(folder, dead_tracks_filename), 'r') as f:\n            dead_tracks = json.load(f)\n            log.debug('Found dead tracks:')\n            log.debug(dead_tracks)\n    except OSError:\n        pass\n\n    for number, raw_song in enumerate(lines, 1):\n        if time.time() > timeout:\n            log.info('Playlist timeout! Stopping download of playlist.\\nDownloaded {} tracks.'.format(number - 1))\n            return False\n\n        if raw_song in dead_tracks:\n            log.info('Skipping {} as it is in dead tracks'.format(raw_song))\n            continue\n\n        try:\n            positive_download = download_single(raw_song, number=number, folder=folder)\n        # token expires after 1 hour\n        except spotipy.client.SpotifyException:\n            # refresh token when it expires\n            log.debug('Token expired, generating new one and authorizing')\n            new_token = spotify_tools.generate_token()\n            spotify_tools.spotify = spotipy.Spotify(auth=new_token)\n            positive_download = download_single(raw_song, number=number, folder=folder)\n        # detect network problems\n        except (urllib.request.URLError, TypeError, IOError) as e:\n            lines.append(raw_song)\n            # remove the downloaded song from file\n            internals.trim_song(text_file)\n            # and append it at the end of file\n            with open(text_file, 'a') as myfile:\n                myfile.write(raw_song + '\\n')\n            log.error(repr(e))\n            log.warning('Failed to download song. Will retry after other songs\\n')\n            # wait 0.5 sec to avoid infinite looping\n            time.sleep(0.5)\n            continue\n        if not positive_download:\n            log.info('Adding song to dead tracks')\n            dead_tracks.append(raw_song)\n        downloaded_songs.append(raw_song)\n        log.debug('Removing downloaded song from text file')\n        internals.trim_song(text_file)\n\n    if len(dead_tracks) > 0:\n        try:\n            with open(os.path.join(folder, dead_tracks_filename), 'w') as f:\n                json.dump(dead_tracks, f)\n                log.debug('Stored dead tracks:')\n        except OSError:\n            pass\n\n    log.info('Finished downloading {} songs from {}\\n'.format(len(downloaded_songs), str(text_file).rsplit('/')[-1]))\n    return True\n\n\ndef download_single(raw_song, number=None, folder=None) -> bool:\n    \"\"\" Logic behind downloading a song. \"\"\"\n    meta_tags = spotify_tools.generate_metadata(raw_song)\n\n    if const.args.download_only_metadata and meta_tags is None:\n        log.info('Found no metadata. Skipping the download')\n        return False\n\n    # generate file name of the song to download\n    songname = 'foo'\n\n    if meta_tags is not None:\n        refined_songname = internals.format_string(const.args.file_format,\n                                                   meta_tags,\n                                                   slugification=True)\n        log.debug('Refining songname from \"{0}\" to \"{1}\"'.format(songname, refined_songname))\n        if not refined_songname == ' - ':\n            songname = refined_songname\n    else:\n        log.warning('Could not find metadata')\n        songname = internals.sanitize_title(songname)\n\n    if not check_exists(songname, raw_song, meta_tags, folder):\n        # deal with file formats containing slashes to non-existent directories\n        if folder:\n            folder_path = folder\n        else:\n            folder_path = const.args.folder\n        songpath = os.path.join(folder_path, os.path.dirname(songname))\n        os.makedirs(songpath, exist_ok=True)\n        file_name = os.path.join(folder_path, songname + const.args.output_ext)\n        try:\n            downloader.download_and_convert(meta_tags['uri'], file_name)\n        except RuntimeError as e:\n            log.error('Download failed! Deleting: {}'.format(songname))\n            try:\n                os.remove(file_name)\n            except OSError:\n                pass\n            return False\n        if not const.args.no_metadata and meta_tags is not None:\n            metadata.embed(file_name, meta_tags)\n        return True\n    return True\n\n\ndef main():\n    const.args = handle.get_arguments()\n\n    if const.args.version:\n        print('spotdl {version}'.format(version=__version__))\n        sys.exit()\n\n    internals.filter_path(const.args.folder)\n\n    const.log = const.logzero.setup_logger(formatter=const._formatter,\n                                           level=const.args.log_level)\n    global log\n    log = const.log\n    log.debug('Python version: {}'.format(sys.version))\n    log.debug('Platform: {}'.format(platform.platform()))\n    log.debug(pprint.pformat(const.args.__dict__))\n\n    try:\n        if const.args.song:\n            download_single(raw_song=const.args.song)\n        elif const.args.list:\n            if os.path.isdir(const.args.list):\n                files = [f for f in os.listdir(const.args.list) if\n                         re.match(r'.*\\.txt', f) and not re.match(r'requirements\\.txt', f)]\n                shuffle(files)\n                timeout = time.time() + int(os.getenv('MAX_DOWNLOAD_TIME_MIN', '300')) * 60\n                index = 0\n                while len(files) > 0:\n                    list_file = files[index]\n                    if time.time() > timeout:\n                        log.info('Total timeout! Stopping download of playlists.')\n                        break\n                    folder = os.path.join(const.args.folder, list_file).rstrip('.txt')\n                    internals.filter_path(folder)\n                    if download_list(os.path.join(const.args.list, list_file), folder):\n                        files.pop(index)\n                    else:\n                        index = index + 1\n                    if index >= len(files):\n                        index = 0\n            else:\n                download_list(text_file=const.args.list)\n        elif const.args.playlist:\n            spotify_tools.write_playlist(playlist_url=const.args.playlist)\n        elif const.args.album:\n            spotify_tools.write_album(album_url=const.args.album)\n        elif const.args.username:\n            if not os.path.exists('./playlists'):\n                os.makedirs('./playlists')\n            spotify_tools.write_user_playlist(username=const.args.username, path='./playlists')\n\n        # actually we don't necessarily need this, but yeah...\n        # explicit is better than implicit!\n        sys.exit(0)\n\n    except KeyboardInterrupt as e:\n        log.exception(e)\n        sys.exit(3)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"spotdl.py","file_name":"spotdl.py","file_ext":"py","file_size_in_byte":9064,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"620936777","text":"# SPDX-FileCopyrightText: 2021 ladyada for Adafruit Industries\n# SPDX-License-Identifier: MIT\n\nimport paho.mqtt.client as mqtt\nimport uuid\n\n# Every client needs a random ID\nclient = mqtt.Client(str(uuid.uuid1()))\n# configure network encryption etc\nclient.tls_set()\n# this is the username and pw we have setup for the class\nclient.username_pw_set('idd', 'device@theFarm')\n\n#connect to the broker\nclient.connect(\n    'farlab.infosci.cornell.edu',\n    port=8883)\n\nimport board\nimport busio\nimport adafruit_ssd1306\n# import pygame\nimport adafruit_mpr121\nfrom collections import deque\nfrom PIL import Image, ImageDraw, ImageFont\nfont = ImageFont.truetype(\"/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf\", 18)\n\n# Create the I2C interface.\ni2c = busio.I2C(board.SCL, board.SDA)\n\nmpr121 = adafruit_mpr121.MPR121(i2c)\n\n# Create the SSD1306 OLED class.\n# The first two parameters are the pixel width and pixel height.  Change these\n# to the right size for your display!\noled = adafruit_ssd1306.SSD1306_I2C(128, 32, i2c)\nimage = Image.new(\"1\", (oled.width, oled.height))\ndraw = ImageDraw.Draw(image)\n\nmusic_script = deque()\n\n# the # wildcard means we subscribe to all subtopics of IDD\ntopic = 'IDD/James'\n\n#this is the callback that gets called once we connect to the broker. \n#we should add our subscribe functions here as well\ndef on_connect(client, userdata, flags, rc):\n\tprint(f\"connected with result code {rc}\")\n\tclient.subscribe(topic)\n\t# you can subsribe to as many topics as you'd like\n\t# client.subscribe('some/other/topic')\n\ncorrect_keys = {'one':1, 'two':2, 'three':3, 'four':4, 'five':5}\n# this is the callback that gets called each time a message is recived\ndef on_message(cleint, userdata, msg):\n    instructions = msg.payload.decode('UTF-8')\n    print(instructions)\n    new_script = []\n    for instruction in instructions:\n        if instruction.isdigit():\n            new_script.append(int(instruction))\n\n    music_script.extend(new_script)\n\t# you can filter by topics\n\t# if msg.topic == 'IDD/some/other/topic': do thing\n\n\n# Every client needs a random ID\nclient2 = mqtt.Client(str(uuid.uuid1()))\n# configure network encryption etc\nclient2.tls_set()\n# this is the username and pw we have setup for the class\nclient2.username_pw_set('idd', 'device@theFarm')\n\n# attach out callbacks to the client\nclient2.on_connect = on_connect\nclient2.on_message = on_message\n\n#connect to the broker\nclient2.connect(\n    'farlab.infosci.cornell.edu',\n    port=8883)\n\n# this is blocking. to see other ways of dealing with the loop\n#  https://www.eclipse.org/paho/index.php?page=clients/python/docs/index.php#network-loop\n\n# def play_key(key):\n#     pygame.mixer.init()\n#     if key == 1:\n#         pygame.mixer.music.load(\"./sound/c.mp3\")\n#     elif key == 2:\n#         pygame.mixer.music.load(\"./sound/d.mp3\")\n#     elif key == 3:\n#         pygame.mixer.music.load(\"./sound/e.mp3\")\n#     elif key == 4:\n#         pygame.mixer.music.load(\"./sound/f.mp3\")\n#     else:\n#         pygame.mixer.music.load(\"./sound/g.mp3\")\n#     pygame.mixer.music.play()\n\n# start with a blank screen\noled.fill(0)\n# we just blanked the framebuffer. to push the framebuffer onto the display, we call show()\noled.show()\nwhile True:\n    if not music_script:\n        next_text = 'DONE'\n        client2.loop()\n    else:\n        next_text = 'Play ' + str(music_script[0])\n\n    draw.rectangle((0, 0, oled.width, oled.height), outline=0, fill=0)\n    draw.text((25, 5), next_text, font=font, fill=\"#0000FF\")\n    oled.image(image)\n    # show all the changes we just made\n    oled.show()\n    if music_script and mpr121[music_script[0] + 5].value:\n        client.publish(\"IDD/John\", music_script[0])\n        music_script.popleft()\n    \n","sub_path":"Lab 6/piano.py","file_name":"piano.py","file_ext":"py","file_size_in_byte":3690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"3488274","text":"import os,sys\nimport numpy\nimport string\nimport IMP\nimport IMP.em\nimport pyRMSD.RMSDCalculator\n\ndef parse_custom_ranges(ranges_file):\n    fl = open(ranges_file, 'r')\n    density_custom_ranges = fl.readlines()[0].strip()\n    exec(density_custom_ranges)\n       \n    fl.close()\n    \n    return density_custom_ranges\n    \n    \ndef get_particles_from_superposed(cluster_conform_i, cluster_conform_0, masses, radii, align): \n\n    m=IMP.Model()\n    ps = []\n    if align:\n        calculator = pyRMSD.RMSDCalculator.RMSDCalculator(\"QCP_SERIAL_CALCULATOR\", numpy.array([cluster_conform_0, cluster_conform_i]))\n    else:\n        calculator = pyRMSD.RMSDCalculator.RMSDCalculator(\"NOSUP_SERIAL_CALCULATOR\", numpy.array([cluster_conform_0, cluster_conform_i]))\n\n    rmsd, superposed_fit = calculator.pairwise(0, 1, get_superposed_coordinates = True)\n\n    for particle_index in range(len(superposed_fit[1])): \n        p = IMP.Particle(m, \"%s\" % str(particle_index))\n    \n        IMP.core.XYZ.setup_particle(p, superposed_fit[1][particle_index])\n        IMP.core.XYZR.setup_particle(p, float(radii[particle_index]))\n        IMP.atom.Mass.setup_particle(p, float(masses[particle_index]))\n        m.update()\n        ps.append(p)\n\n    return rmsd, m, ps\n\nclass GetModelDensity(object):\n    \"\"\"Compute mean density maps from structures.\n    Keeps a dictionary of density maps,\n    keys are in the custom ranges. When you call add_subunits_density, it adds\n    particle coordinates to the existing density maps.\n    \"\"\"\n\n    def __init__(self, custom_ranges=None, resolution=20.0, voxel=5.0, bead_names = None):\n        \"\"\"Constructor.\n        @param list of particles decorated with mass, radius, and XYZ\n           @param resolution The MRC resolution of the output map (in Angstrom unit)\n           @param voxel The voxel size for the output map (lower is slower)\n        \"\"\"\n\n        self.MRCresolution = resolution\n        self.voxel = voxel\n        self.count_models = 0.0\n        self.densities={}\n        self.bead_names = bead_names\n        self.custom_ranges=custom_ranges\n        \n        # for each custom range get the particle indices that will be added to the density for that custom range\n        self.particle_indices_in_custom_ranges={}\n        \n        for density_name in self.custom_ranges:\n            self.particle_indices_in_custom_ranges[density_name]=[]\n        \n        # go through each bead, put it in the appropriate custom range(s)\n        for index,beadname in enumerate(self.bead_names):\n            for density_name in self.custom_ranges:\n                for domain in self.custom_ranges[density_name]: # each domain in the list custom_ranges[density_name]\n                     if self._is_contained(beadname,domain):\n                        self.particle_indices_in_custom_ranges[density_name].append(index)\n                        #print beadname,\"is in\",domain\n                        break # already added particle to this custom range\n\n    def normalize_density(self):\n        pass\n   \n    def _create_density_from_particles(self, ps, name,\n                                      kernel_type='GAUSSIAN'):\n        '''Internal function for adding to densities.\n        pass XYZR particles with mass and create a density from them.\n        kernel type options are GAUSSIAN, BINARIZED_SPHERE, and SPHERE.'''\n        kd = {\n            'GAUSSIAN': IMP.em.GAUSSIAN,\n            'BINARIZED_SPHERE': IMP.em.BINARIZED_SPHERE,\n            'SPHERE': IMP.em.SPHERE}\n        dmap = IMP.em.SampledDensityMap(ps, self.MRCresolution, self.voxel)\n        dmap.calcRMS()\n        dmap.set_was_used(True)\n\n        if name not in self.densities:\n            self.densities[name] = dmap\n        else:\n            bbox1 = IMP.em.get_bounding_box(self.densities[name])\n            bbox2 = IMP.em.get_bounding_box(dmap)\n            bbox1 += bbox2\n            dmap3 = IMP.em.create_density_map(bbox1,self.voxel)\n            dmap3.set_was_used(True)\n            dmap3.add(dmap)\n            dmap3.add(self.densities[name])\n            self.densities[name] = dmap3\n    \n    def _is_contained(self,bead_name,domain):\n        \"\"\" domain can be the name of a single protein or a tuple (start_residue,end_residue,protein_name)\n        bead is a string of type moleculeName_startResidue_endResidue\n        \"\"\"\n       \n        if type(domain)==tuple:\n            bead_protein, bead_res_start,bead_res_end = bead_name.split(\"_\")\n            bead_residues = set(range(int(bead_res_start),int(bead_res_end)+1))\n            domain_protein = domain[2]\n            domain_residues = set(range(int(domain[0]),int(domain[1])+1))\n            \n            if bead_protein == domain_protein and not domain_residues.isdisjoint(bead_residues):\n                return True\n            \n        else:\n            if domain in bead_name:\n                return True\n        \n        return False\n                \n    def add_subunits_density(self, ps):\n        \"\"\"Add a frame to the densities.\n        @param ps List of particles decorated with XYZR and Mass.\n        \"\"\"\n        self.count_models += 1.0\n        # initialize custom list of particles\n        particles_custom_ranges={}\n        for density_name in self.custom_ranges:\n            particles_custom_ranges[density_name]=[]\n        \n        # add each particle to the relevant custom list\n        for density_name in self.custom_ranges:\n            for particle_index in self.particle_indices_in_custom_ranges[density_name]:\n                particles_custom_ranges[density_name].append(ps[particle_index])\n                                    \n        #finally, add each custom particle list to the density\n        for density_name in self.custom_ranges:\n            self._create_density_from_particles(particles_custom_ranges[density_name],density_name)\n     \n    def get_density_keys(self):\n        return list(self.densities.keys())\n\n    def get_density(self,name):\n        \"\"\"Get the current density for some component name\"\"\"\n        if name not in self.densities:\n            return None\n        else:\n            return self.densities[name]\n        \n    def write_mrc(self, path=\"./\",file_prefix=\"\"):\n        for density_name in self.densities:\n            self.densities[density_name].multiply(1. / self.count_models)\n            IMP.em.write_map(\n                self.densities[density_name],\n                path + \"/\" + file_prefix + \"_\"+ density_name + \".mrc\",\n                IMP.em.MRCReaderWriter())\n","sub_path":"analys/scripts/Precision_RMSD.py","file_name":"Precision_RMSD.py","file_ext":"py","file_size_in_byte":6444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"554705354","text":"# Question 2\n\nimport pandas as pd\n\n\nmale = 0\nfemale = 0\n# reading csv file   \ndata = pd.read_csv('Traffic_Violations.csv')\nfor value in data.Gender :\n    if (value == \"M\") :\n        male += 1\n    else :\n        female += 1\nprint(\"\\nCount of male and female who violated traffic rules in MACOSX\\n\")\nprint(\"Male : {}\\nFemale : {}\\n\".format(male,female))\n\n","sub_path":"session 8/traffic_violation_pandas.py","file_name":"traffic_violation_pandas.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"56620072","text":"import timeit\nimport random\n\ndef main():\n\tlength = 10000\n\tarray = []\n\n\t# create an array of random numbers\n\tfor i in range(length):\n\t\tarray.append(random.randrange(length))\n\n\ttimeSort = timeit.default_timer()\n\tselectionSort(array, length)\n\ttimeSort = timeit.default_timer() - timeSort\n\n\tprint(\"Sorting execution time: %.3f seconds\" % timeSort)\n\ndef selectionSort(array, length):\n\tfor i in range(length):\n\t\tj = low = i\n\n\t\twhile j < length:\n\t\t\tif array[j] < array[low]:\n\t\t\t\tlow = j\n\t\t\tj += 1\n\n\t\tarray[i], array[low] = array[low], array[i]\n\nmain()","sub_path":"sort_selection.py","file_name":"sort_selection.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"488370728","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef add_layer(inputs, in_size, out_size, activation_function=None):  # 輸入值,輸入大小,輸出值,激勵函數\n    Weights = tf.Variable(tf.random_normal([in_size, out_size]))  # 隨機變量矩陣比全部0好\n    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)  # biases 推薦值不為0, 所以我們再0向量的基礎+0.1\n    Wx_plus_b = tf.matmul(inputs, Weights) + biases  # Wx_plus_b 是 神經網路未激活的 其中 tf.matmul是矩陣的乘法\n    if activation_function is None:  # 非線性\n        outputs = Wx_plus_b\n    else:\n        outputs = activation_function(Wx_plus_b)\n    return outputs\n\n\n\nx_data = np.linspace(-1,1,300)[:, np.newaxis]  #有三百行\nnoise = np.random.normal(0, 0.05, x_data.shape)\ny_data = np.square(x_data) - 0.5\n\n\nxs = tf.placeholder(tf.float32, [None, 1])  # 一定要加上 tf.float32 否則判斷錯��\nys = tf.placeholder(tf.float32, [None, 1])\n\nl1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)  # 使用 placeholder 的值\n\npredition = add_layer(l1, 10, 1, activation_function=None)\n\n\nloss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - predition), reduction_indices=[1]))\n\ntrain_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)\n\n\ninit = tf.global_variables_initializer()  # 初始\n\nsess = tf.Session()\n\nsess.run(init)\n\n\nfig = plt.figure()\nax = fig.add_subplot(1, 1, 1)\n\nax.scatter(x_data, y_data)\nplt.ion()  # 全局運行\n# plt.show()  # 本次運行\n\nfor i in range(10000):\n    sess.run(train_step, feed_dict={xs: x_data, ys: y_data})  # 因有placeholder 需要加上 feed_dict 只有有運用到 sess.run\n    if i % 50 == 0:\n        # to see the step improvement\n        # print(sess.run(loss, feed_dict={xs: x_data, ys: y_data}))\n        try:\n            ax.lines.remove(lines[0])\n        except Exception:\n            pass\n        predition_value = sess.run(predition, feed_dict={xs: x_data, ys: y_data})\n        lines = ax.plot(x_data, predition_value, 'r-', lw=5)\n        plt.pause(0.1)\n","sub_path":"Tensorflow/tf12-plot-result.py","file_name":"tf12-plot-result.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"107188041","text":"class Vertex:\n    def __init__(self):\n        self.row = None\n        self.col = None\n        self.adj = []  # for BFS\n        self.visited = False  # for BFS\n        self.start = False\n        self.end = False\n        self.successor = None\n        self.cost = None\n\n    # add an adjacent edge\n    def add_adjacent(self, v):\n        self.adj.append(v)","sub_path":"vertex.py","file_name":"vertex.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"8514850","text":"from rest_framework import serializers, routers, viewsets,permissions\nfrom contact.models import Contact,ContactType\nfrom api.organization.serializers import OrganizationsSerializer\n\nclass ContactTypeSerializer(serializers.ModelSerializer):\n\tclass Meta:\n\t\tmodel = ContactType\n\nclass ContactSerializer(serializers.ModelSerializer):\n\ttype = ContactTypeSerializer()\n\torganization = OrganizationsSerializer()\n\n\tclass Meta:\n\t\tmodel = Contact\n\t\tfields = [\n\t\t\t'id',\n\t\t\t'type',\n\t\t\t'organization',\n\t\t\t'first_name',\n\t\t\t'last_name',\n\t\t\t'age',\n\t\t\t'job_title',\n\t\t\t'phone1',\n\t\t\t'phone2',\n\t\t\t'phone3',\n\t\t\t'email1',\n\t\t\t'email2',\n\t\t\t'email3',\n\t\t\t'info',\n\t\t]\n\t\t\n\n\t\n\n","sub_path":"jmi/api/contact/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"8709924","text":"V, E = map(int, input().split())\ninf = float('inf')\ngraphs = [list(map(int, input().split())) for _ in range(E)]\n# コストが低い順に並べる\ngraphs.sort(key=lambda x: x[2])\n# print(graphs)\n\nnodes = [i for i in range(V)]\ncost = 0\n\n\ndef root(node):\n    if nodes[node] == node:\n        return node\n    nodes[node] = root(nodes[node])\n    return nodes[node]\n\n\nfor i, j, k in graphs:\n    if not root(i) == root(j):\n        cost += k\n        nodes[root(i)] = root(j)\nprint(cost)\n","sub_path":"AOJ/GRL/GRL_2_A2.py","file_name":"GRL_2_A2.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"490871486","text":"def function(N):\r\n    symbol = N[0]\r\n    result = 0\r\n\r\n    for ele in list(N):\r\n        if symbol == ele:\r\n            continue\r\n        else:\r\n            symbol = ele\r\n            result += 1\r\n\r\n    if symbol == '+':\r\n        return result\r\n    else:\r\n        return result + 1\r\n\r\n\r\nif __name__ == '__main__':\r\n    with open('A-small-attempt0.in') as fin:\r\n        T = int(fin.readline())\r\n        for _ in range(T):\r\n            N = fin.readline()[:-1]  # get input\r\n            out = function(N)\r\n            with open('out.txt', 'a') as fout:\r\n                fout.write('Case #%d: %s\\n' % (_ + 1, out))\r\n","sub_path":"codes/CodeJamCrawler/16_0_2/OhJack/Source-title2.py","file_name":"Source-title2.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"193211804","text":"#!/usr/bin/env python3\n'''Builds the RTX \"KG2\" second-generation knowledge graph, from various OWL input files.\n\n   Usage: multi_owl_to_json_kg.py <categoriesFile.yaml> <curiesToURILALFile>\n                                  <owlLoadInventoryFile.yaml> <outputFile>\n   (note: outputFile can end in .json or in .gz; if the latter, it will be written as a gzipped file;\n   but using the gzip options for input or output seems to significantly increase transient memory\n   usage)\n'''\n\n__author__ = 'Stephen Ramsey'\n__copyright__ = 'Oregon State University'\n__credits__ = ['Stephen Ramsey']\n__license__ = 'MIT'\n__version__ = '0.1.0'\n__maintainer__ = ''\n__email__ = ''\n__status__ = 'Prototype'\n\n\nimport argparse\nimport errno\nimport functools\nimport hashlib\nimport kg2_util\nimport ontobio\nimport os.path\nimport pickle\nimport posixpath\nimport prefixcommons\nimport re\nimport shutil\nimport subprocess\nimport sys\nimport tempfile\nimport urllib.parse\nimport urllib.request\n\n\n# -------------- define globals here ---------------\n\nREGEX_ENSEMBL = re.compile('ENS[A-Z]{0,3}([PG])[0-9]{11}')\nREGEX_YEAR = re.compile('([12][90][0-9]{2})')\nREGEX_YEAR_MONTH_DAY = re.compile('([12][90][0-9]{2})_([0-9]{1,2})_([0-9]{1,2})')\nREGEX_MONTH_YEAR = re.compile('([0-9]{1,2})_[12][90][0-9]{2}')\nREGEX_YEAR_MONTH = re.compile('[12][90][0-9]{2}_([0-9]{1,2})')\nREGEX_UMLS_CURIE = re.compile('UMLS:([^/]+)/(.*)')\nREGEX_PUBLICATIONS = re.compile(r'((?:(?:PMID)|(?:ISBN)):\\d+)')\nREGEX_PURL = re.compile('http://purl.obolibrary.org/obo/([^_]+)_(.*)')\nREGEX_IDORG = re.compile('https://identifiers.org/umls/([^/]+)/(.*)')\nREGEX_XREF_END_DESCRIP = re.compile(r'.*\\[([^\\]]+)\\]$')\n\nCUI_BASE_IRI = 'https://identifiers.org/umls/cui'\nIRI_OBO_XREF = 'http://purl.org/obo/owl/oboFormat#oboFormat_xref'\nCURIE_OBO_XREF = 'oboFormat:xref'\nOWL_BASE_CLASS = 'owl:Thing'\nOWL_NOTHING = 'owl:Nothing'\nMYSTERIOUS_BASE_NODE_ID_TO_FILTER = '_:genid'\nCUI_PREFIX = 'CUI'\nENSEMBL_LETTER_TO_CATEGORY = {'P': 'protein',\n                              'G': 'gene',\n                              'T': 'transcript'}\n\n\n# -------------- subroutines with side-effects go here ------------------\n\n\ndef delete_ontobio_cachier_caches():\n    kg2_util.purge(\"~/.cachier\", \".ontobio*\")\n    kg2_util.purge(\"~/.cachier\", \".prefixcommons*\")\n\n\n# this function is needed due to an issue with caching in Ontobio; see this GitHub issue:\n#     https://github.com/biolink/ontobio/issues/301\ndef delete_ontobio_cache_json(file_name: str):\n    file_name_hash = hashlib.sha256(file_name.encode()).hexdigest()\n    temp_file_path = os.path.join(\"/tmp\", file_name_hash)\n    if os.path.exists(temp_file_path):\n        try:\n            kg2_util.log_message(message=\"Deleting ontobio JSON cache file: \" + temp_file_path)\n            os.remove(temp_file_path)\n        except OSError as e:\n            if e.errno == errno.ENOENT:\n                kg2_util.log_message(message=\"Error deleting ontobio JSON cache file: \" + temp_file_path)\n            else:\n                raise e\n\n\n# this function will load the ontology object from a pickle file (if it exists) or\n# it will create the ontology object by parsing the OWL-XML ontology file\ndef make_ontology_from_local_file(file_name: str):\n    file_name_without_ext = os.path.splitext(file_name)[0]\n    file_name_with_pickle_ext = file_name_without_ext + \".pickle\"\n    if not os.path.isfile(file_name_with_pickle_ext):\n        # the ontology hsa not been saved as a pickle file, so we need to load it from a text file\n        if not file_name.endswith('.json'):\n            temp_file_name = tempfile.mkstemp(prefix=kg2_util.TEMP_FILE_PREFIX + '-')[1] + '.json'\n            size = os.path.getsize(file_name)\n            kg2_util.log_message(message=\"Reading ontology file: \" + file_name + \"; size: \" + \"{0:.2f}\".format(size/1024) + \" KiB\",\n                                 ontology_name=None)\n            cp = subprocess.run(['owltools', file_name, '-o', '-f', 'json', temp_file_name])\n            # robot commented out because it is giving a NullPointerException on umls_semantictypes.owl\n            # Once robot no longer gives a NullPointerException, we can use it like this:\n            #        cp = subprocess.run(['robot', 'convert', '--input', file_name, '--output', temp_file_name])\n            if cp.stdout is not None:\n                kg2_util.log_message(message=\"OWL convert result: \" + cp.stdout, ontology_name=None, output_stream=sys.stdout)\n            if cp.stderr is not None:\n                kg2_util.log_message(message=\"OWL convert result: \" + cp.stderr, ontology_name=None, output_stream=sys.stderr)\n            assert cp.returncode == 0\n            json_file = file_name_without_ext + \".json\"\n            shutil.move(temp_file_name, json_file)\n        else:\n            json_file = file_name\n        size = os.path.getsize(json_file)\n        kg2_util.log_message(message=\"Reading ontology JSON file: \" + json_file + \"; size: \" + \"{0:.2f}\".format(size/1024) + \" KiB\",\n                             ontology_name=None)\n\n        ont_return = ontobio.ontol_factory.OntologyFactory().create(json_file, ignore_cache=True)\n    else:\n        size = os.path.getsize(file_name_with_pickle_ext)\n        kg2_util.log_message(\"Reading ontology file: \" + file_name_with_pickle_ext + \"; size: \" + \"{0:.2f}\".format(size/1024) + \" KiB\", ontology_name=None)\n        ont_return = pickle.load(open(file_name_with_pickle_ext, \"rb\"))\n    return ont_return\n\n\ndef load_owl_file_return_ontology_and_metadata(file_name: str,\n                                               download_url: str = None,\n                                               ontology_title: str = None):\n    ontology = make_ontology_from_local_file(file_name)\n    file_last_modified_timestamp = kg2_util.format_timestamp(kg2_util.get_file_last_modified_timestamp(file_name))\n    print(\"file: \" + file_name + \"; last modified: \" + file_last_modified_timestamp)\n    ont_version = ontology.meta.get('version', None)\n    bpv = ontology.meta.get('basicPropertyValues', None)\n    title = ontology_title\n    description = None\n    umls_sver = None\n    if bpv is not None:\n        for bpv_dict in bpv:\n            pred = bpv_dict['pred']\n            value = bpv_dict['val']\n            if 'description' in pred:\n                description = value\n            elif 'title' in pred:\n                if title is None:\n                    title = value\n            elif 'umls/sver' in pred:\n                ont_version = value\n                umls_sver = value\n    if ont_version is None:\n        ont_version = 'downloaded:' + file_last_modified_timestamp\n    ontology_id = None\n    if download_url is not None:\n        ontology_id = download_url\n    else:\n        ontology_id = ontology.id\n        #    print(ontology_id)\n        if not ontology_id.startswith('http:') and not ontology_id.startswith('https:'):\n            ontology_id = os.path.basename(file_name)\n    metadata_dict = {'id': ontology_id,\n                     'handle': ontology.handle,\n                     'file': file_name,\n                     'file last modified timestamp': file_last_modified_timestamp,\n                     'version': ont_version,\n                     'title': title,\n                     'description': description,\n                     'umls-sver': umls_sver}\n#    print(metadata_dict)\n    return [ontology, metadata_dict]\n\n\ndef make_kg2(curies_to_categories: dict,\n             uri_to_curie_shortener: callable,\n             map_category_label_to_iri: callable,\n             owl_urls_and_files: tuple,\n             output_file_name: str,\n             test_mode: bool = False):\n\n    owl_file_information_dict_list = []\n\n    # for each OWL file (or URL for an OWL file) described in the YAML config file...\n    for ont_source_info_dict in owl_urls_and_files:\n        if ont_source_info_dict['download']:\n            # get the OWL file onto the local file system and get a full path to it\n            local_file_name = kg2_util.download_file_if_not_exist_locally(ont_source_info_dict['url'],\n                                                                          ont_source_info_dict['file'])\n        else:\n            local_file_name = ont_source_info_dict['file']\n            assert os.path.exists(ont_source_info_dict['file'])\n        # load the OWL file dadta into an ontobio.ontol.Ontology data structure and information dictionary\n        [ont, metadata_dict] = load_owl_file_return_ontology_and_metadata(local_file_name,\n                                                                          ont_source_info_dict['url'],\n                                                                          ont_source_info_dict['title'])\n        metadata_dict['ontology'] = ont\n        owl_file_information_dict_list.append(metadata_dict)\n\n    kg2_util.log_message('Calling make_nodes_dict_from_ontologies_list')\n\n    nodes_dict = make_nodes_dict_from_ontologies_list(owl_file_information_dict_list,\n                                                      curies_to_categories,\n                                                      uri_to_curie_shortener,\n                                                      map_category_label_to_iri)\n\n    kg2_util.log_message('Calling make_map_of_node_ontology_ids_to_curie_ids')\n\n    map_of_node_ontology_ids_to_curie_ids = make_map_of_node_ontology_ids_to_curie_ids(nodes_dict)\n\n    kg2_util.log_message('Calling get_rels_dict')\n\n    # get a dictionary of all relationships including xrefs as relationships\n    all_rels_dict = get_rels_dict(nodes_dict,\n                                  owl_file_information_dict_list,\n                                  uri_to_curie_shortener,\n                                  map_of_node_ontology_ids_to_curie_ids)\n\n    kg2_dict = dict()\n    kg2_dict['edges'] = [rel_dict for rel_dict in all_rels_dict.values()]\n    kg2_util.log_message('Number of edges: ' + str(len(kg2_dict['edges'])))\n    kg2_dict['nodes'] = list(nodes_dict.values())\n    kg2_util.log_message('Number of nodes: ' + str(len(kg2_dict['nodes'])))\n    del nodes_dict\n\n    # delete xrefs from all_nodes_dict\n    for node_dict in kg2_dict['nodes']:\n        del node_dict['xrefs']\n        del node_dict['ontology node ids']\n\n    kg2_util.log_message('Saving JSON file')\n    kg2_util.save_json(kg2_dict, output_file_name, test_mode)\n\n\ndef get_depth_of_ontology_term(ontology_node_id: str,\n                               ontology: ontobio.ontol.Ontology,\n                               ontology_node_ids_visited: set = None,\n                               recursion_level: int = 0):\n    if ontology_node_ids_visited is None:\n        ontology_node_ids_visited = set()\n    min_depth = None\n    parents = ontology.parents(ontology_node_id, ['subClassOf'])\n    if len(parents) == 0:\n        return recursion_level\n    for parent_ontology_node_id in parents:\n        if parent_ontology_node_id not in ontology_node_ids_visited:\n            ontology_node_ids_visited.add(parent_ontology_node_id)\n            depth = get_depth_of_ontology_term(parent_ontology_node_id,\n                                               ontology,\n                                               ontology_node_ids_visited,\n                                               recursion_level + 1)\n            if min_depth is None or depth < min_depth:\n                min_depth = depth\n    return min_depth\n\n\ndef get_biolink_category_for_node(ontology_node_id: str,\n                                  node_curie_id: str,\n                                  ontology: ontobio.ontol.Ontology,\n                                  curies_to_categories: dict,\n                                  uri_to_curie_shortener: callable,\n                                  ontology_node_ids_previously_seen: set,\n                                  get_node_id_of_node_with_category: bool):\n\n    # if we have already looked for a category for this node, return None\n    if ontology_node_id in ontology_node_ids_previously_seen:\n        return [None, None]\n\n    if ontology_node_id == OWL_NOTHING or node_curie_id is None:\n        return [None, None]\n\n    ontology_node_ids_previously_seen.add(ontology_node_id)\n\n    curie_prefix = get_prefix_from_curie_id(node_curie_id)\n\n    # Inelegant hack to ensure that TUI: nodes get mapped to \"semantic type\" while still enabling us\n    # to use get_biolink_category_for_node to determine the specific semantic type of a CUI: based on its\n    # TUI: record. Need to think about a more elegant way to do this. [SAR]\n    if curie_prefix == 'TUI' and ontology.id.endswith('/umls/STY/'):\n        return ['semantic type', None]\n\n    if get_node_id_of_node_with_category:\n        ret_ontology_node_id_of_node_with_category = ontology_node_id\n    else:\n        ret_ontology_node_id_of_node_with_category = None\n\n    curies_to_categories_prefixes = curies_to_categories['prefix-mappings']\n    ret_category = curies_to_categories_prefixes.get(curie_prefix, None)\n    if ret_category is None:\n        # need to walk the ontology hierarchy until we encounter a parent term with a defined biolink category\n        curies_to_categories_terms = curies_to_categories['term-mappings']\n        ret_category = curies_to_categories_terms.get(node_curie_id, None)\n        if ret_category is None:\n            for parent_ontology_node_id in ontology.parents(ontology_node_id, ['subClassOf']):\n                parent_node_curie_id = get_node_curie_id_from_ontology_node_id(parent_ontology_node_id,\n                                                                               ontology,\n                                                                               uri_to_curie_shortener)\n                try:\n                    [ret_category,\n                     ontology_node_id_of_node_with_category] = get_biolink_category_for_node(parent_ontology_node_id,\n                                                                                             parent_node_curie_id,\n                                                                                             ontology,\n                                                                                             curies_to_categories,\n                                                                                             uri_to_curie_shortener,\n                                                                                             ontology_node_ids_previously_seen,\n                                                                                             get_node_id_of_node_with_category)\n                    if get_node_id_of_node_with_category and ontology_node_id_of_node_with_category is not None:\n                        ret_ontology_node_id_of_node_with_category = ontology_node_id_of_node_with_category\n                except RecursionError:\n                    kg2_util.log_message(message=\"recursion error: \" + ontology_node_id,\n                                         ontology_name=ontology.id,\n                                         node_curie_id=node_curie_id,\n                                         output_stream=sys.stderr)\n                    assert False\n                if ret_category is not None:\n                    break\n    if ret_category is None:\n        if node_curie_id.startswith(kg2_util.CURIE_PREFIX_ENSEMBL + ':'):\n            curie_suffix = node_curie_id.replace(kg2_util.CURIE_PREFIX_ENSEMBL + ':', '')\n            ensembl_match = REGEX_ENSEMBL.match(curie_suffix)\n            if ensembl_match is not None:\n                ensembl_match_letter = ensembl_match[1]\n                ret_category = ENSEMBL_LETTER_TO_CATEGORY.get(ensembl_match_letter, None)\n                if ret_category is None:\n                    kg2_util.log_message(message=\"unrecognized Ensembl ID: \" + curie_suffix,\n                                         ontology_name=ontology.id,\n                                         node_curie_id=node_curie_id,\n                                         output_stream=sys.stderr)\n\n    return [ret_category, ret_ontology_node_id_of_node_with_category]\n\n\n# --------------- subroutines that have no side effects except logging printing ----------\n\n\ndef make_rel_key(subject_id: str,\n                 predicate_name: str,\n                 object_id: str,\n                 ontology_id: str = None):\n    key = subject_id + ';' + predicate_name + ';' + object_id\n    if ontology_id is not None:\n        key += ';' + ontology_id\n    return key\n\n\ndef parse_umls_sver_date(umls_sver: str):\n    umls_sver_match = REGEX_YEAR.match(umls_sver)\n    updated_date = None\n    if umls_sver_match is not None:\n        updated_date = umls_sver_match[0]\n    else:\n        umls_sver_match = REGEX_YEAR_MONTH_DAY.match(umls_sver)\n        if umls_sver_match is not None:\n            updated_date = umls_sver_match[0] + '-' + ('%0.2d' % int(umls_sver_match[1])) + '-' + ('%0.2d' % int(umls_sver_match[2]))\n        else:\n            umls_sver_match = REGEX_MONTH_YEAR.match(umls_sver)\n            if umls_sver_match is not None:\n                updated_date = umls_sver_match[1] + '-' + ('%0.2d' % int(umls_sver_match[0]))\n            else:\n                umls_sver_match = REGEX_YEAR_MONTH.match(umls_sver)\n                if umls_sver_match is not None:\n                    updated_date = umls_sver_match[0] + ('%0.2d' % int(umls_sver_match[1]))\n    return updated_date\n\n\ndef make_nodes_dict_from_ontologies_list(ontology_info_list: list,\n                                         curies_to_categories: dict,\n                                         uri_to_curie_shortener: callable,\n                                         category_label_to_iri_mapper: callable):\n    ret_dict = dict()\n    ontologies_iris_to_curies = dict()\n\n    for ontology_info_dict in ontology_info_list:\n        ontology = ontology_info_dict['ontology']\n        iri_of_ontology = ontology_info_dict['id']\n        assert iri_of_ontology is not None\n\n        ontology_curie_id = uri_to_curie_shortener(iri_of_ontology)\n        if ontology_curie_id is None or len(ontology_curie_id) == 0:\n            ontology_curie_id = iri_of_ontology\n        umls_sver = ontology_info_dict.get('umls-sver', None)\n        updated_date = None\n        if umls_sver is not None:\n            # if you can, parse sver string into a date string\n            updated_date = parse_umls_sver_date(umls_sver)\n        if updated_date is None:\n            updated_date = ontology_info_dict['file last modified timestamp']\n\n        ontology_node = kg2_util.make_node(ontology_curie_id,\n                                           iri_of_ontology,\n                                           ontology_info_dict['title'],\n                                           'data source',\n                                           updated_date,\n                                           iri_of_ontology)\n        ontology_node['description'] = ontology_info_dict['description']\n        ontology_node['ontology node ids'] = [iri_of_ontology]\n        ontology_node['xrefs'] = []\n        ret_dict[ontology_curie_id] = ontology_node\n\n        ontologies_iris_to_curies[iri_of_ontology] = ontology_curie_id\n\n        for ontology_node_id in ontology.nodes():\n            onto_node_dict = ontology.node(ontology_node_id)\n            assert onto_node_dict is not None\n\n            if ontology_node_id.startswith(MYSTERIOUS_BASE_NODE_ID_TO_FILTER):\n                continue\n\n            node_curie_id = get_node_curie_id_from_ontology_node_id(ontology_node_id,\n                                                                    ontology,\n                                                                    uri_to_curie_shortener)\n            assert not node_curie_id.startswith('UMLS:C')  # :DEBUG:\n\n            iri = onto_node_dict.get('id', None)\n            if iri is None:\n                iri = ontology_node_id\n\n            # Ensure all CUI nodes use a 'umls/cui' IRI (part of fix for #565)\n            if is_cui_id(node_curie_id):\n                iri = CUI_BASE_IRI + '/' + get_local_id_from_curie_id(node_curie_id)\n\n            if not iri.startswith('http:') and not iri.startswith('https:'):\n                iri = prefixcommons.expand_uri(iri)\n\n            if node_curie_id.startswith('NCBIGene:') or node_curie_id.startswith('HGNC:'):\n                iri = prefixcommons.expand_uri(node_curie_id)\n\n            generated_iri = prefixcommons.expand_uri(node_curie_id)\n            if generated_iri != node_curie_id:\n                if (generated_iri.startswith('http:') or generated_iri.startswith('https:')) and \\\n                   generated_iri != iri:\n                    iri = generated_iri\n\n            node_name = onto_node_dict.get('label', None)\n            node_full_name = None\n\n            [node_category_label,\n             ontology_id_of_node_with_category] = get_biolink_category_for_node(ontology_node_id,\n                                                                                node_curie_id,\n                                                                                ontology,\n                                                                                curies_to_categories,\n                                                                                uri_to_curie_shortener,\n                                                                                set(), False)\n\n            node_deprecated = False\n            node_description = None\n            node_creation_date = None\n            node_update_date = None\n            node_replaced_by_curie = None\n            node_full_name = None\n            node_publications = set()\n            node_synonyms = set()\n            node_xrefs = set()\n            node_tui = None\n            node_has_cui = False\n            node_tui_category_label = None\n\n            node_meta = onto_node_dict.get('meta', None)\n            if node_meta is not None:\n                node_deprecated = node_meta.get('deprecated', False)\n                node_definition = node_meta.get('definition', None)\n                if node_definition is not None:\n                    node_description = node_definition['val']\n                    if node_description.startswith('OBSOLETE:') or node_description.startswith('Obsolete.'):\n                        continue\n\n                    node_definition_xrefs = node_definition.get('xrefs', None)\n                    if node_definition_xrefs is not None:\n                        assert type(node_definition_xrefs) == list\n                        for xref in node_definition_xrefs:\n                            xref_pub = xref_as_a_publication(xref)\n                            if xref_pub is not None:\n                                node_publications.add(xref_pub)\n\n                node_synonyms_list = node_meta.get('synonyms', None)\n                if node_synonyms_list is not None:\n                    for syn_dict in node_synonyms_list:\n                        syn_pred = syn_dict['pred']\n                        if syn_pred == 'hasExactSynonym':\n                            node_synonyms.add(syn_dict['val'])\n                            syn_xrefs = syn_dict['xrefs']\n                            if len(syn_xrefs) > 0:\n                                for syn_xref in syn_xrefs:\n                                    syn_xref_pub = xref_as_a_publication(syn_xref)\n                                    if syn_xref_pub is not None:\n                                        node_publications.add(syn_xref_pub)\n\n                node_xrefs_list = node_meta.get('xrefs', None)\n                if node_xrefs_list is not None:\n                    for xref_dict in node_xrefs_list:\n                        xref_curie = xref_dict['val']\n                        if xref_curie.startswith('MESH:'):\n                            xref_curie = xref_curie.replace('MESH:', 'MSH:')\n                        elif xref_curie.startswith('UMLS:C'):\n                            xref_curie = CUI_PREFIX + ':' + xref_curie.split('UMLS:')[1]\n                        node_xrefs.add(xref_curie)\n                basic_property_values = node_meta.get('basicPropertyValues', None)\n                if basic_property_values is not None:\n                    node_tui_list = []\n                    for basic_property_value_dict in basic_property_values:\n                        bpv_pred = basic_property_value_dict['pred']\n                        bpv_pred_curie = uri_to_curie_shortener(bpv_pred)\n                        if bpv_pred_curie is None:\n                            bpv_pred_curie = bpv_pred\n                        bpv_val = basic_property_value_dict['val']\n                        if bpv_pred_curie in ['OIO:creation_date', 'dcterms:issued', 'HGNC:DATE_CREATED']:\n                            node_creation_date = bpv_val\n                        elif bpv_pred_curie == 'HGNC:DATE_LAST_MODIFIED':\n                            node_update_date = bpv_val\n                        elif bpv_pred_curie == 'IAL:0100001':\n                            assert node_deprecated\n                            node_replaced_by_uri = bpv_val\n                            node_replaced_by_curie = uri_to_curie_shortener(node_replaced_by_uri)\n                        elif bpv_pred_curie == 'UMLS:STY':  # STY_BASE_IRI:\n                            node_tui_list.append(bpv_val)\n                        elif bpv_pred_curie == 'skos:prefLabel':\n                            if not node_curie_id.startswith('HGNC:'):\n                                node_name = bpv_val\n                            else:\n                                node_full_name = bpv_val\n                                if node_name is None:\n                                    node_name = node_full_name\n                        elif bpv_pred_curie == 'skos:altLabel':\n                            node_synonyms.add(bpv_val)\n                        elif bpv_pred_curie == 'skos:definition':\n                            node_description = kg2_util.strip_html(bpv_val)\n                        elif bpv_pred_curie == 'HGNC:GENESYMBOL':\n                            node_name = bpv_val\n                            node_synonyms.add(bpv_val)\n                        elif bpv_pred_curie == 'UMLS:cui':\n                            node_has_cui = True\n                    if len(node_tui_list) == 1:\n                        node_tui = node_tui_list[0]\n                        node_tui_uri = posixpath.join('https://identifiers.org/umls/STY', node_tui)\n                        node_tui_curie = uri_to_curie_shortener(node_tui_uri)\n                        assert node_tui_curie is not None\n                        [node_tui_category_label,\n                         _] = get_biolink_category_for_node(node_tui_uri,\n                                                            node_tui_curie,\n                                                            ontology,\n                                                            curies_to_categories,\n                                                            uri_to_curie_shortener,\n                                                            set(), True)\n\n                node_comments = node_meta.get('comments', None)\n                if node_comments is not None:\n                    comments_str = 'COMMENTS: ' + (' // '.join(node_comments))\n                    if node_description is not None:\n                        node_description += ' // ' + comments_str\n                    else:\n                        node_description = comments_str\n\n            if node_category_label is None:\n                node_type = onto_node_dict.get('type', None)\n                if node_type is not None and node_type == 'PROPERTY':\n                    node_category_label = 'property'\n\n            if node_category_label is None:\n                if not node_deprecated:\n                    kg2_util.log_message(\"Node does not have a category\", ontology.id, node_curie_id, output_stream=sys.stderr)\n                    node_category_label = 'unknown category'\n                else:\n                    node_category_label = 'deprecated node'\n\n            if node_has_cui:\n                assert node_tui is not None or len(node_tui_list) > 0\n\n                if node_tui_category_label is None:\n                    node_tui_category_label = 'unknown category'\n                    if node_tui is not None:\n                        kg2_util.log_message(message='Node ' + ontology_node_id + ' has CUI whose TUI cannot be mapped to category: ' + node_tui)\n                    else:\n                        kg2_util.log_message(message='Node ' + ontology_node_id + ' has CUI with multiple associated TUIs: ' + ', '.join(node_tui_list))\n                else:\n                    if node_category_label is None:\n                        node_category_label = node_tui_category_label  # override the node category label if we have a TUI\n                node_tui_category_iri = category_label_to_iri_mapper(node_tui_category_label)\n            ontology_curie_id = ontologies_iris_to_curies[iri_of_ontology]\n            source_ontology_information = ret_dict.get(ontology_curie_id, None)\n            if source_ontology_information is None:\n                kg2_util.log_message(message=\"ontology IRI has no information dictionary available\",\n                                     ontology_name=iri_of_ontology,\n                                     output_stream=sys.stderr)\n                assert False\n            source_ontology_update_date = source_ontology_information['update date']\n            if node_update_date is None:\n                node_update_date = source_ontology_update_date\n\n            if node_description is not None:\n                node_description_xrefs_match = REGEX_XREF_END_DESCRIP.match(node_description)\n                if node_description_xrefs_match is not None:\n                    node_description_xrefs_str = node_description_xrefs_match[1]\n                    node_description_xrefs_list = node_description_xrefs_str.split(',')\n                    for node_description_xref_str in node_description_xrefs_list:\n                        node_description_xref_str = node_description_xref_str.strip()\n                        if ':' in node_description_xref_str:\n                            node_xrefs.add(node_description_xref_str)\n                node_description_pubs = REGEX_PUBLICATIONS.findall(node_description)\n                for pub_curie in node_description_pubs:\n                    node_publications.add(pub_curie)\n\n            # deal with node names that are ALLCAPS\n            if node_name is not None and node_name.isupper():\n                node_name = kg2_util.allcaps_to_only_first_letter_capitalized(node_name)\n\n            node_dict = kg2_util.make_node(node_curie_id,\n                                           iri,\n                                           node_name,\n                                           node_category_label,\n                                           node_update_date,\n                                           iri_of_ontology)\n            node_dict['full name'] = node_full_name\n            node_dict['description'] = node_description\n            node_dict['creation date'] = node_creation_date   # slot name is not biolink standard\n            node_dict['deprecated'] = node_deprecated         # slot name is not biolink standard\n            node_dict['replaced by'] = node_replaced_by_curie  # slot name is not biolink standard\n            node_dict['ontology node ids'] = [ontology_node_id]  # slot name is not biolink standard\n            node_dict['xrefs'] = list(node_xrefs)            # slot name is not biolink standard\n            node_dict['synonym'] = list(node_synonyms)       # slot name is not biolink standard\n            node_dict['publications'] = list(node_publications)\n\n            # check if we need to make a CUI node\n            if node_meta is not None and basic_property_values is not None:\n                for basic_property_value_dict in basic_property_values:\n                    bpv_pred = basic_property_value_dict['pred']\n                    bpv_pred_curie = uri_to_curie_shortener(bpv_pred)\n                    bpv_val = basic_property_value_dict['val']\n                    if bpv_pred_curie == 'UMLS:cui':   # CUI_BASE_IRI:\n                        cui_node_dict = dict(node_dict)\n                        cui_uri = bpv_pred + '/' + bpv_val\n                        cui_curie = uri_to_curie_shortener(cui_uri)\n                        assert cui_curie is not None\n                        assert not cui_curie.startswith('UMLS:C')  # :DEBUG:\n                        # Skip this CUI if it's identical to the ontology node itself (happens with files created\n                        # using 'load_on_cuis' - part of fix for issue #565)\n                        if get_local_id_from_curie_id(cui_curie) == get_local_id_from_curie_id(node_curie_id):\n                            continue\n                        cui_node_dict['id'] = cui_curie\n                        cui_node_dict['iri'] = cui_uri\n                        cui_node_dict['synonym'] = []\n                        cui_node_dict['category'] = node_tui_category_iri\n                        cui_node_dict['category label'] = node_tui_category_label.replace(' ', '_')\n                        cui_node_dict['ontology node ids'] = []\n                        cui_node_dict['provided by'] = CUI_BASE_IRI\n                        cui_node_dict['xrefs'] = []  # blanking the \"xrefs\" here is *vital* in order to avoid issue #395\n                        cui_node_dict_existing = ret_dict.get(cui_curie, None)\n                        if cui_node_dict_existing is not None:\n                            cui_node_dict = kg2_util.merge_two_dicts(cui_node_dict,\n                                                                     cui_node_dict_existing)\n                        ret_dict[cui_curie] = cui_node_dict\n                        node_dict_xrefs = node_dict['xrefs']\n                        node_dict_xrefs.append(cui_curie)\n                        node_dict['xrefs'] = list(set(node_dict_xrefs))\n                    elif bpv_pred_curie == 'HGNC:ENTREZGENE_ID':\n                        entrez_gene_id = bpv_val\n                        entrez_node_dict = dict(node_dict)\n                        entrez_curie = 'NCBIGene:' + entrez_gene_id\n                        entrez_node_dict['id'] = entrez_curie\n                        entrez_node_dict['iri'] = 'https://identifiers.org/NCBIGene/' + entrez_gene_id\n                        ret_dict[entrez_curie] = entrez_node_dict\n                        node_dict_xrefs = node_dict['xrefs']\n                        node_dict_xrefs.append(entrez_curie)\n                        node_dict['xrefs'] = list(set(node_dict_xrefs))\n            if node_curie_id in ret_dict:\n                node_dict = kg2_util.merge_two_dicts(ret_dict[node_curie_id], node_dict)\n            ret_dict[node_curie_id] = node_dict\n    return ret_dict\n\n\ndef get_rels_dict(nodes: dict,\n                  owl_file_information_dict_list: list,\n                  uri_to_curie_shortener: callable,\n                  map_of_node_ontology_ids_to_curie_ids: dict):\n    rels_dict = dict()\n\n    for owl_file_information_dict in owl_file_information_dict_list:\n        ontology = owl_file_information_dict['ontology']\n        ontology_id = owl_file_information_dict['id']\n        ont_graph = ontology.get_graph()\n        ontology_curie_id = map_of_node_ontology_ids_to_curie_ids[ontology_id]\n        for (object_id, subject_id, predicate_dict) in ont_graph.edges(data=True):\n            assert type(predicate_dict) == dict\n\n            ontology_node = nodes.get(ontology_curie_id, None)\n            if ontology_node is not None:\n                ontology_update_date = ontology_node['update date']\n\n            if subject_id == OWL_BASE_CLASS or object_id == OWL_BASE_CLASS:\n                continue\n\n            if subject_id.startswith(MYSTERIOUS_BASE_NODE_ID_TO_FILTER) or \\\n               object_id.startswith(MYSTERIOUS_BASE_NODE_ID_TO_FILTER):\n                continue\n\n            # subject_id and object_id are IDs from the original ontology objects; these may not\n            # always be the node curie IDs (e.g., for SNOMED terms). Need to map them\n            subject_curie_id = map_of_node_ontology_ids_to_curie_ids.get(subject_id, None)\n            if subject_curie_id is None:\n                kg2_util.log_message(message=\"ontology node ID has no curie ID in the map\",\n                                     ontology_name=ontology.id,\n                                     node_curie_id=subject_id,\n                                     output_stream=sys.stderr)\n                continue\n            object_curie_id = map_of_node_ontology_ids_to_curie_ids.get(object_id, None)\n            if object_curie_id is None:\n                kg2_util.log_message(message=\"ontology node ID has no curie ID in the map\",\n                                     ontology_name=ontology.id,\n                                     node_curie_id=object_id,\n                                     output_stream=sys.stderr)\n                continue\n\n            predicate_label = None\n            edge_pred_string = predicate_dict['pred']\n\n            if subject_curie_id.startswith('TUI:') and object_curie_id.startswith('TUI:') and edge_pred_string == 'subClassOf':\n                continue\n\n            if not edge_pred_string.startswith('http:') and not edge_pred_string.startswith('https'):\n                # edge_pred_string is not a URI; this is the most common case\n                if ':' not in edge_pred_string:\n                    # edge_pred_string is not a CURIE; this is the most common subcase\n                    if edge_pred_string != 'subClassOf':\n                        predicate_curie = 'owl:' + edge_pred_string\n                    else:\n                        predicate_curie = 'rdfs:subClassOf'\n                    predicate_label = kg2_util.convert_camel_case_to_snake_case(edge_pred_string)\n                else:\n                    # edge_pred_string is a CURIE\n                    predicate_curie = edge_pred_string\n                    predicate_node = nodes.get(predicate_curie, None)\n                    if predicate_node is not None:\n                        predicate_label = predicate_node['name']\n                    else:\n                        # predicate has no node object defined; just pull the label out of the CURIE\n                        if edge_pred_string.startswith('OBO:'):\n                            test_curie = edge_pred_string.replace('OBO:', '').replace('_', ':')\n                            predicate_node = nodes.get(test_curie, None)\n                            if predicate_node is None:\n                                predicate_label = edge_pred_string.split(':')[1].split('#')[-1]\n                            else:\n                                predicate_curie = test_curie\n                        else:\n                            predicate_label = edge_pred_string\n                predicate_iri = prefixcommons.expand_uri(predicate_curie)\n                predicate_curie_new = uri_to_curie_shortener(predicate_iri)\n                if predicate_curie_new is not None:\n                    predicate_curie = predicate_curie_new\n            else:\n                predicate_iri = edge_pred_string\n                predicate_curie = uri_to_curie_shortener(predicate_iri)\n\n            if predicate_curie is None:\n                kg2_util.log_message(message=\"predicate IRI has no CURIE: \" + predicate_iri,\n                                     ontology_name=ontology.id,\n                                     output_stream=sys.stderr)\n                continue\n\n            if subject_curie_id == object_curie_id and predicate_label == 'xref':\n                continue\n\n            if predicate_curie == 'UMLS:hasSTY':\n                subject_node = nodes[subject_curie_id]\n                object_node = nodes[object_curie_id]\n                subject_description = subject_node['description']\n                if subject_description is None:\n                    subject_description = ''\n                subject_node['description'] = '; '.join(list(filter(None, [subject_description,\n                                                                           'UMLS Semantic Type: ' + object_node['id']])))\n                continue\n\n            rel_key = make_rel_key(subject_curie_id, predicate_curie, object_curie_id, ontology_curie_id)\n\n            if predicate_label is None and ':' in predicate_curie:\n                pred_node = nodes.get(predicate_curie, None)\n                if pred_node is not None:\n                    predicate_label = pred_node['name']\n                    if predicate_label[0].isupper():\n                        predicate_label = predicate_label[0].lower() + predicate_label[1:]\n\n            assert predicate_label is not None\n            predicate_label = predicate_label.replace(' ', '_')\n            # Only tested on Food and Efo ontologies\n            predicate_label = kg2_util.convert_camel_case_to_snake_case(predicate_label)\n            if rels_dict.get(rel_key, None) is None:\n                edge = kg2_util.make_edge(subject_curie_id,\n                                          object_curie_id,\n                                          predicate_iri,\n                                          predicate_curie,\n                                          predicate_label,\n                                          ontology_id,\n                                          ontology_update_date)\n                rels_dict[rel_key] = edge\n        for node_id, node_dict in nodes.items():\n            xrefs = node_dict['xrefs']\n            if xrefs is not None:\n                for xref_node_id in xrefs:\n                    if xref_node_id in nodes and node_id != xref_node_id:\n                        provided_by = nodes[node_id]['provided by']\n                        key = make_rel_key(node_id, CURIE_OBO_XREF, xref_node_id, provided_by)\n                        if rels_dict.get(key, None) is None:\n                            edge = kg2_util.make_edge(node_id,\n                                                      xref_node_id,\n                                                      IRI_OBO_XREF,\n                                                      CURIE_OBO_XREF,\n                                                      'xref',\n                                                      provided_by,\n                                                      ontology_update_date)\n                            rels_dict[key] = edge\n\n    return rels_dict\n\n\ndef get_node_curie_id_from_ontology_node_id(ontology_node_id: str,\n                                            ontology: ontobio.ontol.Ontology,\n                                            uri_to_curie_shortener: callable):\n    node_curie_id = None\n    if not ontology_node_id.startswith('http:') and not ontology_node_id.startswith('https:'):\n        if not ontology_node_id.startswith('OBO:'):\n            if not ontology_node_id.startswith('UMLS:C'):\n                node_curie_id = ontology_node_id\n            else:\n                node_curie_id = CUI_PREFIX + ':' + ontology_node_id.split('UMLS:')[1]\n        else:\n            node_curie_id = uri_to_curie_shortener(prefixcommons.expand_uri(ontology_node_id))\n    else:\n        node_curie_id = uri_to_curie_shortener(ontology_node_id)\n        if node_curie_id is None:\n            kg2_util.log_message(message=\"could not shorten this IRI to a CURIE\",\n                                 ontology_name=ontology.id,\n                                 node_curie_id=ontology_node_id,\n                                 output_stream=sys.stderr)\n            node_curie_id = ontology_node_id\n\n    # Ensure that all CUI CURIE IDs use the \"CUI:\" prefix (part of fix for issue #565)\n    if is_cui_id(node_curie_id) and get_prefix_from_curie_id(node_curie_id) != CUI_PREFIX:\n        node_curie_id = CUI_PREFIX + \":\" + get_local_id_from_curie_id(node_curie_id)\n\n    return node_curie_id\n\n# --------------- pure functions here -------------------\n\n\ndef shorten_iri_to_curie(iri: str, curie_to_iri_map: list = []):\n    if iri.startswith('owl:') or iri.startswith('OIO:'):\n        return iri\n    if \"/GO/GO%3A\" in iri:  # hack for fixing issue #410\n        iri = iri.replace(\"/GO/GO%3A\", \"/GO/\")\n    if \"/HPO/HP%3A\" in iri:  # hack for fixing issue #665\n        iri = iri.replace(\"/HPO/HP%3A\", \"/HP/\")\n    curie_list = prefixcommons.contract_uri(iri,\n                                            curie_to_iri_map)\n    assert len(curie_list) in [0, 1]\n    if len(curie_list) == 1:\n        curie_id = curie_list[0]\n    else:\n        curie_id = None\n    if curie_id is not None:\n        # deal with IRIs like 'https://identifiers.org/umls/ATC/L01AX02' which get converted to CURIE 'UMLS:ATC/L01AX02'\n        umls_match = REGEX_UMLS_CURIE.match(curie_id)\n        if umls_match is not None:\n            curie_id = umls_match[1] + ':' + umls_match[2]\n    return curie_id\n\n\ndef is_ignorable_ontology_term(iri: str):\n    parsed_iri = urllib.parse.urlparse(iri)\n    iri_netloc = parsed_iri.netloc\n    iri_path = parsed_iri.path\n    return iri_netloc in ('example.com', 'usefulinc.com') or iri_path.startswith('/ontology/provisional')\n\n\ndef make_uri_to_curie_shortener(curie_to_iri_map: list = []):\n    return lambda iri: shorten_iri_to_curie(iri, curie_to_iri_map)\n\n\ndef get_prefix_from_curie_id(curie_id: str):\n    assert ':' in curie_id\n    return curie_id.split(':')[0]\n\n\ndef get_local_id_from_curie_id(curie_id: str):\n    \"\"\"\n    This function returns the local ID from a CURIE ID, where a CURIE ID consists of \"<Prefix>:<Local ID>\".\n    For example, the function would return \"C3540330\" for CURIE ID \"CUI:C3540330\".\n    \"\"\"\n    assert ':' in curie_id\n    return curie_id.split(':')[1]\n\n\ndef is_cui_id(curie_id: str):\n    \"\"\"\n    This function determines if the local ID in a CURIE ID is a CUI, regardless of the CURIE prefix used.\n    For example, the function would return True for CURIE ID \"MTH:C1527116\".\n    \"\"\"\n    local_id = get_local_id_from_curie_id(curie_id)\n    # CUIs consist of the letter 'C' followed by 7 numbers\n    return True if local_id.startswith('C') and len(local_id) == 8 and local_id[1:].isdigit() else False\n\n\ndef make_map_of_node_ontology_ids_to_curie_ids(nodes: dict):\n    ret_dict = dict()\n    for curie_id, node_dict in nodes.items():\n        ontology_node_ids = node_dict['ontology node ids']\n        assert curie_id not in ret_dict\n        assert ontology_node_ids is not None\n        assert type(ontology_node_ids) == list\n        for ontology_node_id in ontology_node_ids:\n            ret_dict[ontology_node_id] = curie_id\n    return ret_dict\n\n\ndef xref_as_a_publication(xref: str):\n    ret_xref = None\n    if xref.upper().startswith('PMID:') or xref.upper().startswith('ISBN:'):\n        ret_xref = xref.upper()\n    elif xref.startswith('https://') or xref.startswith('http://'):\n        ret_xref = xref\n    return ret_xref\n\n\ndef make_arg_parser():\n    arg_parser = argparse.ArgumentParser(description='multi_owl_to_json_kg.py: builds the KG2 knowledge graph for the RTX system')\n    arg_parser.add_argument('--test', dest='test', action=\"store_true\", default=False)\n    arg_parser.add_argument('categoriesFile', type=str)\n    arg_parser.add_argument('curiesToURILALFile', type=str)\n    arg_parser.add_argument('owlLoadInventoryFile', type=str)\n    arg_parser.add_argument('outputFile', type=str)\n    return arg_parser\n\n\n# --------------- main starts here -------------------\n\nif __name__ == '__main__':\n    delete_ontobio_cachier_caches()\n    args = make_arg_parser().parse_args()\n    curies_to_categories_file_name = args.categoriesFile\n    curies_to_uri_lal_file_name = args.curiesToURILALFile\n    owl_load_inventory_file = args.owlLoadInventoryFile\n    output_file = args.outputFile\n    test_mode = args.test\n    curies_to_categories = kg2_util.safe_load_yaml_from_string(kg2_util.read_file_to_string(curies_to_categories_file_name))\n    curies_to_uri_map = kg2_util.make_curies_to_uri_map(curies_to_uri_lal_file_name)\n    uri_to_curie_shortener = make_uri_to_curie_shortener(curies_to_uri_map)\n    map_category_label_to_iri = functools.partial(kg2_util.convert_biolink_category_to_iri,\n                                                  biolink_category_base_iri=kg2_util.BIOLINK_CATEGORY_BASE_IRI)\n\n    owl_urls_and_files = tuple(kg2_util.safe_load_yaml_from_string(kg2_util.read_file_to_string(owl_load_inventory_file)))\n\n    make_kg2(curies_to_categories,\n             uri_to_curie_shortener,\n             map_category_label_to_iri,\n             owl_urls_and_files,\n             output_file,\n             test_mode)\n","sub_path":"code/kg2/multi_owl_to_json_kg.py","file_name":"multi_owl_to_json_kg.py","file_ext":"py","file_size_in_byte":48511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"396663416","text":"from flask import Blueprint, render_template, redirect, request\nfrom jenkins import Jenkins, EMPTY_CONFIG_XML\nfrom time import sleep\n\ncon = Jenkins('http://localhost:8080/',\n                username='brunomendes',\n                password='4linux')\n\njenkins = Blueprint('jenkins', __name__, url_prefix='/jenkins')\n\n@jenkins.route('')\ndef index():\n    # print(con.get_all_jobs()[0])\n    return render_template('jenkins.html', jobs=con.get_all_jobs())\n\n@jenkins.route('/build/<string:name>')\ndef build_job(name):\n    con.build_job(name)\n    sleep(10)\n    return redirect('/jenkins')\n\n@jenkins.route('/update/<string:name>', methods=['GET', 'POST'])\ndef update_job(name):\n    if request.method == 'GET':\n        return render_template('jenkins_update.html', \n                                job_name=name, xml=con.get_job_config(name))\n    elif request.method == 'POST':\n        # print(request.form)\n        print('action')\n        xml = request.form['xml']\n        con.reconfig_job(name, xml)\n        return redirect('/jenkins')","sub_path":"python-devops/pjenkins/bjenkins.py","file_name":"bjenkins.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"387965767","text":"import pygame\nfrom pygame.locals import *\nfrom random import randint\nimport time\nimport lib.creature as cre\nimport lib.gui_and_inputs as gui\n\n\nSLEEP = .09\n\n\n# Grid\ndef create_grid(width, height, colors):\n    column = lambda: [colors['grey']() for _ in range(width)]\n    rows = [column() for _ in range(height)]\n    return rows\n\n\ndef move_to_pos(grid, row, column, color):\n    grid[row][column] = color\n    return grid\n\n\ndef _make_new_position_but_stay_in_bounds(row, column, direction, grid):\n    new_pos = [row, column]\n    if row > 0 and direction == 'up':\n        new_pos = [row-1, column]\n    elif row < (len(grid) - 1) and direction == 'down':\n        new_pos = [row+1, column]\n    elif column > 0 and direction == 'left':\n        new_pos = [row, column-1]\n    elif column < (len(grid[0]) - 1) and direction == 'right':\n        new_pos = [row, column+1]\n    return new_pos\n\n\ndef move_creature(creature, grid, color_map):\n    row = creature['row']\n    column = creature['column']\n    direction = cre._ask_creature_where_to_move_to(creature)\n    creature['facing'] = direction\n    grid[row][column] = color_map['grey']()\n    new_pos = _make_new_position_but_stay_in_bounds(row, column, direction, grid)\n    if new_pos[0] != creature['row']:\n        creature['moved'] = True\n    creature['row'] = new_pos[0]\n    creature['column'] = new_pos[1]\n    grid = move_to_pos(grid, new_pos[0], new_pos[1], creature['color'])\n    return grid, creature\n\n\ndef _create_default_color_map():\n    blue = lambda: (0, 128, 255)\n    grey = lambda: (128, randint(90, 130), 128)\n    yellow = lambda: (200, randint(0, 130), 255)\n    return {'blue': blue, 'grey': grey, 'yellow': yellow}\n\n\ndef update_creature_sight(creature, grid, foods_map):\n    row, column = creature['row']-1, creature['column']-1\n    what_it_can_see = [\n        ['|', '|', '|'],\n        ['|', 'I', '|'],\n        ['|', '|', '|'],\n    ]\n    for x in range(3):\n        for y in range(3):\n            if not (0 <= (row + x) <= len(grid)):\n                continue\n            if not (0 <= (column + y) <= len(grid)):\n                continue\n            if (row + x, column + y) in foods_map:\n                target = 'f'\n            else:\n                target = 'o'\n            what_it_can_see[x][y] = target\n    what_it_can_see[1][1] = 'I'\n    creature['what_it_can_see'] = what_it_can_see\n    return creature\n\n\ndef make_one_food(grid):\n    return cre.create_creature(randint(0, len(grid)-1), randint(0, len(grid[0])-1))\n\n\ndef make_foods_map(grid):\n    foods_map = {}\n    for x in range(len(grid)):\n        food = make_one_food(grid)\n        foods_map[(food['row'], food['column'])] = food\n    return foods_map\n\n\ndef set_food_onto_grid(grid, foods_map, color_map):\n    for food in foods_map.values():\n        grid = move_to_pos(grid, food['row'], food['column'], color_map['yellow']())\n    return grid\n\ndef main_loop():\n    app = gui.App()\n\n    color_map = _create_default_color_map()\n    grid = create_grid(app.width, app.height, color_map)\n    creature = cre.create_creature()\n    foods_map = make_foods_map(grid)\n    grid = set_food_onto_grid(grid, foods_map, color_map)\n\n    app = gui.handle_events(app)\n\n    while app._running:\n        gui.render(app, grid)\n        print()\n        print('what it can see')\n        for line in creature['what_it_can_see']:\n            print(line)\n\n        if not app._paused:\n            grid, creature = move_creature(creature, grid, color_map)\n            creature, foods_map, new_food = cre.handle_eating(creature, foods_map)\n            if new_food:\n                food = make_one_food(grid)\n                while (food['row'], food['column']) in foods_map:\n                    food = make_one_food(grid)\n                grid = move_to_pos(grid, food['row'], food['column'], color_map['yellow']())\n                foods_map[(food['row'], food['column'])] = food\n\n            if creature['moved']:\n                creature = cre.hurt_creature(creature)\n            if creature['hp'] < 1:\n                creature = cre.create_creature()\n            creature = update_creature_sight(creature, grid, foods_map)\n\n        if not app._running:\n            pygame.quit()\n\n        app = gui.handle_events(app)\n        time.sleep(SLEEP)\n","sub_path":"lib/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":4222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"640137343","text":"import time\nfrom typing import List\nfrom hither.job import Job\nimport hither2 as hi\nimport numpy as np\nfrom example_functions.test_numpy_serialization import test_numpy_serialization\n\ndef main():\n    # test1()\n    # test2()\n    test3()\n    # test4()\n    # test5()\n\ndef test1():\n    a = np.array([[1, 2, 3], [4, 5, 6 + 7j]])\n    b, c = test_numpy_serialization(x=a)\n    print(b)\n    print(c)\n\n    b, c = test_numpy_serialization(x=a)\n    print(b)\n    print(c)\n\n@hi.function('test_id', '0.1.0')\ndef test_id(x):\n    return x\n\ndef test2():\n    a = np.array([1, 2, 3, 4, 5])\n    with hi.Config(use_container=True, show_console=True):\n        j = hi.Job(test_numpy_serialization, dict(x=a))\n        j2 = hi.Job(test_id, dict(x=j))\n        print('*******************************************')\n        r = j2.wait()\n        b, c = r.return_value\n        print(b)\n        print(c)\n\ndef test3():\n    jh = hi.ParallelJobHandler(num_workers=4)\n    a = np.array([1, 2, 3, 4, 5])\n    with hi.Config(use_container=True, show_console=True, job_handler=jh):\n        jobs = [\n            hi.Job(test_numpy_serialization, dict(x=a*i, delay=3))\n            for i in range(4)\n        ]\n        j2 = hi.Job(test_id, {'x': jobs})\n        print('*******************************************')\n        cc = j2.wait().return_value\n        print(cc)\n\ndef test4():\n    a = np.array([1, 2, 3, 4, 5])\n    jc = hi.JobCache(feed_name='default-job-cache')\n    with hi.Config(use_container=True, job_cache=jc):\n        j = hi.Job(test_numpy_serialization, dict(x=a))\n        j2 = hi.Job(test_id, dict(x=j))\n        print('*******************************************')\n        r = j2.wait()\n        b, c = r.return_value\n        print(b)\n        print(c)\n\n@hi.function('multiply_arrays', '0.1.2')\ndef multiply_arrays(x: np.ndarray, y: np.ndarray, delay: float):\n    if delay > 0: time.sleep(delay)\n    return x * y\n\ndef test5():\n    jc = hi.JobCache(feed_name='default-job-cache')\n    jh = hi.ParallelJobHandler(num_workers=4)\n    jobs: List[hi.Job] = []\n    with hi.Config(job_cache=jc, job_handler=jh):\n        for i in range(8):\n            print(f'Creating job {i}')\n            j = hi.Job(multiply_arrays, dict(x=np.array([i, i]), y=np.array([2, 2]), delay=4))\n            jobs.append(j)\n    print('Waiting for jobs to complete')\n    hi.wait(None)\n    for j in jobs:\n        if j.status == 'finished':\n            print('RESULT:', j.status, j.result.return_value)\n        elif j.status == 'error':\n            print('ERROR', j.result.error)\n\nif __name__ == '__main__':\n    main()","sub_path":"hither2/examples/example1_pjh.py","file_name":"example1_pjh.py","file_ext":"py","file_size_in_byte":2548,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"253433426","text":"# If our interact service should be more complicate, we should use requests module.\n\nimport requests\n\n# Base URL being accessed\nurl = 'http://httpbin.org/post'\n# Dictionary of query parameters\nparms = {\n\t'name1': 'value1',\n\t'name2': 'value2'\n}\n\n# Extra headers\n\nheaders = {\n\t'User-agent': 'none/ofyourbusiness',\n\t'Spam': 'Eggs'\n}\n\nresp = requests.post(url, data=parms, headers=headers)\n\n# Decoded text returned by the request\n\ntext = resp.text\nprint (text)\n","sub_path":"11_net_programming/1_interact_with_http_server_as_client/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"118314359","text":"# encoding:utf-8\nfrom twisted.internet import protocol, reactor\nfrom twisted.python import log\nimport MySQLdb\nimport datetime\nimport struct\nfrom SqlOpration import SqlOprate \n\nTD_8HOUR = datetime.timedelta(0, 28800)\n\ndef dm2d(v):\n    \"\"\"ddmmmmmm --> d\n        要求 v 是整数\n        \"\"\"\n    return round(int(v/1000000) + float(v % 1000000) / 600000, 6)\n\n#16进制转换为2进制\ndef bin(number): \n    m = {'0':'0000', '1':'0001', '2':'0010', '3':'0011', \n        '4':'0100', '5':'0101', '6':'0110', '7':'0111', \n        '8':'1000', '9':'1001', 'A':'1010', 'B':'1011', \n        'C':'1100', 'D':'1101', 'E':'1110', 'F':'1111'} \n    s = number\n    return ''.join(m[x] for x in s)\n\nclass TianHeProtocol(protocol.Protocol):\n    timeOut=330\n    timeout_deferred=None\n    epidCurrent=None\n    \n    connected=False\n    def __init__(self, factory):\n        self.factory = factory\n    \n    def connectionMade(self):\n        log.msg(\"connection\")\n        self.factory.numProtocols = self.factory.numProtocols+1\n        log.msg(self.factory.numProtocols)\n        #设置超时\n        self.timeout_deferred = reactor.callLater(30, self.timeout)\n        connected=True\n    \n    def dataReceived(self, data):\n        \n        #重置超时设置\n        if self.timeout_deferred:  \n            self.timeout_deferred.cancel()  \n            self.timeout_deferred = reactor.callLater(self.timeOut, self.timeout)  \n        \n        print(data)\n        \n        if data.startswith(\"*\"):\n            textcmd, e, data = data.partition(\"#\")\n            \n            if e != \"#\":\n                return\n            \n            self.textCommandReceived(textcmd)\n        \n        elif data.startswith(\"$\"):\n            \n            data=data.split(\"$\")\n            self.binaryCommandReceived(data)\n        else:\n            pass\n    def timeout(self):\n        if self.epidCurrent:\n            log.msg(self.epidCurrent+\"--timeout\")\n            now = datetime.datetime.now().strftime(\"%y-%m-%d %H:%M:%S\")\n            value=(now,self.epidCurrent)\n            SqlOprate.sqlUpdate_epstatLost(self.factory.factoryKey,value)\n        self.transport.loseConnection()\n    \n    def connectionLost(self, reason):\n        log.msg(\"connectionLost\")\n        if self.factory.devepid.get(self.epidCurrent) == self:\n            del self.factory.devepid[self.epidCurrent]\n        \n        if self.timeout_deferred.active():\n            self.timeout_deferred.cancel()\n            del self.timeout_deferred\n        \n        self.factory.numProtocols = self.factory.numProtocols-1\n    \n    #解析＊开头的数据\n    def textCommandReceived(self, data):\n        log.msg(data)\n        data=data.split(\",\")\n        \n        \n        if data[2]=='V1':\n            l=float(data[5])\n            w=float(data[7])\n            self.epidCurrent=data[1]\n            \n            value=('NULL',data[1],'TianHe')\n            \n            SqlOprate.sqlInsert_ep(self.factory.factoryKey,value)\n            \n            \n            if data[2]=='V1' and self.connected:\n                self.connected=False\n                \n                last_epid=self.factory.devepid.get(self.epidCurrent)\n                \n                self.factory.devepid[self.epidCurrent]=self\n                \n                if last_epid and last_epid != self:\n                    last_epid.transport.loseConnection()\n            \n            dmy=data[11]\n            \n            day=int(dmy[0:2])\n            month=int(dmy[2:4])\n            year=int(dmy[4:])\n            \n            hms=data[3]\n            hour=int(hms[0:2])\n            minute=int(hms[2:4])\n            second=int(hms[4:])\n            year += 2000\n            t=datetime.datetime(year, month, day, hour, minute, second) + TD_8HOUR\n            t=t.strftime(\"%y-%m-%d %H:%M:%S\")\n            direction=data[10]\n            speed=data[9]\n            state=data[-1]\n            \n            if (data[5]!=\"\" and l!=0) and (data[7]!=\"\" and w!=0):\n                \n                str1=state[:2]\n                str2=state[2:4]\n                str3=state[4:6]\n                str4=state[6:]\n                str1=bin(str1)\n                str2=bin(str2)\n                str3=bin(str3)\n                str4=bin(str4)\n                discrubes=[]\n                alerType=[]\n                #红色按钮 维修报警\n                if str1[0]=='0':\n                    log.msg(\"warnning－－维修报警\")\n                    discrube='维修报警'\n                    tm=4\n                    alerType.append(tm)\n                    discrubes.append(discrube)\n                \n                #绿色按钮 油料报警\n                if str4[6]=='0':\n                    log.msg(\"warnning－－油料报警\")\n                    discrube='油料报警'\n                    tm=5\n                    alerType.append(tm)\n                    discrubes.append(discrube)\n                \n                #磁控开关\n                if str3[-1]=='0':\n                    log.msg(\"warnning－－磁控开关:开\")\n                    discrube='磁控开关:开'\n                    tm=8\n                    alerType.append(tm)\n                    discrubes.append(discrube)\n                \n                if str3[-1]=='1':\n                    log.msg(\"warnning－－磁控开关:关\")\n                    discrube='磁控开关:关'\n                    tm=9\n                    alerType.append(tm)\n                    discrubes.append(discrube)\n                        \n                weidu=float(data[5])*10000\n                weidu=dm2d(weidu)\n                jingdu=float(data[7])*10000\n                jingdu=dm2d(jingdu)\n                    \n                for d in range(0,len(discrubes)):\n                    now = datetime.datetime.now().strftime(\"%y-%m-%d %H:%M:%S\")\n                    value=('NULL',data[1],now,'NULL',alerType[d],discrubes[d],t,jingdu,weidu,direction,speed,0,state)\n                    SqlOprate.sqlInsert_alm(self.factory.factoryKey,value)\n                \n                now = datetime.datetime.now().strftime(\"%y-%m-%d %H:%M:%S\") \n                value=('NULL',data[1],now,t,jingdu,weidu,direction,speed,0,state)\n                value=value+value[2:]\n                SqlOprate.sqlInsert_epstat(self.factory.factoryKey,value)\n            else:\n                now = datetime.datetime.now().strftime(\"%y-%m-%d %H:%M:%S\")\n                value=('NULL',data[1],now,t,data[5],data[7],direction,speed,0,state)\n                value=value+value[2:]\n                SqlOprate.sqlInsert_epstat(self.factory.factoryKey,value)\n            \n    \n    #解析$开头的2进制数据\n    def binaryCommandReceived(self,data):\n        print(data)\n        insetOrUpate=True\n        end=len(data)\n        print(len(data))\n        # print(end)\n        for i in range(0,end):\n            #插入表gps\n            \n            dataAtIndex=data[i]\n            if len(dataAtIndex)==31:\n                value=self.binaryCommandTransport(dataAtIndex)\n                #print(value)\n                if value[4]!=0 and value[5]!=0:\n                    #print(value)\n                    SqlOprate.sqlInsert_gps(self.factory.factoryKey,value)\n                \n                if insetOrUpate:\n                    if len(data[end-i])==31:\n                        value=self.binaryCommandTransport(data[end-i])\n                        value=value+value[2:]\n                        SqlOprate.sqlUpdate_epstatConnecting(self.factory.factoryKey,value)\n                        insetOrUpate=False\n    \n    \n    def binaryCommandTransport(self,data):\n        (epid,eptime,epdate,weidu,bl0,jingdu,speedD,state,userAlarm,bl1,rNum)=struct.unpack('5s3s3s4sB5s3s4sBBB',data)\n        epid=epid.encode(\"hex\")\n        eptime=eptime.encode(\"hex\")\n        epdate=epdate.encode(\"hex\")\n        weidu=float(weidu.encode(\"hex\"))\n        #bl0=bl0.encode(\"hex\")\n        jingdu=jingdu.encode(\"hex\")  \n        speedD=speedD.encode(\"hex\")\n        direction=speedD[3:]\n        speedD=speedD[:3]\n        \n        state= state.encode(\"hex\").upper()\n        \n        weidu=dm2d(weidu)\n        jingdu=float(jingdu[:-1])\n        jingdu=dm2d(jingdu)\n        day=int(epdate[0:2])\n        month=int(epdate[2:4])\n        year=int(epdate[4:])\n        \n        hour=int(eptime[0:2])\n        minute=int(eptime[2:4])\n        second=int(eptime[4:])\n        year += 2000\n        now = datetime.datetime.now().strftime(\"%y-%m-%d %H:%M:%S\")\n        t=datetime.datetime(year, month, day, hour, minute, second) + TD_8HOUR\n        t=t.strftime(\"%y-%m-%d %H:%M:%S\")\n        #print(t)\n        #print(epid,eptime,epdate,weidu,bl0,jingdu,direction,speedD,state,userAlarm,bl1,rNum)\n        return ('NULL',epid,now,t,jingdu,weidu,direction,speedD,0,state)","sub_path":"TCPServer/Protocol/TianHeProtocol.py","file_name":"TianHeProtocol.py","file_ext":"py","file_size_in_byte":8694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"192610982","text":"#! /usr/lib/env python\n# -*- coding: utf-8 -*-\n\n\"\"\" \ncreate by kaka on 2016/8/1\n\"\"\"\nimport tornado.web\nimport json\n\nfrom prepool.utils import log as logging\nfrom prepool.utils import request\nfrom prepool.utils.options import get_options\nfrom prepool.utils import exception\noptions = get_options()\n\n__author__ = \"kaka_zhou@simpleware.com.cn\"\n\nLOG = logging.getLogger()\n\n\nclass BaseHandler(tornado.web.RequestHandler):\n    def __init__(self, application, request, **kwargs):\n        super(BaseHandler, self).__init__(application, request, **kwargs)\n        self.set_default_headers()\n        if self.request.method != 'OPTIONS':\n            user = self._auth(self.request)\n            if isinstance(user, exception.PrePoolException):\n                self.set_status(user.code)\n                self._transforms = []\n                self.write_json(msg=str(user), status_code=user.code)\n\n            LOG.debug(\"%s %s\" % (request.method, request.uri))\n\n    def _auth(self, request):\n        token = request.headers.get(\"X-Auth-Token\") or self.get_argument('token', False)\n        if not token:\n            return exception.RequiredParamNotExist(param_name=\"X-Auth-Token\")\n\n        return self._get_user_from_token(token=token)\n\n    def _get_user_from_token(self, token):\n        \"\"\"\n        Return object from keystone by token\n        \"\"\"\n        try:\n            session = request.sync_get_session()\n            url = options.auth_url.rsplit(\"/\", 2)[0]\n            headers = {\"X-Auth-Token\": session.id, \"X-Subject-Token\": token}\n            body_info = request.sync_request(url=url + '/v3/auth/tokens', method=\"GET\", headers=headers)\n            if not body_info.get(\"token\"):\n                return exception.NotAuth()\n        except Exception as e:\n            LOG.error(\"validate token error: %s\" % e)\n            return exception.NotAuth()\n\n    def set_default_headers(self):\n        self.set_header(\"Access-Control-Allow-Origin\", \"*\")\n        self.set_header(\"Access-Control-Allow-Methods\", \"GET, POST, PUT, DELETE\")\n        self.set_header(\"Access-Control-Allow-Headers\", \"X-Auth-Token, Content-type\")\n        self.set_header(\"Content-Type\", \"application/json\")\n\n    def del_default_headers(self):\n        self.clear_header(\"Access-Control-Allow-Origin\")\n        self.clear_header(\"Access-Control-Allow-Methods\")\n        self.clear_header(\"Access-Control-Allow-Headers\")\n        self.clear_header(\"Content-Type\")\n\n    def gen_http_error(self, status, msg):\n        self.del_default_headers()\n        self.set_status(status)\n        self.flush()\n        self.finish(str(msg))\n\n    def write_log(self, request_method, request_url, status_code):\n        LOG.info(\"a request is attempt to %s %s %s from %s\" % (\n            request_method, request_url, status_code, self.request.remote_ip))\n\n    def write_json(self, data=None, status_code=200, msg='success'):\n        self.finish(json.dumps({\n            'code': status_code,\n            'msg': msg,\n            'result': data\n        }))\n\n    def options(self, *args, **kwargs):\n        self.finish()\n\n\ndef main():\n    pass\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"prepool/utils/http_utils.py","file_name":"http_utils.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"62001625","text":"#!/usr/bin/env python3\n\"\"\"\nJeu de devinettes\n\nAuteur: Émile Boucher\n\"\"\"\n\nimport random\n\n# Constante\nMINIMUM = 1\nMAXIMUM = 100\nNOMBRE_ESSAI_MAX = 10\n\n\ndef main():\n    \"\"\"\n    Code princiapale du jeu de devinette\n    \"\"\"\n    print(\"Bonjour, je m'appelle Emilex 2020\")\n    nombre_devinable = random.randrange(MINIMUM, MAXIMUM + 1)\n    print(f\"J'ai choisi un nombre entier entre {MINIMUM} et {MAXIMUM}\")\n    print('Pouvez-vous le deviner ?')\n    reponse = 'OUI'\n    nombre_partie = 0\n    nombre_essais = 0\n    while True:\n        if reponse == 'OUI' or reponse == 'O':\n            print('')\n            nombre_essais += loop_principale(nombre_devinable)\n            nombre_partie += 1\n            print('Voulez-vous rejouer [O/N]?')\n        else:\n            print('Veuillez entrer OUI ou NON. (\"O\" ou \"N\")')\n        reponse = input().upper()\n        if reponse == 'NON' or reponse == 'N':\n            print(f'Nombre de parties jouées: {nombre_partie}')\n            print(f\"Nombre d'essais: {nombre_essais}\")\n            print(f\"Moyenne d'essais par partie: {nombre_essais / nombre_partie}\")\n            print('Au revoir!')\n            break\n    pass\n\n\ndef loop_principale(nombre_devinable):\n    \"\"\"\n    La fonction de jeu principale\n    :param nombre_devinable: Le nombre a deviner\n    \"\"\"\n    essai = 1\n    nombre_essayer = []\n    while True:\n        print(f'Essai {essai}: ', end=' ')\n        deviner = input()\n        sortie = cheat_code(deviner, essai, nombre_essayer, nombre_devinable)\n        if not sortie and validateur_format(deviner):\n            nombre_essayer.append(int(deviner))\n            essai += 1\n            if int(deviner) > int(nombre_devinable):\n                print('Votre nombre est trop grand...')\n            elif int(deviner) < int(nombre_devinable):\n                print('Votre nombre est trop petit...')\n            else:\n                print('Bravo, vous avez deviné le nombre')\n                print(f'Votre séquense gagnante est: {nombre_essayer}')\n                sortie = True\n        if sortie:\n            break\n        if essai > 10:\n            print(f'Désolé, vous avez échoué après {NOMBRE_ESSAI_MAX} tentatives')\n            print(f'Le nombre choisi était {nombre_devinable}')\n            break\n    return essai\n\n\ndef cheat_code(code_entrer, nb_essais, nb_essayer, devinable):\n    \"\"\"\n    Fonction qui détecte et gère les \"Cheat code\"\n    :param devinable: Le nombre a deviné\n    :param nb_essayer: Les nombres essayer\n    :param nb_essais: Nombre d'essais fait\n    :param code_entrer: Code entrer\n    :return: L'id du \"Cheat code\" (0 pour aucun)\n    \"\"\"\n    if code_entrer == 'PPP':\n        print(f'Désolé, vous avez échoué après {nb_essais} tentatives')\n        print(f'Le nombre choisi était {devinable}')\n        return True\n    elif code_entrer == 'GGG':\n        nb_essayer.append(code_entrer)\n        print('Bravo, vous avez deviné le nombre')\n        print(f'Votre séquense gagnante est: {nb_essayer}')\n        return True\n    return False\n\n\ndef validateur_format(string):\n    \"\"\"\n    Fonction pour validé le nombre\n    :param string: nombre a validé\n    :return: vrai = nombre valide\n    \"\"\"\n    erreur_msg = 'ERREUR: Entrez un nombre entier svp'\n    try:\n        nombre = int(string)\n        if isinstance(nombre, int):\n            return True\n    except ValueError:\n        print(erreur_msg)\n    return False\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"devinettes.py","file_name":"devinettes.py","file_ext":"py","file_size_in_byte":3424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"552681412","text":"import threading\nfrom wxpy import *\nbot = Bot()\ndef send_news():\n    try:\n        # my_friend = bot.friends().search(my_lady_name)[0]\n        # my_friend.send('(•‾̑⌣‾̑•)已经三个小时,休息一下啦')\n        # my_friend.send(get_news()[0])\n        # my_friend.send(get_news()[1])\n        bot.file_helper.send('test1')\n        t = threading.Timer(60,send_news)  #10800 是秒数\n        t.start()\n    except:\n        print('false')\nif __name__ == '__main__':\n\n    t = threading.Timer(60, send_news)  # 10800 是秒数\n    t.start()\n\n\n# import threading\n#\n# def func1():\n#     print('Do something')\n#     timer = threading.Timer(3, func1)\n#     timer.start()\n#\n# if __name__ == '__main__':\n#\n#     timer = threading.Timer(3, func1)\n#     timer.start()\n\n\n\n\n","sub_path":"情绪/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"452296497","text":"#Question A of Technical Test\r\n\r\ndef intersection_check(line1, line2):\r\n    #I will assume the two inputs are two arrays of length 2, with the first point being the \r\n    #left most point and the second point being the right most point of the line\r\n    if type(line1) != tuple or type(line2) != tuple:\r\n        raise TypeError(\"Please enter two tuples containing the start and end points of the lines\")\r\n    if len(line1) != 2 or len(line2) != 2:\r\n        raise ValueError(\"Ensure both tuples contain only 2 values\")\r\n\r\n    #First, the leftmost line will be determined. \r\n    #Checks if the rightmost part of the leftmost line is less than the leftmost part of the rightmost line\r\n    if line1[0] < line2[0]:\r\n        return line1[1] >= line2[0]\r\n    elif line2[0] < line1[0]:\r\n        return line2[1] >= line1[0]\r\n    else:\r\n        #If both lines' left point is the same, an intersection already exists \r\n        return True","sub_path":"questionA.py","file_name":"questionA.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"552573930","text":"import numpy as np\nimport loopy as lp\nimport pyopencl as cl\n\n\ndef softmax(queue, input_array):\n    '''\n    Compute the softmax of `input_array` (e^x_i) / sum(e^x_i for all i)self.\n    '''\n    exp_kernel = lp.make_kernel(\n      name=\"exp\",\n      domains=[\n        '{ [i]: 0<=i<n }',\n      ],\n      instructions=[\n        'out_exp[i] = z[i]',\n      ],\n      assumptions=\"n>0\")\n\n    sum_kernel = lp.make_kernel(\n      name=\"sum\",\n      domains=[\n        '{ [i]: 0<=i<n }',\n      ],\n      instructions=[\n        'out_sum = sum(i, exp[i])',\n      ],\n      assumptions='n>0')\n\n    softmax_divide_kernel = lp.make_kernel(\n      name=\"softmax_divide\",\n      domains=[\n        '{ [i]: 0<=i<n }',\n      ],\n      instructions=[\n        'out_final[i] = exp[i] / total',\n      ],\n      assumptions='n>0')\n    print(exp_kernel)\n    print(sum_kernel)\n    print(softmax_divide_kernel)\n    evt, (exp,) = exp_kernel(queue, z=input_array, E=2.7)\n    evt, (sum_exp,) = sum_kernel(queue, exp=exp)\n    evt, (softmax,) = softmax_divide_kernel(queue, exp=exp, total=sum_exp)\n\n    return softmax\n\nif __name__ == \"__main__\":\n    ctx = cl.create_some_context()\n    queue = cl.CommandQueue(ctx)\n    x = np.array([1, 2, 3])\n    y = softmax(queue, x)\n    print(\"-- x: %s\" % (x,))\n    print(\"-- y: %s\" % (y,))\n","sub_path":"examples/softmax.py","file_name":"softmax.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"506205216","text":"import math\nimport decor\n\npositionOeil = (0.0,1.6,5.0)\ndirectionVisee = -math.pi/2.0\n\ndef changeParametreCamera(newPositionOeil,newDirectionVisee):\n\tglobal directionVisee\n\tglobal positionOeil\n\tpositionOeil=newPositionOeil\n\tdirectionVisee=newDirectionVisee\n\t\ndef deg2rad(d):\n\treturn math.pi*d/180.0\n\ndef parametresCamera():\n\tex, ey,ez = positionOeil\n\tdx, dy, dz = math.cos(directionVisee), 0.0, math.sin(directionVisee)\n\t\n\tpointVise = ex + dx, ey+dy, ez+dz\n\n\treturn positionOeil, pointVise, (0.0,1.0,0.0)\n\ndef rotationCamera(angleDegre):\n\tglobal directionVisee\n\tangleRadian = deg2rad(angleDegre)\n\tdirectionVisee += angleRadian\n\ndef deplacementCamera(deplacement):\n\tglobal positionOeil\n\tex, ey, ez = positionOeil\n\tdx, dy, dz = math.cos(directionVisee), 0.0, math.sin(directionVisee)\n\tl = math.sqrt(dx*dx + dy*dy + dz*dz)\n\td1x, d1y,d1z = dx/l, dy/l, dz/l\n\tflag = decor.testDeplacement([ex+deplacement*d1x, ey+deplacement*d1y, ez+deplacement*d1z])\n\tif flag :\n\t\tpositionOeil = [ex+deplacement*d1x, ey+deplacement*d1y, ez+deplacement*d1z] \n\telse :\n\t\tpositionOeil = [ex, ey, ez]\n\t","sub_path":"move.py","file_name":"move.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"652727549","text":"import subprocess\nimport os\nimport sys\nfrom bs4 import UnicodeDammit\nimport time\n\ndef find_files(directory, input_token_file):\n    paths = []\n    for root, directories, files in os.walk(directory):\n        for filename in files:\n            filepath = os.path.join(root, filename)\n            paths.append(filepath)\n    return [path for path in paths if path.endswith(input_token_file)]\n\n\ndef save_tokens_from_original_lexicon(directory, input_token_file, target_file):\n    print(\"Cleaning........\")\n\n    if input_token_file != \"\":\n        input_tokens = []\n        with open(input_token_file, \"r\", encoding=\"utf-8\") as read_tokens:\n            for line in read_tokens:\n                input_tokens.append(line)\n\n\n    for fl in find_files(\"./\", input_token_file):\n        with open(fl, \"r\", encoding=\"utf-8\") as infile:\n            with open(target_file, \"w\", encoding=\"utf-8\") as outfile:  # open file to write\n\n                for index, line in enumerate(infile):\n                    # format of input file\n                    #########################################################################################\n                    #   Warning!                                                                            #\n                    #   The position of token(s) to be translated depends on the original lexicon format    #\n                                                                #\n                    #                                                                                       #\n                    #########################################################################################\n\n                    new_token = input_tokens[index]\n                    new_token = new_token.rstrip()\n\n                    # remove de, la and las\n                    if 'de ' in new_token or 'la ' in new_token or 'las ' in new_token:\n                        new_token = new_token[3:]\n\n\n                    # remove other multi tokens and write new token to file\n                    if len(new_token.split()) == 3:\n                        outfile.write(new_token.rstrip() + \"\\n\")  # write line to new file\n\n\ndef main():\n    # just a timer to benchmark the system\n    t0 = time.time()\n\n    # argument control / start the process\n    if len(sys.argv) != 3:\n        print(\"Usage: save_tokens_from_original_lexicon.py <input_token_file> <target_file>\")  # token position starts from 0!\n        print()\n        print(\"Usage: python3 bash.py NRC-Tokens.txt NRC-tokens_cleaned.txt\")\n        print()\n        sys.exit (1)\n    else:\n        save_tokens_from_original_lexicon(\"./\", sys.argv[1], sys.argv[2])\n\n    # output system runtime\n    test_time = time.time() - t0\n    print(\"Runtime: \", test_time)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"translated_lexicons_v1.0/scripts/clean_tokens/clean_tokens.py","file_name":"clean_tokens.py","file_ext":"py","file_size_in_byte":2761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"629704196","text":"from venues.models import AdjudicatorVenueConstraint, InstitutionVenueConstraint, TeamVenueConstraint\n\n\ndef _constraints_satisfied(constraints_dict, key, venue):\n    if key not in constraints_dict:\n        return True\n    constraints = constraints_dict[key]\n    return any(constraint.venue_group_id == venue.group_id for constraint in constraints)\n\n\ndef venue_conflicts_display(debates):\n    \"\"\"Returns a dict mapping elements (debates) in `debates` to a list of\n    strings of explaining unfulfilled venue constraints for participants that\n    debate. A venue constraint (or more precisely, a set of venue constraints\n    relating to a single participant) is \"unfulfilled\" if the relevant\n    participant had constraints and *none* of their constraints were met.\"\"\"\n\n    teamconstraints = {}\n    for constraint in TeamVenueConstraint.objects.filter(team__debateteam__debate__in=debates).distinct():\n        teamconstraints.setdefault(constraint.team_id, []).append(constraint)\n    instconstraints = {}\n    for constraint in InstitutionVenueConstraint.objects.filter(institution__team__debateteam__debate__in=debates).distinct():\n        instconstraints.setdefault(constraint.institution_id, []).append(constraint)\n    adjconstraints = {}\n    for constraint in AdjudicatorVenueConstraint.objects.filter(adjudicator__debateadjudicator__debate__in=debates).distinct():\n        adjconstraints.setdefault(constraint.adjudicator_id, []).append(constraint)\n\n    conflict_messages = {debate: [] for debate in debates}\n    for debate in debates:\n        venue = debate.venue\n        if venue is None:\n            continue\n\n        for team in debate.teams:\n            if not _constraints_satisfied(teamconstraints, team.id, venue):\n                conflict_messages[debate].append(\"Venue does not meet constraints of {}\".format(team.short_name))\n            if not _constraints_satisfied(instconstraints, team.institution_id, venue):\n                conflict_messages[debate].append(\"Venue does not meet constraints of institution {} ({})\".format(team.institution.code, team.short_name))\n\n        for adjudicator in debate.adjudicators.all():\n            if not _constraints_satisfied(adjconstraints, adjudicator.id, venue):\n                conflict_messages[debate].append(\"Venue does not meet constraints of {}\".format(adjudicator.name))\n\n    return conflict_messages\n","sub_path":"tabbycat/venues/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"437955352","text":"### START FUNCTION\n\ndef sq_cube(numbers):\n    # empty list to store the resulting list of lists\n    evenNum = [] \n    for n in numbers:\n\n        # rounding to the nearest integer\n        num = round(n) \n\n        # check if number is even\n        if num % 2 == 0: \n\n            # square and cube of a number\n            num_sq = num**2 \n            num_cb = num**3 \n\n            # append eveNum list with a list containing square and cube\n            evenNum.append([num_sq, num_cb]) \n    return evenNum\n    \n### END FUNCTION\nprint(sq_cube([1.1, 2.2, 3.3, 4.4, 5.5, 6.6, 7.7, 8.8, 9.9]))\n### END FUNCTION","sub_path":"cube_squares.py","file_name":"cube_squares.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"229120648","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n#     ||          ____  _ __\n#  +------+      / __ )(_) /_______________ _____  ___\n#  | 0xBC |     / __  / / __/ ___/ ___/ __ `/_  / / _ \\\n#  +------+    / /_/ / / /_/ /__/ /  / /_/ / / /_/  __/\n#   ||  ||    /_____/_/\\__/\\___/_/   \\__,_/ /___/\\___/\n#\n#  Copyright (C) 2019 Bitcraze AB\n#\n#  This program is free software; you can redistribute it and/or\n#  modify it under the terms of the GNU General Public License\n#  as published by the Free Software Foundation; either version 2\n#  of the License, or (at your option) any later version.\n#\n#  This program is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU General Public License for more details.\n#  You should have received a copy of the GNU General Public License\n#  along with this program; if not, write to the Free Software\n#  Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston,\n#  MA  02110-1301, USA.\n\"\"\"\nThis is modified version of https://github.com/bitcraze/crazyflie-lib-python/blob/master/examples/swarm/synchronizedSequence.py\n\nSimple example of a synchronized swarm choreography using the High level\ncommander.\nThe swarm takes off and flies a synchronous choreography before landing.\nThe take-of is relative to the start position but the Goto are absolute.\nThe sequence contains a list of commands to be executed at each step.\nThis example is intended to work with any absolute positioning system.\nIt aims at documenting how to use the High Level Commander together with\nthe Swarm class to achieve synchronous sequences.\n\"\"\"\nimport threading\nimport time\nfrom collections import namedtuple\nfrom queue import Queue\n\nimport cflib.crtp\nfrom cflib.crazyflie.log import LogConfig\nfrom cflib.crazyflie.swarm import CachedCfFactory\nfrom cflib.crazyflie.swarm import Swarm\n\nfrom cflib.crazyflie.syncLogger import SyncLogger\n\nimport os\n\n\n# Time for one step in second\nSTEP_TIME = 0.333\n\n# Possible commands, all times are in seconds\nTakeoff = namedtuple('Takeoff', ['height', 'time'])\nLand = namedtuple('Land', ['time'])\nGoto = namedtuple('Goto', ['x', 'y', 'z', 'time'])\n# RGB [0-255], Intensity [0.0-1.0]\nRing = namedtuple('Ring', ['r', 'g', 'b', 'intensity', 'time'])\n# Reserved for the control loop, do not use in sequence\nQuit = namedtuple('Quit', [])\n\nuris = [\n    'radio://0/70/2M/E7E7E7E7E6',\n    #'radio://0/70/2M/E7E7E7E7E2',\n    #'radio://0/70/2M/E7E7E7E7E3',\n    #'radio://0/70/2M/E7E7E7E7E4',\n    #'radio://0/70/2M/E7E7E7E7E5',\n    #'radio://0/70/2M/E7E7E7E7E6',\n    #'radio://0/70/2M/E7E7E7E7E7',\n    #'radio://0/80/2M/E7E7E7E7E1',\n    #'radio://0/80/2M/E7E7E7E7E2',\n    #'radio://0/80/2M/E7E7E7E7E3',\n    #'radio://0/80/2M/E7E7E7E7E4',\n    #'radio://0/80/2M/E7E7E7E7E5',\n    #'radio://0/80/2M/E7E7E7E7E6',\n    #'radio://0/80/2M/E7E7E7E7E7',\n]\n\nsequence_init = [\n    # Step, CF_id,  action\n    (0,    0,      Ring(0, 0, 0, 0.1, 0)),\n]\n\nsequence_1 = [\n    (0,    0,      Ring(255, 0, 0, 1, 0)),#RED\n    (1,    0,      Ring(0, 255, 0, 1, 0)),#GREEN\n    (2,    0,      Ring(0, 0, 255, 1, 0)),#BLUE\n    (3,    0,      Ring(0, 0, 0, 0, 0)),\n]\n\nsequence_2 = [\n    (0,    0,      Takeoff(1.5, 3)),\n]\n\nsequence_3 = [\n    (0, 0, Ring(255, 255, 255, 1, 0)),#WHITE\n    (0, 0, Takeoff(1.5, 3)),\n    (6, 0, Goto(0, 0.3,1.5 ,STEP_TIME)),\n    (7, 0, Goto(0.3, 0.3 ,1.5 ,STEP_TIME)),\n    (8, 0, Goto(0.3 ,0 ,1.5 ,STEP_TIME)),\n    (9, 0, Goto(0 ,0 ,1.5 ,STEP_TIME)),\n    (10, 0, Land(3)),\n    (10, 0, Ring(0, 0, 0, 0, 0)),#LED OFF\n]\n\nsequence_4 = [\n    (0,    0,      Land(3)),\n]\n\n\ndef wait_for_position_estimator(scf):\n    print('Waiting for estimator to find position...')\n\n    log_config = LogConfig(name='Kalman Variance', period_in_ms=500)\n    log_config.add_variable('kalman.varPX', 'float')\n    log_config.add_variable('kalman.varPY', 'float')\n    log_config.add_variable('kalman.varPZ', 'float')\n\n    var_y_history = [1000] * 10\n    var_x_history = [1000] * 10\n    var_z_history = [1000] * 10\n\n    threshold = 0.001\n\n    with SyncLogger(scf, log_config) as logger:\n        for log_entry in logger:\n            data = log_entry[1]\n\n            var_x_history.append(data['kalman.varPX'])\n            var_x_history.pop(0)\n            var_y_history.append(data['kalman.varPY'])\n            var_y_history.pop(0)\n            var_z_history.append(data['kalman.varPZ'])\n            var_z_history.pop(0)\n\n            min_x = min(var_x_history)\n            max_x = max(var_x_history)\n            min_y = min(var_y_history)\n            max_y = max(var_y_history)\n            min_z = min(var_z_history)\n            max_z = max(var_z_history)\n\n            # print(\"{} {} {}\".\n            #       format(max_x - min_x, max_y - min_y, max_z - min_z))\n\n            if (max_x - min_x) < threshold and (\n                    max_y - min_y) < threshold and (\n                    max_z - min_z) < threshold:\n                break\n\n\ndef reset_estimator(scf):\n    cf = scf.cf\n    cf.param.set_value('kalman.resetEstimation', '1')\n    time.sleep(0.1)\n    cf.param.set_value('kalman.resetEstimation', '0')\n    wait_for_position_estimator(scf)\n\n\ndef activate_high_level_commander(scf):\n    scf.cf.param.set_value('commander.enHighLevel', '1')\n\n\ndef activate_mellinger_controller(scf, use_mellinger):\n    controller = 1\n    if use_mellinger:\n        controller = 2\n    scf.cf.param.set_value('stabilizer.controller', str(controller))\n\n\ndef set_ring_color(cf, r, g, b, intensity, time):\n    cf.param.set_value('ring.fadeTime', str(time))\n\n    r *= intensity\n    g *= intensity\n    b *= intensity\n\n    color = (int(r) << 16) | (int(g) << 8) | int(b)\n\n    cf.param.set_value('ring.fadeColor', str(color))\n\n\ndef crazyflie_control(scf):\n    cf = scf.cf\n    control = controlQueues[uris.index(cf.link_uri)]\n\n    activate_mellinger_controller(scf, False)\n\n    commander = scf.cf.high_level_commander\n\n    # Set fade to color effect and reset to Led-ring OFF\n    set_ring_color(cf, 0, 0, 0, 0, 0)\n    cf.param.set_value('ring.effect', '14')\n\n    while True:\n        command = control.get()\n        if type(command) is Quit:\n            return\n        elif type(command) is Takeoff:\n            commander.takeoff(command.height, command.time)\n        elif type(command) is Land:\n            commander.land(0.0, command.time)\n        elif type(command) is Goto:\n            commander.go_to(command.x, command.y, command.z, 0, command.time)\n        elif type(command) is Ring:\n            set_ring_color(cf, command.r, command.g, command.b,\n                           command.intensity, command.time)\n            pass\n        else:\n            print('Warning! unknown command {} for uri {}'.format(command,\n                                                                  cf.uri))\n\n\n\ndef control_thread():\n    sequence_stop = False\n    cur_sequence = sequence_init\n    while not sequence_stop:\n        key_input=input('sequence what you want :')\n        if key_input == 'q': \n            sequence_stop = True\n            break;\n\n        else:\n            if(key_input == 'init'):\n                cur_sequence = sequence_init\n            elif(key_input == '1'):\n                cur_sequence = sequence_1\n            elif(key_input == '2'):\n                cur_sequence = sequence_2\n            elif(key_input == '3'):\n                cur_sequence = sequence_3\n            elif(key_input == '4'):\n                cur_sequence = sequence_4\n            else:\n                cur_sequence = sequence_init\n\n            pointer = 0\n            step = 0\n            stop = False\n            while not stop:\n                print('Step {}:'.format(step))\n                while cur_sequence[pointer][0] <= step:\n                    cf_id = cur_sequence[pointer][1]\n                    command = cur_sequence[pointer][2]\n                    print(' - Running: {} on {}'.format(command, cf_id))\n                    controlQueues[cf_id].put(command)\n                    pointer += 1\n                    if pointer >= len(cur_sequence):\n                        print('Reaching the end of the cur_sequence, stopping!')\n                        stop = True\n                        break\n                    \n                step += 1\n                time.sleep(STEP_TIME)\n       \n    for ctrl in controlQueues:\n        ctrl.put(Quit())\n\n\nif __name__ == '__main__':\n    controlQueues = [Queue() for _ in range(len(uris))]\n\n    cflib.crtp.init_drivers()\n    factory = CachedCfFactory(rw_cache='./cache')\n\n    \n    with Swarm(uris, factory=factory) as swarm:\n        swarm.parallel_safe(activate_high_level_commander)\n        swarm.parallel_safe(reset_estimator)\n        print('Starting sequence!')\n        threading.Thread(target=control_thread).start()\n        swarm.parallel_safe(crazyflie_control)\n        time.sleep(1)\n        \n","sub_path":"drone_show.py","file_name":"drone_show.py","file_ext":"py","file_size_in_byte":8833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"388857555","text":"from django.urls import path\n\nfrom store.views import *\n\nurlpatterns = [\n    path('', index, name='store_index'),\n    path('shop', shop, name=\"shop\"),\n    path('shop/<int:id>', product, name=\"product\"),\n    path('shop/other', shopOther, name=\"other\"),\n    path('shop/female', shopFeMale, name=\"female\"),\n    path('shop/male', shopMale, name=\"male\"),\n    path('blog', blog, name=\"blog\"),\n    path('order', orders, name=\"order\"),\n    path('order/detail/<int:id>', detail),\n    # products\n    path('add-product/', admin_add_product, name='add_product'),\n    path('products/<int:pk>', update_product, name='update_product'),\n    path('products/', admin_products, name='admin_products'),\n    # blog\n    path('blog-list/', admin_blog_list, name='admin_blog_list'),\n    path('add-blog/', add_blog_admin, name='add_blog_admin'),\n    path('update-blog/<int:pk>', update_blog_admin, name='update_blog_admin'),\n    path('delete-blog-admin/<int:pk>', delete_blog_admin, name='delete_blog_admin'),\n    # orders\n    path('order-list/', order_list_admin, name='order_list_admin'),\n    path('order-detail-list/<int:pk>', order_detail_list_admin, name='order_detail_list'),\n\n]\n","sub_path":"store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"303243330","text":"from libdecaf.parser.base_types import (\n    ValueCarierASTNode, AllowOnly, BaseASTNode\n)\nfrom ..identifier.ast import ASTIdentifier\nfrom ..expression.ast import ASTBaseExpression\n\n\nclass ASTBaseLocation(BaseASTNode):\n    pass\n\n\nclass ASTIdBasedLocation(ASTBaseLocation, ValueCarierASTNode):\n    value = AllowOnly(ASTIdentifier)\n\n\nclass ASTArrayBasedLocation(ASTBaseLocation):\n    identifier = AllowOnly(ASTIdentifier)\n    index_expression = AllowOnly(ASTBaseExpression)\n\n    def __init__(self, identifier, index_expression):\n        self.identifier = identifier\n        self.index_expression = index_expression\n\n    def buld_abstract(self):\n        result = super().build_abstract()\n        result.update(\n            {\n                \"identifier\": self.identifier.build_abstract(),\n                \"index_expression\": self.index_expression.build_abstract(),\n            }\n        )\n        return result\n","sub_path":"libdecaf/parser/location/ast.py","file_name":"ast.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"236757008","text":"# -----------------------------------\n# Tem Tamre\n# CMPT 103: Lab 5\n# Program Name: Lab 5 Assignment\n# -----------------------------------\n\n\"\"\"\nClick 1: Bottom left of house\nClick 2: Top right of house\nClick 3: Centre-top of doorframe (width = 1/5 house width)\nClick 4: Middle of window (side length = half of door width)\nClick 5: Peak of the roof\n\nEach house must be drawn in it's own function\nIn the upper right corner of the GraphWin, add a reset button\n    -Upon user click program undraws every drawn component of the house\n    -After the reset, the user can again select five points for the house\n    -When exiting the program, an error should not be raised\n\"\"\"\n\nfrom graphics import *\n\n\n# Main function that runs when the program runs\n# Creates the graphics window and object list, then processes user clicks\ndef main():\n    win = GraphWin(\"Tem Tamre's house\", 500, 500)\n    reset_button = draw_button(win)\n    object_list = []\n    try:\n        pt = win.getMouse()\n    except GraphicsError:\n        return\n    while pt != None:\n        dot = draw_dot(win, pt)\n        object_list.append(pt)\n        object_list.append(dot)\n        if is_clicked(reset_button, pt):\n            undraw_objects(win, object_list)\n            object_list = []\n        else:\n            new_obj = object_order(object_list, win, pt)\n            if new_obj != None:\n                object_list.append(new_obj)\n        try:\n            pt = win.getMouse()\n        except GraphicsError:\n            return\n\n\n# Determines which objects will be called in what order, and calls them\n# Parameters: object_length (length of object_list), win (graphics window),\n#               pt (point that the user clicks)\n# Return: Graphics object to be drawn and added to object_list\n\n# = [pt, dot, pt, dot, frame, pt, dot, door, pt, dot, window, pt, dot, peak]\n#    1    2   3    4     5    6    7    8    9   10     11    12  13   14\ndef object_order(object_list, win, pt):\n    if len(object_list) == 4:\n        frame = draw_frame(win, object_list[0], object_list[2])\n        return frame\n    elif len(object_list) == 7:\n        door = draw_door(win, pt, object_list[0])\n        return door\n    elif len(object_list) == 10:\n        window = draw_window(win, pt, object_list[0], object_list[2])\n        return window\n    elif len(object_list) == 13:\n        peak = draw_peak(win, object_list[0], object_list[2], pt)\n        return peak\n    else:\n        return None\n\n\n# Draws a dot where the user clicks\n# Parameters: win (graphics window), pt (point where the user clicks)\n# Return: dot (Circle object)\ndef draw_dot(win, pt):\n    dot = Circle(pt, 2)\n    dot.setFill('black')\n    dot.draw(win)\n    return dot\n\n\n# Draws the base of the house\n# Parameters: win (graphics window), pt1 (user click), object_list (list of\n#                   drawn objects)\n# Return: frame (Rectangle object)\ndef draw_frame(win, pt1, pt2):\n    ul = Point(pt1.x, pt2.y)\n    lr = Point(pt2.x, pt1.y)\n\n    frame = Rectangle(ul, lr)\n    frame.setOutline('black')\n    frame.setFill('brown')\n    frame.draw(win)\n    return frame\n\n\n# Draws the door to the house\n# Parameters: win (graphics window), pt (user click), first_point_created (\n#                   the first point used to draw the rectangle)\n# Return: door (Rectangle objecy\ndef draw_door(win, pt, first_point_created):\n    ul = Point(pt.x - pt.x // 5, pt.y)\n    lr = Point(pt.x + pt.x // 5, first_point_created.y)\n    door = Rectangle(ul, lr)\n    door.setOutline('black')\n    door.setFill('red')\n    door.draw(win)\n    return door\n\n\n# Draws the window to the house\n# Parameters: win (graphics window), pt (user click)\n# Return: window (Rectangle object)\ndef draw_window(win, pt, first_point, second_point):\n    # 20% of door width = 10% of frame width\n    width = (second_point.x - first_point.x) // 10\n\n    # can use width for height as well, since we're drawing a square\n    ul = Point(pt.x - width, pt.y - width)\n    lr = Point(pt.x + width, pt.y + width)\n\n    window = Rectangle(ul, lr)\n    window.setOutline('black')\n    window.setFill('sky blue')\n    window.draw(win)\n    return window\n\n\n# Draws the roof of the house\n# Parameters: win (graphics window), pt3 (peak of the roof)\n# Return: peak (Polygon object)\ndef draw_peak(win, first_point, second_point, pt3):\n    pt1 = Point(first_point.x, second_point.y)\n    pt2 = Point(second_point.x, second_point.y)\n    peak = Polygon(pt1, pt2, pt3)\n    peak.setOutline('black')\n    peak.setFill('green')\n    peak.draw(win)\n    return peak\n\n\n# Draws the reset button\n# Parameter: win (graphics window)\n# Return: button (Rectangle object)\ndef draw_button(win):\n    button = Rectangle(Point(410, 10), Point(500, 50))\n    button.setFill('gray')\n    button.draw(win)\n    caption = Text(Point(455, 30), 'RESET')\n    caption.draw(win)\n    return button\n\n\n# Checks to see if the reset button has been clicked\n# Parameters: reset_button (rectangle object), pt (point where user clicked)\n# Return: True if reset button was clicked, false otherwise\ndef is_clicked(reset_button, pt):\n    ul = reset_button.getP1()  # Get the upper left hand point of the button\n    lr = reset_button.getP2()  # Get the lower right hand point of the button\n\n    if ul.x <= pt.x <= lr.x and ul.y <= pt.y <= lr.y:\n        # Checks if the point object is within the reset button bounds\n        return True\n    else:\n        return False\n\n\n# Undraws all graphics objects in object_list\n# Parameters: win (graphics window), object_list (list of drawn objects)\n# Return: none\ndef undraw_objects(win, object_list):\n    for obj in object_list:\n        if type(obj) in (Circle, Rectangle, Polygon):\n            obj.undraw()\n\nmain()\n","sub_path":"labs/Lab5_TT.py","file_name":"Lab5_TT.py","file_ext":"py","file_size_in_byte":5637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"590877834","text":"import tensorflow as tf\nimport pandas as pd\nimport numpy as np\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nprint(tf.__version__)\n\nx = [-2,3,20,0,-5,7,-23,-8,32,-15];\ny = [-1,4,21,1,-4,8,-22,-7,33,-14];\n\nx_train = np.array(x).reshape(-1,1);\ny_train = np.array(y);\n\nprint(x_train)\nprint(y_train)\nprint(x_train.shape)\nprint(y_train.shape)\n\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense\n##모델 구성\nmodel = Sequential()\n##단층 퍼셉트론 구성(신경망 구축)\nmodel.add(Dense(units=1,activation='linear',input_dim=1))\n##모델 준비\nmodel.compile(optimizer='adam',loss='mse',metrics=['mae']);\n##모델 학습\nmodel.fit(x_train,y_train,epochs=3000,verbose=1);\n\nx_data = [8,9,10,-1,4,-15];\nx_data = np.array(x_data).reshape(-1,1)\n\nresult = model.predict(x_data);\nprint(result)","sub_path":"deep2/deep01.py","file_name":"deep01.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"324423282","text":"import subprocess\nimport gits_logging\nimport sys\n\n\ndef gits_authenticator(args):\n    try:\n        process_commands = [\"gh\", \"auth\", \"login\", \"--web\"]\n        if sys.platform == \"linux\" or sys.platform == \"linux2\":\n            process = subprocess.Popen(process_commands, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n            stdout, stderr = process.communicate()\n            if(process.returncode == 1):\n                return False\n        elif sys.platform[:3] == \"win\":\n            process = subprocess.Popen(process_commands, shell=True,\n                          stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n            print(process)\n            stdout, stderr = process.communicate()\n            print(stdout.decode(\"UTF-8\"))\n            print(stderr.decode(\"UTF-8\"))\n            if(process.returncode == 1):\n                return False\n    except Exception as e:\n        gits_logging.gits_logger.error(\n            \"gits auth command caught an exception\")\n        gits_logging.gits_logger.error(\"{}\".format(str(e)))\n        print(\"ERROR: gits auth command caught an exception\")\n        print(\"ERROR: {}\".format(str(e)))\n        return False\n    return True\n","sub_path":"code/gits_auth.py","file_name":"gits_auth.py","file_ext":"py","file_size_in_byte":1176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"346302686","text":"from .ctrl_enum import EnumOutDoorRunCond, EnumFanDirection, EnumFanVolume, EnumSwitch, EnumControl, EnumDevice\n\n\nclass Device:\n    def __init__(self):\n        self.alias: str = ''\n        self.id: int = 0\n        self.name: str = ''\n        self.room_id: int = 0\n        self.unit_id: int = 0\n\n\ndef _nothing():\n    \"\"\"do nothing\"\"\"\n\n\nclass AirConStatus:\n    def __init__(self, current_temp: int = None, setted_temp: int = None,\n                 switch: EnumControl.Switch = None,\n                 air_flow: EnumControl.AirFlow = None,\n                 breathe: EnumControl.Breathe = None,\n                 fan_direction1: EnumControl.FanDirection = None,\n                 fan_direction2: EnumControl.FanDirection = None,\n                 humidity: EnumControl.Humidity = None,\n                 mode: EnumControl.Mode = None,\n                 hum_allow: bool = False,\n                 fresh_air_allow: bool = False,\n                 fresh_air_humidification: EnumControl.FreshAirHumidification =  None,\n                 three_dfresh: EnumControl.ThreeDFresh = None):\n        self.current_temp = current_temp      # type: int\n        self.setted_temp = setted_temp        # type: int\n        self.switch = switch                  # type: EnumControl.Switch\n        self.air_flow = air_flow              # type: EnumControl.AirFlow\n        self.breathe = breathe                # type: EnumControl.Breathe\n        self.fan_direction1 = fan_direction1  # type: EnumControl.FanDirection\n        self.fan_direction2 = fan_direction2  # type: EnumControl.FanDirection\n        self.humidity = humidity              # type: EnumControl.Humidity\n        self.mode = mode                      # type: EnumControl.Mode\n        self.hum_allow = hum_allow       # type: bool\n        self.fresh_air_allow = fresh_air_allow    # type: bool\n        self.fresh_air_humidification = fresh_air_humidification #EnumControl.FreshAirHumidification\n        self.three_dfresh  = three_dfresh     #EnumControl.ThreeDFresh\n\n\nclass AirCon(Device):\n    def __init__(self):\n        super().__init__()\n        self.auto_dry_mode: int = 0\n        self.auto_mode: int = 0\n        self.bath_room: bool = False\n        self.new_air_con: bool = False\n        self.cool_mode: int = 0\n        self.dry_mode: int = 0\n        self.fan_dire_auto: bool = False\n        self.fan_direction1: EnumFanDirection = EnumFanDirection.FIX\n        self.fan_direction2: EnumFanDirection = EnumFanDirection.FIX\n        self.fan_volume: EnumFanVolume = EnumFanVolume.FIX\n        self.fan_volume_auto: bool = False\n        self.temp_set: bool = False\n        self.hum_fresh_air_allow: int = 0\n        self.three_dfresh_allow: bool = False\n        self.heat_mode: int = 0\n        self.more_dry_mode: int = 0\n        self.out_door_run_cond: EnumOutDoorRunCond = EnumOutDoorRunCond.VENT\n        self.pre_heat_mode: int = 0\n        self.relax_mode: int = 0\n        self.sleep_mode: int = 0\n        self.ventilation_mode: int = 0\n        self.status: AirConStatus = AirConStatus()\n\n\ndef get_device_by_aircon(aircon: AirCon):\n    if aircon.new_air_con:\n        return EnumDevice.NEWAIRCON\n    elif aircon.bath_room:\n        return EnumDevice.BATHROOM\n    else:\n        return EnumDevice.AIRCON\n\n\nclass Geothermic(Device):\n    \"\"\"do nothing\"\"\"\n\n\nclass Ventilation(Device):\n    \"\"\"do nothing\"\"\"\n\n\nclass HD(Device):\n    def __init__(self):\n        Device.__init__(self)\n        self.switch: EnumSwitch\n\nclass Sensor(Device):\n    \"\"\"do nothing\"\"\"\n\nclass Room:\n    def __init__(self):\n        self.air_con = AirCon()\n        self.alias: str = ''\n        self.geothermic: Geothermic = Geothermic()\n        self.hd_room: bool = False\n        self.icon: str = ''\n        self.id: int = 0\n        self.name: str = ''\n        self.type: int = 0\n        self.ventilation: Ventilation = Ventilation()\n        self.sensor: Sensor = Sensor()\n","sub_path":"custom_components/ds_air/ds_air_service/dao.py","file_name":"dao.py","file_ext":"py","file_size_in_byte":3867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"623416964","text":"# -*- coding: utf-8 -*-\nimport json\nfrom collections import OrderedDict\nfrom gl_lib.sim.robot import *\nfrom gl_lib.sim.geometry import *\n\nclass AreneRobot(Arene):\n    \"\"\"\n        Permet de discrétiser une arène contenant des robots et des pavés\n    \"\"\"\n\n    @staticmethod\n    def hook(dct):\n        res = RobotMotorise.hook(dct)\n        if res is not None:\n            return res\n        elif dct[\"__class__\"] == RobotTarget:\n            return RobotTarget.hook(dct)\n\n    @staticmethod\n    def load(filename):\n        \"\"\"\n            Permet de charger un objet AreneRobot et ses composants à partir d'un fichier json\n\n        :param filename: Nom du fichier\n\n        \"\"\"\n        with open(filename, 'r', encoding='utf-8') as f:\n            return json.load(f, object_hook=AreneRobot.hook)\n\n\nif __name__=='__main__':\n    a = AreneRobot()\n\n    a.add(RobotMotorise())\n\n    d = a.__dict__()\n    print(a)\n\n    #a2 = AreneRobot.load(\"arene_robot.json\")\n    #print(a2)\n\n","sub_path":"gl_lib/sim/robot/AreneRobot.py","file_name":"AreneRobot.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"465140640","text":"import sqlite3\nfrom django.urls import reverse\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\nfrom libraryapp.models import Librarian\nfrom libraryapp.models import model_factory\nfrom ..connection import Connection\n\ndef get_librarian(librarian_id):\n    with sqlite3.connect(Connection.db_path) as conn:\n        conn.row_factory = sqlite3.Row\n        db_cursor = conn.cursor()\n\n        db_cursor.execute(\"\"\"\n        SELECT l.id, l.location_id, l.user_id, u.first_name, u.last_name, u.email, lb.name, u.date_joined\n        FROM libraryapp_librarian l\n        JOIN auth_user u ON u.id = l.user_id\n        JOIN libraryapp_library lb ON lb.id = l.location_id\n        WHERE l.id = ?\n        \"\"\", (librarian_id,))\n\n        return db_cursor.fetchone()\n\n@login_required\ndef librarian_details(request, librarian_id):\n    if request.method == 'GET':\n        dataset = get_librarian(librarian_id)\n\n        librarian = Librarian()\n        librarian.id = dataset[\"id\"]\n        librarian.location_id = dataset[\"location_id\"]\n        librarian.user_id = dataset[\"user_id\"]\n        librarian.first_name = dataset[\"first_name\"]\n        librarian.last_name = dataset[\"last_name\"]\n        librarian.email = dataset[\"email\"]\n        librarian.branch_name = dataset[\"name\"]\n        librarian.date_joined = dataset[\"date_joined\"]\n\n        template = 'librarians/detail.html'\n\n        context = {\n            'librarian': librarian\n        }\n\n    return render(request, template, context)","sub_path":"libraryapp/views/librarians/details.py","file_name":"details.py","file_ext":"py","file_size_in_byte":1520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"240316691","text":"import os\nimport pathlib\nfrom configparser import ConfigParser\n\nfrom alembic.config import CommandLine, Config\n\n\ndef main():\n    current_dir = os.path.abspath(os.getcwd())\n    module_path = pathlib.Path(__file__).parent\n    os.chdir(current_dir)\n\n    cmd = CommandLine()\n    cmd.parser.add_argument('--app-config', dest='db_config', required=True, help='service configuration file path')\n    cmd_options = cmd.parser.parse_args()\n\n    config = ConfigParser()\n    config.read(cmd_options.db_config)\n    db_url = config.get(section='db', option='url')\n    if not db_url:\n        cmd.parser.error(f'source config {cmd_options.db_config} contains empty \"url\" in [db] section')\n\n    alembic_ini = Config(file_=str(module_path / 'alembic.ini'), ini_section=cmd_options.name, cmd_opts=cmd_options)\n    alembic_ini.set_main_option('script_location', str(module_path / 'alembic'))\n    alembic_ini.set_main_option('sqlalchemy.url', db_url)\n    exit(cmd.run_cmd(alembic_ini, cmd_options))\n","sub_path":"plan_b/db_manage.py","file_name":"db_manage.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"298385138","text":"\"\"\"\nThis script is for creating the \nmacs jobscripts\n\"\"\"\n\n# create dicts\nmetadata = {}\nmacs_params = {\"inputdir\": \"\", \n\t\t\t\t\"outputdir\": \"\", \n\t\t\t\t\"species\": \"\",\n\t\t\t\t\"p_value_cutoff\": \"\"}\n\ndef convert_meta_to_dict(filename):\n\twith open(filename, \"r\") as f:\n\t\tfor lines in f:\n\t\t\tlines = lines.strip().split()\n\t\t\tmetadata[lines[0]] = lines[1]\n\treturn(metadata)\n\ndef collect_macs_info(inputdir, outputdir, species, p_value_cutoff):\n\tmacs_params[\"inputdir\"] = inputdir\n\tmacs_params[\"outputdir\"] = outputdir\n\tmacs_params[\"species\"] = species \n\tmacs_params[\"p_value_cutoff\"] = p_value_cutoff\n\treturn macs_params\n\ndef generate_macs(treatment_list, control_list, macs_jobname):\n\twith open(macs_jobname + \"_macs.sh\", \"w\") as outfile:\n\t\tbam_suffix = \".unique.sorted.rmdup.bam\"\n\t\ttreatment = \" \".join(metadata[v]+bam_suffix for v in treatment_list)\n\t\tcontrol = \" \".join(metadata[v]+bam_suffix for v in control_list)\n\t\toutfile.write(\"\"\"\nINPUTDIR={0}\nOUTPUTDIR={1}\ncd {2} \nmacs2 callpeak \\\\\n-t {3} \\\\\n-c {4} \\\\\n-f BAM \\\\\n-g {5} \\\\\n-n {6}/{7} \\\\\n--bw 300 \\\\\n-p {8} 2> {9}/{10}\n\t\"\"\".format(macs_params['inputdir'], macs_params['outputdir'],\n\t\t\t\tmacs_params['inputdir'],\n\t\t\t\ttreatment, control,\n\t\t\t\tmacs_params['species'], macs_params['outputdir'], \n\t\t\t\tmacs_jobname, str(macs_params['p_value_cutoff']),\n\t\t\t\tmacs_params['outputdir'], macs_jobname + \".o\"))\n\ndef main():\n\t# metadata format\n\t# input_SS_1\t2_S1_L005\n\t# input_SS_2\t4_S2_L005\n\t# input_SS_3\t5_S3_L005\n\t# input_Dual_1\t6_S4_L005\n\t# input_Dual_2\t7_S5_L005\n\tconvert_meta_to_dict(\"metadata.txt\")\n\n\t# set misc. params\n\tcollect_macs_info(\"/data/home/ramaka02/projects/2018/ian/control_sus_res_dual_k4me3_chipseq/processed/bam\",\n\t\t\t\t\t  \"/data/home/ramaka02/projects/2018/ian/control_sus_res_dual_k4me3_chipseq/macs_snakemake\",\n\t\t\t\t\t  \"mm\", # 2e9 for rat, hs for human, mm for mouse\n\t\t\t\t\t  1e-5) # p-value cutoff\n\n\t# comparisons\n\tgenerate_macs([\"Dual_SS_1\", \"Dual_SS_2\", \"Dual_SS_3\"], \n\t\t\t\t  [\"input_SS_1\", \"input_SS_2\", \"input_SS_3\"], \n\t\t\t\t  \"Dual_SS\")\n\n\tgenerate_macs([\"K4me3_SS_1\", \"K4me3_SS_2\", \"K4me3_SS_3\"], \n\t\t\t\t  [\"input_SS_1\", \"input_SS_2\", \"input_SS_3\"], \n\t\t\t\t  \"K4me3_SS\")\n\nif __name__ == '__main__':\n\tmain()\n\n\n","sub_path":"macs/create_macs_command.py","file_name":"create_macs_command.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"580629079","text":"# -*- coding: utf-8 -*-\nimport torch\nfrom scipy.sparse.linalg import eigsh, ArpackNoConvergence\nfrom scipy.sparse import coo_matrix\nimport numpy as np\nimport torch.nn as nn\nfrom torch_sparse import spspmm\n\n\nclass Graph(object):\n    def __init__(self, A: torch.Tensor, lap_type=\"normalized\"):\n        self.A = A.float().coalesce()\n        self.lap_type = lap_type\n        self.n_vertices = self.A.shape[0]\n        self.n_edges = len(self.A.values())\n        self.L = None\n        self._lmax = None\n        self._D = None\n        self._dw = None\n        self._lmax_method = None\n\n        self.compute_laplacian(lap_type)\n\n    def _get_upper_bound(self):\n        if self.lap_type == 'normalized':\n            return 2.0  # Equal iff the graph is bipartite.\n        elif self.lap_type == 'combinatorial':\n            bounds = []\n            # Equal for full graphs.\n            bounds += [self.n_vertices * torch.max(self.A)]\n            # Gershgorin circle theorem. Equal for regular bipartite graphs.\n            # Special case of the below bound.\n            bounds += [2 * torch.max(self.dw)]\n            # Anderson, Morley, Eigenvalues of the Laplacian of a graph.\n            # Equal for regular bipartite graphs.\n            if self.n_edges > 0:\n                sources, targets = self.A.nonzero(as_tuple=True)\n                bounds += [torch.max(self.dw[sources] + self.dw[targets])]\n            m = self.A.dot(self.dw) / self.dw  # Mean degree of adjacent vertices.\n            bounds += [torch.max(self.dw + m)]\n            # Good review: On upper bounds for Laplacian graph eigenvalues.\n            return min(bounds)\n        elif self.lap_type == 'hermitian':\n            return torch.max(torch.symeig(self.L)[0])\n        elif self.lap_type == 'random walk':\n            return torch.max(1 - torch.eig(self.L)[0].T[0])\n        else:\n            raise ValueError('Unknown Laplacian type '\n                             '{}'.format(self.lap_type))\n\n    def compute_laplacian(self, lap_type='combinatorial', q=0.02):\n        if lap_type != self.lap_type:\n            # Those attributes are invalidated when the Laplacian is changed.\n            # Alternative: don't allow the user to change the Laplacian.\n            self._lmax = None\n            self._D = None\n\n        self.lap_type = lap_type\n\n        if lap_type == 'combinatorial':\n            self.L = self.D - self.A\n        elif lap_type == 'normalized':\n            d = torch.pow(self.dw, -0.5)\n            diagonal_indices = torch.tensor([list(range(self.n_vertices)), list(range(self.n_vertices))])\n            indexDmA, valueDmA = spspmm(\n                d.indices().repeat(2, 1),\n                d.values(),\n                # d.values().double(),\n                self.A.indices(),\n                self.A.values(),\n                # self.A.values().double(),\n                self.n_vertices,\n                self.n_vertices,\n                self.n_vertices,\n            )\n            indexDmAmD, valueDmAmD = spspmm(\n                indexDmA,\n                valueDmA,\n                d.indices().repeat(2, 1),\n                # d.values().double(),\n                d.values(),\n                self.n_vertices,\n                self.n_vertices,\n                self.n_vertices\n            )\n            self.L = torch.sparse_coo_tensor(diagonal_indices, torch.ones(self.n_vertices),\n                                             [self.n_vertices, self.n_vertices]) \\\n                     - torch.sparse_coo_tensor(indexDmAmD, valueDmAmD, [self.n_vertices, self.n_vertices])\n            self.L = self.L.coalesce()\n        elif lap_type == 'hermitian':\n            sym_weighted_adajacency_matrix = (self.A + self.A.T) / 2\n            Gamma_q = torch.pow(np.e, 2j * np.pi * q * (self.A - self.A.T))\n            self.L = self.D - Gamma_q.mm(sym_weighted_adajacency_matrix)\n        elif lap_type == 'random walk':\n            self.L = torch.inverse(torch.diag(self.dw)).mm(self.A)\n        else:\n            raise ValueError('Unknown Laplacian type {}'.format(lap_type))\n\n    @property\n    def dw(self):\n        if self._dw is None:\n            self._dw = torch.sparse.sum(self.A, dim=0).float()\n        return self._dw\n\n    @property\n    def lmax(self):\n        if self._lmax is None:\n            self.estimate_lmax()\n        return self._lmax\n\n    @property\n    def D(self):\n        if self._D is None:\n            self._D = torch.sparse_coo_tensor([range(self.n_vertices), range(self.n_vertices)],\n                                        self.dw.values(), [self.n_vertices, self.n_vertices])\n        return self._D\n\n    def estimate_lmax(self, method='lanczos'):\n        if method == self._lmax_method:\n            return\n        self._lmax_method = method\n\n        if method == 'lanczos':\n            try:\n                # We need to cast the matrix L to a supported type.\n                # TODO: not good for memory. Cast earlier?\n                L_coo = coo_matrix((self.L.values().cpu(), self.L.indices().cpu().numpy()))\n                lmax = eigsh(L_coo.asfptype(), k=1, tol=5e-3,\n                             ncv=min(self.n_vertices, 10),\n                             # return_eigenvectors=False).astype('float16')\n                             return_eigenvectors = False)\n                lmax = lmax[0]\n                if lmax > self._get_upper_bound() + 1e-12:\n                    lmax = 2\n                lmax *= 1.01  # Increase by 1% to be robust to errors.\n                self._lmax = lmax\n            except ArpackNoConvergence:\n                raise ValueError('The Lanczos method did not converge. '\n                                 'Try to use bounds.')\n        elif method == 'bounds':\n            self._lmax = self._get_upper_bound()\n        else:\n            raise ValueError('Unknown method {}'.format(method))\n\n\nclass Filter(nn.Module):\n    def __init__(self, G, kernel, chebyshev_order=32):\n        super(Filter, self).__init__()\n        self.G = G\n\n        self._kernel = kernel\n        self.chebyshev_order = chebyshev_order\n\n        self.signal = torch.sparse_coo_tensor(\n            [range(G.n_vertices), range(G.n_vertices)],\n            torch.ones(G.n_vertices),\n            [G.n_vertices, G.n_vertices]\n        ).coalesce()\n\n    def evaluate(self, x):\n        y = self._kernel(x)\n        return y\n\n    def cheby_eval(self, x):\n        x = x.view(-1, 1)\n        a, b = 0, self.G.lmax\n        y = (2.0 * x - a - b) * (1.0 / (b - a))\n        y2 = 2.0 * y\n        c = self.compute_cheby_coeff(m=self.chebyshev_order)\n        (d, dd) = (c[-1], 0)  # Special case first step for efficiency\n        for cj in c.flip(0)[1:-1]:  # Clenshaw's recurrence\n            (d, dd) = (y2 * d - dd + cj, d)\n        return y * d - dd + 0.5 * c[0]\n\n    def compute_cheby_coeff(self, m: int = 32, N: int = None) -> torch.Tensor:\n        if not N:\n            N = m + 1\n\n        a_arange = [0, self.G.lmax]\n\n        a1 = (a_arange[1] - a_arange[0]) / 2\n        a2 = (a_arange[1] + a_arange[0]) / 2\n        c = []\n\n        # tmpN = torch.arange(N).double()\n        tmpN = torch.arange(N)\n        num = torch.cos(np.pi * (tmpN + 0.5) / N)\n        for o in range(m + 1):\n            c.append(2. / N * torch.cos(np.pi * o * (tmpN + 0.5) / N).view(1, -1).mm(self._kernel(a1 * num + a2)))\n\n        return torch.cat(c)\n\n    def cheby_op(self, c: torch.Tensor) -> torch.Tensor:\n        G = self.G\n        M = c.shape[0]\n\n        if M < 2:\n            raise TypeError(\"The coefficients have an invalid shape\")\n\n        # thanks to that, we can also have 1d signal.\n\n        a_arange = [0, G.lmax]\n\n        a1 = float(a_arange[1] - a_arange[0]) / 2.\n        a2 = float(a_arange[1] + a_arange[0]) / 2.\n\n        twf_old = self.signal\n        twf_cur = ((G.L - a2 * self.signal) / a1).coalesce()\n\n        nf = c.shape[1]\n        r = []\n\n        for i in range(nf):\n            r.append(\n                torch.sparse_coo_tensor(twf_old.indices(), twf_old.values() * 0.5 * c[0][i], twf_old.shape)\n                + torch.sparse_coo_tensor(twf_cur.indices(), twf_cur.values() * c[1][i], twf_cur.shape)\n            )\n\n        factor = (2 / a1 * (G.L - a2 * self.signal)).coalesce()\n\n        for k in range(2, M):\n            fmt_index, fmt_value = spspmm(\n                factor.indices(),\n                factor.values(),\n                twf_cur.indices(),\n                twf_cur.values(),\n                G.n_vertices,\n                G.n_vertices,\n                G.n_vertices)\n\n            twf_new = (torch.sparse_coo_tensor(fmt_index, fmt_value, [G.n_vertices, G.n_vertices]) - twf_old).coalesce()\n            for i in range(nf):\n                r[i] = torch.sparse_coo_tensor(twf_new.indices(), twf_new.values() * c[k,i], twf_new.shape) + r[i]\n\n            twf_old = twf_cur\n            twf_cur = twf_new\n\n        for i in range(len(r)):\n            r[i] = r[i].coalesce()\n        return r\n\n    def forward(self) -> object:\n        \"\"\"\n        Parameters\n        ----------\n        s: N x Df x Ns. where N is the number of nodes, Nf is the dimension of node features, Ns is the number of signals\n\n        Returns\n        -------\n        s: torch.Tensor\n        \"\"\"\n\n        # TODO: update Chebyshev implementation (after 2D filter banks).\n        c = self.compute_cheby_coeff(m=self.chebyshev_order)\n        s = self.cheby_op(c)\n\n        return s\n\n    def localize(self, i, **kwargs):\n        s = torch.zeros(self.G.n_vertices)\n        s[i] = 1\n\n        return np.sqrt(self.G.n_vertices) * self.forward(s, **kwargs)\n\n    def estimate_frame_bounds(self, x=None):\n        if x is None:\n            x = torch.linspace(0, self.G.lmax, 1000)\n\n        sum_filters = torch.sum(self.evaluate(x) ** 2, dim=0)\n\n        return sum_filters.min(), sum_filters.max()\n\n    def complement(self, frame_bound=None):\n        def kernel(x):\n            y = self.evaluate(x)\n            y = torch.pow(y, 2)\n            y = torch.sum(y, dim=1)\n\n            if frame_bound is None:\n                bound = y.max()\n            elif y.max() > frame_bound:\n                raise ValueError('The chosen bound is not feasible. '\n                                 'Choose at least {}.'.format(y.max()))\n            else:\n                bound = frame_bound\n\n            return torch.sqrt(bound - y)\n\n        return Filter(self.G, kernel)\n\n    def inverse(self):\n        A, _ = self.estimate_frame_bounds()\n        if A == 0:\n            raise ValueError('The filter bank is not invertible as it is not '\n                             'a frame (lower frame bound A=0).')\n\n        def kernel(g, i, x):\n            y = g.evaluate(x).T\n            z = torch.pinverse(y.view(-1, 1)).view(-1)\n            return z[:, i]  # Return one filter.\n\n        return Filter(self.G, kernel)\n\n    def plot(self, eigenvalues=None, sum=None, title=None,\n             ax=None, **kwargs):\n        import matplotlib.pyplot as plt\n\n        fig, ax = plt.subplots()\n        x = torch.linspace(torch.min(self.G.e), self.G.lmax, self.chebyshev_order).detach()\n        y = self.evaluate(x).T.detach()\n        x = x.cpu()\n        y = y.cpu()\n        lines = ax.plot(x, y)\n\n        if len(y.shape) == 2:\n            for i in range(y.shape[1]):\n                ax.plot(x, y[:, i], '.')\n        else:\n            ax.plot(x, y, '.')\n        if sum:\n            ax.plot(x, np.sum(y ** 2, 1), '.')\n\n        # TODO: plot highlighted eigenvalues\n        if sum:\n            line_sum, = ax.plot(x, np.sum(y ** 2, 1), 'k', **kwargs)\n\n        ax.set_xlabel(r\"$\\lambda$: laplacian's eigenvalues / graph frequencies\")\n        ax.set_ylabel(r'$\\hat{g}(\\lambda)$: filter response')\n        plt.show()\n","sub_path":"model/spectral_filter.py","file_name":"spectral_filter.py","file_ext":"py","file_size_in_byte":11589,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"646974234","text":"import binascii\nimport random\nimport datetime\nfrom twisted.internet.protocol import Protocol\nfrom twisted.internet import error as twisted_error\nfrom twisted.internet import reactor, task, defer\nfrom twisted.internet.address import IPv4Address\nfrom twisted.internet.defer import CancelledError\nfrom twisted.internet import error\nfrom neo.Core.Blockchain import Blockchain as BC\nfrom neocore.IO.BinaryReader import BinaryReader\nfrom neo.Network.Message import Message\nfrom neo.IO.MemoryStream import StreamManager\nfrom neo.IO.Helper import Helper as IOHelper\nfrom neo.Core.Helper import Helper\nfrom .Payloads.GetBlocksPayload import GetBlocksPayload\nfrom .Payloads.InvPayload import InvPayload\nfrom .Payloads.NetworkAddressWithTime import NetworkAddressWithTime\nfrom .Payloads.VersionPayload import VersionPayload\nfrom .Payloads.HeadersPayload import HeadersPayload\nfrom .Payloads.AddrPayload import AddrPayload\nfrom .InventoryType import InventoryType\nfrom neo.Settings import settings\nfrom neo.logging import log_manager\nfrom neo.Network.address import Address\n\nlogger = log_manager.getLogger('network')\nlogger_verbose = log_manager.getLogger('network.verbose')\nMODE_MAINTAIN = 7\nMODE_CATCHUP = 2\n\nmode_to_name = {MODE_CATCHUP: 'CATCHUP', MODE_MAINTAIN: 'MAINTAIN'}\n\nHEARTBEAT_BLOCKS = 'B'\nHEARTBEAT_HEADERS = 'H'\n\n\nclass NeoNode(Protocol):\n    Version = None\n\n    leader = None\n\n    identifier = None\n\n    def has_tasks_running(self):\n        block = False\n        header = False\n        peer = False\n        if self.block_loop and self.block_loop.running:\n            block = True\n\n        if self.peer_loop and self.peer_loop.running:\n            peer = True\n\n        if self.header_loop and self.header_loop.running:\n            header = True\n\n        return block and header and peer\n\n    def start_all_tasks(self):\n        if not self.disconnecting:\n            self.start_block_loop()\n            self.start_header_loop()\n            self.start_peerinfo_loop()\n\n    def start_block_loop(self):\n        logger_verbose.debug(f\"{self.prefix} start_block_loop\")\n        if self.block_loop and self.block_loop.running:\n            logger_verbose.debug(f\"start_block_loop: still running -> stopping...\")\n            self.stop_block_loop()\n        self.block_loop = task.LoopingCall(self.AskForMoreBlocks)\n        self.block_loop_deferred = self.block_loop.start(self.sync_mode, now=False)\n        self.block_loop_deferred.addErrback(self.OnLoopError)\n        # self.leader.task_handles[self.block_loop] = self.prefix + f\"{'block_loop':>15}\"\n\n    def stop_block_loop(self, cancel=True):\n        logger_verbose.debug(f\"{self.prefix} stop_block_loop: cancel -> {cancel}\")\n        if self.block_loop:\n            logger_verbose.debug(f\"{self.prefix} self.block_loop true\")\n            if self.block_loop.running:\n                logger_verbose.debug(f\"{self.prefix} stop_block_loop, calling stop\")\n                self.block_loop.stop()\n        if cancel and self.block_loop_deferred:\n            logger_verbose.debug(f\"{self.prefix} stop_block_loop: trying to cancel\")\n            self.block_loop_deferred.cancel()\n\n    def start_peerinfo_loop(self):\n        logger_verbose.debug(f\"{self.prefix} start_peerinfo_loop\")\n        if self.peer_loop and self.peer_loop.running:\n            logger_verbose.debug(f\"start_peer_loop: still running -> stopping...\")\n            self.stop_peerinfo_loop()\n        self.peer_loop = task.LoopingCall(self.RequestPeerInfo)\n        self.peer_loop_deferred = self.peer_loop.start(120, now=False)\n        self.peer_loop_deferred.addErrback(self.OnLoopError)\n        # self.leader.task_handles[self.peer_loop] = self.prefix + f\"{'peerinfo_loop':>15}\"\n\n    def stop_peerinfo_loop(self, cancel=True):\n        logger_verbose.debug(f\"{self.prefix} stop_peerinfo_loop: cancel -> {cancel}\")\n        if self.peer_loop and self.peer_loop.running:\n            logger_verbose.debug(f\"{self.prefix} stop_peerinfo_loop, calling stop\")\n            self.peer_loop.stop()\n        if cancel and self.peer_loop_deferred:\n            logger_verbose.debug(f\"{self.prefix} stop_peerinfo_loop: trying to cancel\")\n            self.peer_loop_deferred.cancel()\n\n    def start_header_loop(self):\n        logger_verbose.debug(f\"{self.prefix} start_header_loop\")\n        if self.header_loop and self.header_loop.running:\n            logger_verbose.debug(f\"start_header_loop: still running -> stopping...\")\n            self.stop_header_loop()\n        self.header_loop = task.LoopingCall(self.AskForMoreHeaders)\n        self.header_loop_deferred = self.header_loop.start(5, now=False)\n        self.header_loop_deferred.addErrback(self.OnLoopError)\n        # self.leader.task_handles[self.header_loop] = self.prefix + f\"{'header_loop':>15}\"\n\n    def stop_header_loop(self, cancel=True):\n        logger_verbose.debug(f\"{self.prefix} stop_header_loop: cancel -> {cancel}\")\n        if self.header_loop:\n            logger_verbose.debug(f\"{self.prefix} self.header_loop true\")\n            if self.header_loop.running:\n                logger_verbose.debug(f\"{self.prefix} stop_header_loop, calling stop\")\n                self.header_loop.stop()\n        if cancel and self.header_loop_deferred:\n            logger_verbose.debug(f\"{self.prefix} stop_header_loop: trying to cancel\")\n            self.header_loop_deferred.cancel()\n\n    def __init__(self, incoming_client=False):\n        \"\"\"\n        Create an instance.\n        The NeoNode class is the equivalent of the C# RemoteNode.cs class. It represents a single Node connected to the client.\n\n        Args:\n            incoming_client (bool): True if node is an incoming client and the handshake should be initiated.\n        \"\"\"\n        from neo.Network.NodeLeader import NodeLeader\n\n        self.leader = NodeLeader.Instance()\n        self.nodeid = self.leader.NodeId\n        self.remote_nodeid = random.randint(1294967200, 4294967200)\n        self.endpoint = ''\n        self.address = None\n        self.buffer_in = bytearray()\n        self.myblockrequests = set()\n        self.bytes_in = 0\n        self.bytes_out = 0\n\n        self.sync_mode = MODE_CATCHUP\n\n        self.host = None\n        self.port = None\n\n        self.incoming_client = incoming_client\n        self.handshake_complete = False\n        self.expect_verack_next = False\n        self.start_outstanding_data_request = {HEARTBEAT_BLOCKS: 0, HEARTBEAT_HEADERS: 0}\n\n        self.block_loop = None\n        self.block_loop_deferred = None\n\n        self.peer_loop = None\n        self.peer_loop_deferred = None\n\n        self.header_loop = None\n        self.header_loop_deferred = None\n\n        self.disconnect_deferred = None\n        self.disconnecting = False\n\n        logger.debug(f\"{self.prefix} new node created, not yet connected\")\n\n    def Disconnect(self, reason=None, isDead=True):\n        \"\"\"Close the connection with the remote node client.\"\"\"\n        self.disconnecting = True\n        self.expect_verack_next = False\n        if reason:\n            logger.debug(f\"Disconnecting with reason: {reason}\")\n        self.stop_block_loop()\n        self.stop_header_loop()\n        self.stop_peerinfo_loop()\n        if isDead:\n            self.leader.AddDeadAddress(self.address, reason=f\"{self.prefix} Forced disconnect by us\")\n\n        self.leader.forced_disconnect_by_us += 1\n\n        self.disconnect_deferred = defer.Deferred()\n        self.disconnect_deferred.debug = True\n        # force disconnection without waiting on the other side\n        # calling later to give func caller time to add callbacks to the deferred\n        reactor.callLater(1, self.transport.abortConnection)\n        return self.disconnect_deferred\n\n    @property\n    def prefix(self):\n        if isinstance(self.endpoint, IPv4Address) and self.identifier is not None:\n            return f\"[{self.identifier:03}][{mode_to_name[self.sync_mode]}][{self.address:>21}]\"\n        else:\n            return f\"\"\n\n    def Name(self):\n        \"\"\"\n        Get the peer name.\n\n        Returns:\n            str:\n        \"\"\"\n        name = \"\"\n        if self.Version:\n            name = self.Version.UserAgent\n        return name\n\n    def GetNetworkAddressWithTime(self):\n        \"\"\"\n        Get a network address object.\n\n        Returns:\n            NetworkAddressWithTime: if we have a connection to a node.\n            None: otherwise.\n        \"\"\"\n        if self.port is not None and self.host is not None and self.Version is not None:\n            return NetworkAddressWithTime(self.host, self.port, self.Version.Services)\n        return None\n\n    def IOStats(self):\n        \"\"\"\n        Get the connection I/O stats.\n\n        Returns:\n            str:\n        \"\"\"\n        biM = self.bytes_in / 1000000  # megabyes\n        boM = self.bytes_out / 1000000\n\n        return f\"{biM:>10} MB in / {boM:>10} MB out\"\n\n    def connectionMade(self):\n        \"\"\"Callback handler from twisted when establishing a new connection.\"\"\"\n        self.endpoint = self.transport.getPeer()\n        # get the reference to the Address object in NodeLeader so we can manipulate it properly.\n        tmp_addr = Address(f\"{self.endpoint.host}:{self.endpoint.port}\")\n        try:\n            known_idx = self.leader.KNOWN_ADDRS.index(tmp_addr)\n            self.address = self.leader.KNOWN_ADDRS[known_idx]\n        except ValueError:\n            # Not found.\n            self.leader.AddKnownAddress(tmp_addr)\n            self.address = tmp_addr\n\n        self.address.address = \"%s:%s\" % (self.endpoint.host, self.endpoint.port)\n        self.host = self.endpoint.host\n        self.port = int(self.endpoint.port)\n        self.leader.AddConnectedPeer(self)\n        self.leader.RemoveFromQueue(self.address)\n        self.leader.peers_connecting -= 1\n        logger.debug(f\"{self.address} connection established\")\n        if self.incoming_client:\n            # start protocol\n            self.SendVersion()\n\n    def connectionLost(self, reason=None):\n        \"\"\"Callback handler from twisted when a connection was lost.\"\"\"\n        try:\n            self.connected = False\n            self.stop_block_loop()\n            self.stop_peerinfo_loop()\n            self.stop_header_loop()\n\n            self.ReleaseBlockRequests()\n            self.leader.RemoveConnectedPeer(self)\n\n            time_expired = self.time_expired(HEARTBEAT_BLOCKS)\n            # some NEO-cli versions have a 30s timeout to receive block/consensus or tx messages. By default neo-python doesn't respond to these requests\n            if time_expired > 20:\n                self.address.last_connection = Address.Now()\n                self.leader.AddDeadAddress(self.address, reason=f\"{self.prefix} Premature disconnect\")\n\n            if reason and reason.check(twisted_error.ConnectionDone):\n                # this might happen if they close our connection because they've reached max peers or something similar\n                logger.debug(f\"{self.prefix} disconnected normally with reason:{reason.value}\")\n                self._check_for_consecutive_disconnects(\"connection done\")\n\n            elif reason and reason.check(twisted_error.ConnectionLost):\n                # Can be due to a timeout. Only if this happened again within 5 minutes do we label the node as bad\n                # because then it clearly doesn't want to talk to us or we have a bad connection to them.\n                # Otherwise allow for the node to be queued again by NodeLeader.\n                logger.debug(f\"{self.prefix} disconnected with connectionlost reason: {reason.value}\")\n                self._check_for_consecutive_disconnects(\"connection lost\")\n\n            else:\n                logger.debug(f\"{self.prefix} disconnected with reason: {reason.value}\")\n        except Exception as e:\n            logger.error(\"Error with connection lost: %s \" % e)\n\n        def try_me(err):\n            err.check(error.ConnectionAborted)\n\n        if self.disconnect_deferred:\n            d, self.disconnect_deferred = self.disconnect_deferred, None  # type: defer.Deferred\n            d.addErrback(try_me)\n            if len(d.callbacks) > 0:\n                d.callback(reason)\n            else:\n                print(\"connLost, disconnect_deferred cancelling!\")\n                d.cancel()\n\n    def _check_for_consecutive_disconnects(self, error_name):\n        now = datetime.datetime.utcnow().timestamp()\n        FIVE_MINUTES = 5 * 60\n        if self.address.last_connection != 0 and now - self.address.last_connection < FIVE_MINUTES:\n            self.leader.AddDeadAddress(self.address, reason=f\"{self.prefix} second {error_name} within 5 minutes\")\n        else:\n            self.address.last_connection = Address.Now()\n\n    def ReleaseBlockRequests(self):\n        bcr = BC.Default().BlockRequests\n        requests = self.myblockrequests\n\n        for req in requests:\n            try:\n                if req in bcr:\n                    bcr.remove(req)\n            except Exception as e:\n                logger.debug(f\"{self.prefix} Could not remove request {e}\")\n\n        self.myblockrequests = set()\n\n    def dataReceived(self, data):\n        \"\"\" Called from Twisted whenever data is received. \"\"\"\n        self.bytes_in += (len(data))\n        self.buffer_in = self.buffer_in + data\n\n        while self.CheckDataReceived():\n            pass\n\n    def CheckDataReceived(self):\n        \"\"\"Tries to extract a Message from the data buffer and process it.\"\"\"\n        currentLength = len(self.buffer_in)\n        if currentLength < 24:\n            return False\n        # Extract the message header from the buffer, and return if not enough\n        # buffer to fully deserialize the message object.\n\n        try:\n            # Construct message\n            mstart = self.buffer_in[:24]\n            ms = StreamManager.GetStream(mstart)\n            reader = BinaryReader(ms)\n            m = Message()\n\n            # Extract message metadata\n            m.Magic = reader.ReadUInt32()\n            m.Command = reader.ReadFixedString(12).decode('utf-8')\n            m.Length = reader.ReadUInt32()\n            m.Checksum = reader.ReadUInt32()\n\n            # Return if not enough buffer to fully deserialize object.\n            messageExpectedLength = 24 + m.Length\n            if currentLength < messageExpectedLength:\n                return False\n\n        except Exception as e:\n            logger.debug(f\"{self.prefix} Error: could not read message header from stream {e}\")\n            # self.Log('Error: Could not read initial bytes %s ' % e)\n            return False\n\n        finally:\n            StreamManager.ReleaseStream(ms)\n            del reader\n\n        # The message header was successfully extracted, and we have enough enough buffer\n        # to extract the full payload\n        try:\n            # Extract message bytes from buffer and truncate buffer\n            mdata = self.buffer_in[:messageExpectedLength]\n            self.buffer_in = self.buffer_in[messageExpectedLength:]\n\n            # Deserialize message with payload\n            stream = StreamManager.GetStream(mdata)\n            reader = BinaryReader(stream)\n            message = Message()\n            message.Deserialize(reader)\n\n            if self.incoming_client and self.expect_verack_next:\n                if message.Command != 'verack':\n                    self.Disconnect(\"Expected 'verack' got {}\".format(message.Command))\n\n            # Propagate new message\n            self.MessageReceived(message)\n\n        except Exception as e:\n            logger.debug(f\"{self.prefix} Could not extract message {e}\")\n            # self.Log('Error: Could not extract message: %s ' % e)\n            return False\n\n        finally:\n            StreamManager.ReleaseStream(stream)\n\n        return True\n\n    def MessageReceived(self, m):\n        \"\"\"\n        Process a message.\n\n        Args:\n            m (neo.Network.Message):\n        \"\"\"\n        if m.Command == 'verack':\n            # only respond with a verack when we connect to another client, not when a client connected to us or\n            # we might end up in a verack loop\n            if self.incoming_client:\n                if self.expect_verack_next:\n                    self.expect_verack_next = False\n            else:\n                self.HandleVerack()\n        elif m.Command == 'version':\n            self.HandleVersion(m.Payload)\n        elif m.Command == 'getaddr':\n            self.SendPeerInfo()\n        elif m.Command == 'getdata':\n            self.HandleGetDataMessageReceived(m.Payload)\n        elif m.Command == 'getblocks':\n            self.HandleGetBlocksMessageReceived(m.Payload)\n        elif m.Command == 'inv':\n            self.HandleInvMessage(m.Payload)\n        elif m.Command == 'block':\n            self.HandleBlockReceived(m.Payload)\n        elif m.Command == 'getheaders':\n            self.HandleGetHeadersMessageReceived(m.Payload)\n        elif m.Command == 'headers':\n            self.HandleBlockHeadersReceived(m.Payload)\n        elif m.Command == 'addr':\n            self.HandlePeerInfoReceived(m.Payload)\n        else:\n            logger.debug(f\"{self.prefix} Command not implemented: {m.Command}\")\n\n    def OnLoopError(self, err):\n        # happens if we cancel the disconnect_deferred before it is executed\n        # causes no harm\n        if type(err.value) == CancelledError:\n            logger_verbose.debug(f\"{self.prefix} OnLoopError cancelled deferred\")\n            return\n        logger.debug(f\"{self.prefix} On neo Node loop error {err}\")\n\n    def onThreadDeferredErr(self, err):\n        if type(err.value) == CancelledError:\n            logger_verbose.debug(f\"{self.prefix} onThreadDeferredError cancelled deferred\")\n            return\n        logger.debug(f\"{self.prefix} On Call from thread error {err}\")\n\n    def keep_alive(self):\n        ka = Message(\"ping\")\n        self.SendSerializedMessage(ka)\n\n    def ProtocolReady(self):\n        # do not start the looping tasks if we're in the BlockRequests catchup task\n        # otherwise BCRLen will not drop because the new node will continue adding blocks\n        logger_verbose.debug(f\"{self.prefix} ProtocolReady called\")\n        if not self.leader.check_bcr_loop or (self.leader.check_bcr_loop and not self.leader.check_bcr_loop.running):\n            logger_verbose.debug(f\"{self.prefix} Protocol ready -> starting loops\")\n            self.start_block_loop()\n            self.start_peerinfo_loop()\n            self.start_header_loop()\n\n        self.RequestPeerInfo()\n\n    def AskForMoreHeaders(self):\n        logger.debug(f\"{self.prefix} asking for more headers, starting from {BC.Default().HeaderHeight}\")\n        self.health_check(HEARTBEAT_HEADERS)\n        get_headers_message = Message(\"getheaders\", GetBlocksPayload(hash_start=[BC.Default().CurrentHeaderHash]))\n        self.SendSerializedMessage(get_headers_message)\n\n    def AskForMoreBlocks(self):\n\n        distance = BC.Default().HeaderHeight - BC.Default().Height\n\n        current_mode = self.sync_mode\n\n        if distance > 2000:\n            self.sync_mode = MODE_CATCHUP\n        else:\n            self.sync_mode = MODE_MAINTAIN\n\n        if self.sync_mode != current_mode:\n            logger.debug(f\"{self.prefix} changing sync_mode to {mode_to_name[self.sync_mode]}\")\n            self.stop_block_loop()\n            self.start_block_loop()\n\n        else:\n            if len(BC.Default().BlockRequests) > self.leader.BREQMAX:\n                logger.debug(f\"{self.prefix} data request speed exceeding node response rate...pausing to catch up\")\n                self.leader.throttle_sync()\n            else:\n                self.DoAskForMoreBlocks()\n\n    def DoAskForMoreBlocks(self):\n        hashes = []\n        hashstart = BC.Default().Height + 1\n        current_header_height = BC.Default().HeaderHeight + 1\n\n        do_go_ahead = False\n        if BC.Default().BlockSearchTries > 100 and len(BC.Default().BlockRequests) > 0:\n            do_go_ahead = True\n\n        first = None\n        while hashstart <= current_header_height and len(hashes) < self.leader.BREQPART:\n            hash = BC.Default().GetHeaderHash(hashstart)\n            if not do_go_ahead:\n                if hash is not None and hash not in BC.Default().BlockRequests \\\n                        and hash not in self.myblockrequests:\n\n                    if not first:\n                        first = hashstart\n                    BC.Default().BlockRequests.add(hash)\n                    self.myblockrequests.add(hash)\n                    hashes.append(hash)\n            else:\n                if hash is not None:\n                    if not first:\n                        first = hashstart\n                    BC.Default().BlockRequests.add(hash)\n                    self.myblockrequests.add(hash)\n                    hashes.append(hash)\n\n            hashstart += 1\n\n        if len(hashes) > 0:\n            logger.debug(\n                f\"{self.prefix} asking for more blocks {first} - {hashstart} ({len(hashes)}) stale count: {BC.Default().BlockSearchTries} \"\n                f\"BCRLen: {len(BC.Default().BlockRequests)}\")\n            self.health_check(HEARTBEAT_BLOCKS)\n            message = Message(\"getdata\", InvPayload(InventoryType.Block, hashes))\n            self.SendSerializedMessage(message)\n\n    def RequestPeerInfo(self):\n        \"\"\"Request the peer address information from the remote client.\"\"\"\n        logger.debug(f\"{self.prefix} requesting peer info\")\n        self.SendSerializedMessage(Message('getaddr'))\n\n    def HandlePeerInfoReceived(self, payload):\n        \"\"\"Process response of `self.RequestPeerInfo`.\"\"\"\n        addrs = IOHelper.AsSerializableWithType(payload, 'neo.Network.Payloads.AddrPayload.AddrPayload')\n\n        if not addrs:\n            return\n\n        for nawt in addrs.NetworkAddressesWithTime:\n            self.leader.RemoteNodePeerReceived(nawt.Address, nawt.Port, self.prefix)\n\n    def SendPeerInfo(self):\n        # if not self.leader.ServiceEnabled:\n        #     return\n\n        peerlist = []\n        for peer in self.leader.Peers:\n            addr = peer.GetNetworkAddressWithTime()\n            if addr is not None:\n                peerlist.append(addr)\n        peer_str_list = list(map(lambda p: p.ToString(), peerlist))\n        logger.debug(f\"{self.prefix} Sending Peer list {peer_str_list}\")\n\n        addrpayload = AddrPayload(addresses=peerlist)\n        message = Message('addr', addrpayload)\n        self.SendSerializedMessage(message)\n\n    def RequestVersion(self):\n        \"\"\"Request the remote client version.\"\"\"\n        m = Message(\"getversion\")\n        self.SendSerializedMessage(m)\n\n    def SendVersion(self):\n        \"\"\"Send our client version.\"\"\"\n        m = Message(\"version\", VersionPayload(settings.NODE_PORT, self.remote_nodeid, settings.VERSION_NAME))\n        self.SendSerializedMessage(m)\n\n    def SendVerack(self):\n        \"\"\"Send version acknowledge\"\"\"\n        m = Message('verack')\n        self.SendSerializedMessage(m)\n        self.expect_verack_next = True\n\n    def HandleVersion(self, payload):\n        \"\"\"Process the response of `self.RequestVersion`.\"\"\"\n        self.Version = IOHelper.AsSerializableWithType(payload, \"neo.Network.Payloads.VersionPayload.VersionPayload\")\n\n        if not self.Version:\n            return\n\n        if self.incoming_client:\n            if self.Version.Nonce == self.nodeid:\n                self.Disconnect()\n            self.SendVerack()\n        else:\n            self.nodeid = self.Version.Nonce\n            self.SendVersion()\n\n    def HandleVerack(self):\n        \"\"\"Handle the `verack` response.\"\"\"\n        m = Message('verack')\n        self.SendSerializedMessage(m)\n        self.leader.NodeCount += 1\n        self.identifier = self.leader.NodeCount\n        logger.debug(f\"{self.prefix} Handshake complete!\")\n        self.handshake_complete = True\n        self.ProtocolReady()\n\n    def HandleInvMessage(self, payload):\n        \"\"\"\n        Process a block header inventory payload.\n\n        Args:\n            inventory (neo.Network.Payloads.InvPayload):\n        \"\"\"\n\n        if self.sync_mode != MODE_MAINTAIN:\n            return\n\n        inventory = IOHelper.AsSerializableWithType(payload, 'neo.Network.Payloads.InvPayload.InvPayload')\n        if not inventory:\n            return\n\n        if inventory.Type == InventoryType.BlockInt:\n\n            ok_hashes = []\n            for hash in inventory.Hashes:\n                hash = hash.encode('utf-8')\n                if hash not in self.myblockrequests and hash not in BC.Default().BlockRequests:\n                    ok_hashes.append(hash)\n                    BC.Default().BlockRequests.add(hash)\n                    self.myblockrequests.add(hash)\n            if len(ok_hashes):\n                message = Message(\"getdata\", InvPayload(InventoryType.Block, ok_hashes))\n                self.SendSerializedMessage(message)\n\n        elif inventory.Type == InventoryType.TXInt:\n            pass\n        elif inventory.Type == InventoryType.ConsensusInt:\n            pass\n\n    def SendSerializedMessage(self, message):\n        \"\"\"\n        Send the `message` to the remote client.\n\n        Args:\n            message (neo.Network.Message):\n        \"\"\"\n        try:\n            ba = Helper.ToArray(message)\n            ba2 = binascii.unhexlify(ba)\n            self.bytes_out += len(ba2)\n            self.transport.write(ba2)\n        except Exception as e:\n            logger.debug(f\"Could not send serialized message {e}\")\n\n    def HandleBlockHeadersReceived(self, inventory):\n        \"\"\"\n        Process a block header inventory payload.\n\n        Args:\n            inventory (neo.Network.Inventory):\n        \"\"\"\n        try:\n            inventory = IOHelper.AsSerializableWithType(inventory, 'neo.Network.Payloads.HeadersPayload.HeadersPayload')\n            if inventory is not None:\n                logger.debug(f\"{self.prefix} received headers\")\n                self.heart_beat(HEARTBEAT_HEADERS)\n                BC.Default().AddHeaders(inventory.Headers)\n\n        except Exception as e:\n            logger.debug(f\"Error handling Block headers {e}\")\n\n    def HandleBlockReceived(self, inventory):\n        \"\"\"\n        Process a Block inventory payload.\n\n        Args:\n            inventory (neo.Network.Inventory):\n        \"\"\"\n        block = IOHelper.AsSerializableWithType(inventory, 'neo.Core.Block.Block')\n        if not block:\n            return\n\n        blockhash = block.Hash.ToBytes()\n        try:\n            if blockhash in BC.Default().BlockRequests:\n                BC.Default().BlockRequests.remove(blockhash)\n        except KeyError:\n            pass\n        try:\n            if blockhash in self.myblockrequests:\n                # logger.debug(f\"{self.prefix} received block: {block.Index}\")\n                self.heart_beat(HEARTBEAT_BLOCKS)\n                self.myblockrequests.remove(blockhash)\n        except KeyError:\n            pass\n        self.leader.InventoryReceived(block)\n\n    def time_expired(self, what):\n        now = datetime.datetime.utcnow().timestamp()\n        start_time = self.start_outstanding_data_request.get(what)\n        if start_time == 0:\n            delta = 0\n        else:\n            delta = now - start_time\n        return delta\n\n    def health_check(self, what):\n        # now = datetime.datetime.utcnow().timestamp()\n        # delta = now - self.start_outstanding_data_request.get(what)\n\n        time_expired = self.time_expired(what)\n\n        if time_expired == 0:\n            # startup scenario, just go\n            logger.debug(f\"{self.prefix}[HEALTH][{what}] startup or bcr catchup heart_beat\")\n            self.heart_beat(what)\n        else:\n            if self.sync_mode == MODE_CATCHUP:\n                response_threshold = 45  # seconds\n            else:\n                response_threshold = 90  #\n            if time_expired > response_threshold:\n                header_time = self.time_expired(HEARTBEAT_HEADERS)\n                header_bad = header_time > response_threshold\n                block_time = self.time_expired(HEARTBEAT_BLOCKS)\n                blocks_bad = block_time > response_threshold\n                if header_bad and blocks_bad:\n                    logger.debug(\n                        f\"{self.prefix}[HEALTH] FAILED - No response for Headers {header_time:.2f} and Blocks {block_time:.2f} seconds. Removing node...\")\n                    self.Disconnect()\n                elif blocks_bad and self.leader.check_bcr_loop and self.leader.check_bcr_loop.running:\n                    # when we're in data throttling it is never acceptable if blocks don't come in. \n                    logger.debug(\n                        f\"{self.prefix}[HEALTH] FAILED - No Blocks for {block_time:.2f} seconds while throttling. Removing node...\")\n                    self.Disconnect()\n                else:\n                    if header_bad:\n                        logger.debug(\n                            f\"{self.prefix}[HEALTH] Headers FAILED @ {header_time:.2f}s, but Blocks OK @ {block_time:.2f}s. Keeping node...\")\n                    else:\n                        logger.debug(\n                            f\"{self.prefix}[HEALTH] Headers OK @ {header_time:.2f}s, but Blocks FAILED @ {block_time:.2f}s. Keeping node...\")\n\n                # logger.debug(\n                #     f\"{self.prefix}[HEALTH][{what}] FAILED - No response for {time_expired:.2f} seconds. Removing node...\")\n\n            else:\n                logger.debug(f\"{self.prefix}[HEALTH][{what}] OK - response time {time_expired:.2f}\")\n\n    def heart_beat(self, what):\n        self.start_outstanding_data_request[what] = datetime.datetime.utcnow().timestamp()\n\n    def HandleGetHeadersMessageReceived(self, payload):\n\n        if not self.leader.ServiceEnabled:\n            return\n\n        inventory = IOHelper.AsSerializableWithType(payload, 'neo.Network.Payloads.GetBlocksPayload.GetBlocksPayload')\n\n        if not inventory:\n            return\n\n        blockchain = BC.Default()\n\n        hash = inventory.HashStart[0]\n\n        if hash is None or hash == inventory.HashStop:\n            logger.debug(\"getheaders: Hash {} not found or hashstop reached\".format(inventory.HashStart))\n            return\n\n        headers = []\n        header_count = 0\n\n        while hash != inventory.HashStop and header_count < 2000:\n            hash = blockchain.GetNextBlockHash(hash)\n            if not hash:\n                break\n            headers.append(blockchain.GetHeader(hash))\n            header_count += 1\n\n        if header_count > 0:\n            self.SendSerializedMessage(Message('headers', HeadersPayload(headers=headers)))\n\n    def HandleBlockReset(self, hash):\n        \"\"\"Process block reset request.\"\"\"\n        self.myblockrequests = set()\n\n    def HandleGetDataMessageReceived(self, payload):\n        \"\"\"\n        Process a InvPayload payload.\n\n        Args:\n            payload (neo.Network.Inventory):\n        \"\"\"\n        inventory = IOHelper.AsSerializableWithType(payload, 'neo.Network.Payloads.InvPayload.InvPayload')\n        if not inventory:\n            return\n\n        for hash in inventory.Hashes:\n            hash = hash.encode('utf-8')\n\n            item = None\n            # try to get the inventory to send from relay cache\n\n            if hash in self.leader.RelayCache.keys():\n                item = self.leader.RelayCache[hash]\n\n            if inventory.Type == InventoryType.TXInt:\n                if not item:\n                    item, index = BC.Default().GetTransaction(hash)\n                if not item:\n                    item = self.leader.GetTransaction(hash)\n                if item:\n                    message = Message(command='tx', payload=item, print_payload=False)\n                    self.SendSerializedMessage(message)\n\n            elif inventory.Type == InventoryType.BlockInt:\n                if not item:\n                    item = BC.Default().GetBlock(hash)\n                if item:\n                    message = Message(command='block', payload=item, print_payload=False)\n                    self.SendSerializedMessage(message)\n\n            elif inventory.Type == InventoryType.ConsensusInt:\n                if item:\n                    self.SendSerializedMessage(Message(command='consensus', payload=item, print_payload=False))\n\n    def HandleGetBlocksMessageReceived(self, payload):\n        \"\"\"\n        Process a GetBlocksPayload payload.\n\n        Args:\n            payload (neo.Network.Payloads.GetBlocksPayload):\n        \"\"\"\n        if not self.leader.ServiceEnabled:\n            return\n\n        inventory = IOHelper.AsSerializableWithType(payload, 'neo.Network.Payloads.GetBlocksPayload.GetBlocksPayload')\n        if not inventory:\n            return\n\n        blockchain = BC.Default()\n        hash = inventory.HashStart[0]\n        if not blockchain.GetHeader(hash):\n            return\n\n        hashes = []\n        hcount = 0\n        while hash != inventory.HashStop and hcount < 500:\n            hash = blockchain.GetNextBlockHash(hash)\n            if hash is None:\n                break\n            hashes.append(hash)\n            hcount += 1\n        if hcount > 0:\n            self.SendSerializedMessage(Message('inv', InvPayload(type=InventoryType.Block, hashes=hashes)))\n\n    def Relay(self, inventory):\n        \"\"\"\n        Wrap the inventory in a InvPayload object and send it over the write to the remote node.\n\n        Args:\n            inventory:\n\n        Returns:\n            bool: True (fixed)\n        \"\"\"\n        inventory = InvPayload(type=inventory.InventoryType, hashes=[inventory.Hash.ToBytes()])\n        m = Message(\"inv\", inventory)\n        self.SendSerializedMessage(m)\n\n        return True\n\n    def __eq__(self, other):\n        if type(other) is type(self):\n            return self.address == other.address and self.identifier == other.identifier\n        else:\n            return False\n","sub_path":"neo/Network/NeoNode.py","file_name":"NeoNode.py","file_ext":"py","file_size_in_byte":33637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"244034409","text":"from random import*\ndef probability(N):\n     counter=0\n     for i in range(N):\n          r = random()\n          if r >= 0.5 and r <= 0.6:\n               counter+=1\n     return counter, counter/float(N)\n\nfor i in [1,2,3,6]:\n     N = 10**i\n     print(N,probability(N))\n\n\"\"\"\nTerminal:-:python python3 compute_prob.py\n10 (0, 0.0)\n100 (10, 0.1)\n1000 (89, 0.089)\n1000000 (99780, 0.09978)\n\n\"\"\"         \n\n\n\n\n\n\n\n     \n\n\n\n     \n","sub_path":"Oblig/compute_prob.py","file_name":"compute_prob.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"649347343","text":"from __future__ import division\r\nimport sfml as sf\r\nimport itertools\r\nimport renderer\r\nimport windows\r\nimport game\r\nimport time\r\nimport ui\r\n\r\nclass SceneUI:\r\n    def __init__(self):\r\n        self.mouse_queue = []\r\n        self.contents = []\r\n\r\n\r\nclass MapSceneUI(SceneUI):\r\n    def __init__(self):\r\n        SceneUI.__init__(self)\r\n        self.windows = ui.map_scene_windows\r\n        self.windows.append(windows.MouseHoverArea())\r\n        self.windows.append(windows.Minimap())\r\n        self.windows.append(windows.PathBar())\r\n\r\n    def process_input(self, event, pos, gm):\r\n        if event.pressed == True:\r\n            click_target_node = self.get_click_target(pos)\r\n            if gm.scenes['map'].contents['player'].target_node is not None:\r\n                gm.scenes['map'].contents['player'].path = gm.scenes['map'].get_path(\r\n                    gm.scenes['map'].contents['player'].target_node,\r\n                    (click_target_node[0], click_target_node[1])\r\n                    )\r\n            else:\r\n                gm.scenes['map'].contents['player'].path = gm.scenes['map'].get_path(\r\n                    gm.scenes['map'].contents['player'].node,\r\n                    (click_target_node[0], click_target_node[1])\r\n                    )\r\n\r\n    def get_click_target(self, pos):\r\n        for di in ui.map_rects.values():\r\n            if di['rect'].contains(pos):\r\n                if not di['node'] in game.removed_nodes:\r\n                    return di['node']\r\n                else:\r\n                    if (abs(di['node'][0] - game.player.node[0]) <\r\n                        abs(di['node'][1] - game.player.node[1])):\r\n                        first = 1\r\n                        second = 0\r\n                    else:\r\n                        first = 0\r\n                        second = 1\r\n                    slope = ((di['node'][second] - game.player.node[second]) /\r\n                             (di['node'][first] - game.player.node[first]))\r\n                    intercept = game.player.node[second] - (slope * game.player.node[first])\r\n                    if di['node'][first] > game.player.node[first]:\r\n                        step = -1\r\n                    else:\r\n                        step = 1\r\n                    for x in xrange(di['node'][first], game.player.node[first] + step, step):\r\n                        y = int(slope * x + intercept)\r\n                        if first == 0:\r\n                            node = (x, y)\r\n                        else:\r\n                            node = (y, x)\r\n                        if not node in game.removed_nodes:\r\n                            return node\r\n        return game.player.node\r\n\r\n    def update(self, delta, window, gm):\r\n        for wnd in self.windows:\r\n            wnd.update()\r\n        #if self.mouse_hovered_node == None:\r\n        #    self.mouse_hovered_node = game.player.node\r\n        pos = window.map_pixel_to_coords(sf.Mouse.get_position(window), window.view)\r\n        ui.mouse_pos = pos\r\n        #for di in ui.map_rects.values():\r\n        #    if di['rect'].contains(pos):\r\n        #        if di['node'] is not None:\r\n        #            self.mouse_hovered_node = di['node']\r\n        if len(self.mouse_queue) > 0:\r\n            self.process_input(self.mouse_queue.pop(0), pos, gm)\r\n\r\n\r\n        #mouse_coords = window.map_pixel_to_coords(sf.Mouse.get_position(window), window.view)\r\n        # for di in ui.map_rects.values():\r\n        #     if self.mouse_cooldown == True:\r\n        #         self.mouse_cooldown_elapsed += delta\r\n        #         if self.mouse_cooldown_elapsed > 0.5:\r\n        #             self.mouse_cooldown = False\r\n        #             self.mouse_cooldown_elapsed = 0\r\n        #         return\r\n        #     if self.mouse_cooldown == False:\r\n        #         if (di['rect'].contains(mouse_coords)\r\n        #             and sf.Mouse.is_button_pressed(sf.Mouse.LEFT)\r\n        #             and gm.scenes['map'].contents['player'].moving == False):\r\n        #             start = time.time()\r\n        #             self.mouse_cooldown = True\r\n        #             print('moving to %d, %d' % (di['node'][0], di['node'][1]))\r\n        #             gm.scenes['map'].contents['player'].path = gm.scenes['map'].get_path(\r\n        #                 gm.scenes['map'].contents['player'].node,\r\n        #                 (di['node'][0], di['node'][1])\r\n        #             )\r\n        #             finish = time.time()\r\n        #             print('completed in %.5f' % (finish - start))\r\n\r\n\r\n\r\nclass BattleSceneUI(SceneUI):\r\n    def __init__(self):\r\n        SceneUI.__init__(self)\r\n\r\n    def update(self, delta, window, gm):\r\n        pass\r\n","sub_path":"ui/scenes.py","file_name":"scenes.py","file_ext":"py","file_size_in_byte":4650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"32189687","text":"# coding: utf-8\n\n\"\"\"\n    LiveAgent API\n\n    This page contains complete API documentation for LiveAgent software. To display additional info and examples for specific API method, just click on the method name in the list below.<br/><br/>To be able to make API requests you need to generate an API key in your admin panel first. [See this article for detailed info.](https://support.liveagent.com/741982-API-key)<br/><br/>Additional info about more advanced agent, contact or ticket API filters can be found [in this article](https://support.liveagent.com/513528-APIv3-advanced-filter-examples).<br/><br/>If you have any question or doubts regarding this API, please do not hesitate to contact our support team.  # noqa: E501\n\n    OpenAPI spec version: 3.0.0\n    Contact: support@qualityunit.com\n    Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nimport pprint\nimport re  # noqa: F401\n\nimport six\n\n\nclass TimeReportRow(object):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'id': 'int',\n        'agentid': 'str',\n        'agent': 'str',\n        'conversationid': 'str',\n        'ticket': 'str',\n        'parent_contactid': 'str',\n        'company': 'str',\n        'contactid': 'str',\n        'customer': 'str',\n        'customer_firstname': 'str',\n        'customer_lastname': 'str',\n        'customer_systemname': 'str',\n        'agent_ticket': 'str',\n        'reportdate': 'str',\n        'reportedtime': 'str',\n        'reportedtime_hhmmss': 'str',\n        'spenttime': 'str',\n        'dateinvoiced': 'str',\n        'note': 'str',\n        'type': 'str'\n    }\n\n    attribute_map = {\n        'id': 'id',\n        'agentid': 'agentid',\n        'agent': 'agent',\n        'conversationid': 'conversationid',\n        'ticket': 'ticket',\n        'parent_contactid': 'parent_contactid',\n        'company': 'company',\n        'contactid': 'contactid',\n        'customer': 'customer',\n        'customer_firstname': 'customer_firstname',\n        'customer_lastname': 'customer_lastname',\n        'customer_systemname': 'customer_systemname',\n        'agent_ticket': 'agent_ticket',\n        'reportdate': 'reportdate',\n        'reportedtime': 'reportedtime',\n        'reportedtime_hhmmss': 'reportedtime_hhmmss',\n        'spenttime': 'spenttime',\n        'dateinvoiced': 'dateinvoiced',\n        'note': 'note',\n        'type': 'type'\n    }\n\n    def __init__(self, id=None, agentid=None, agent=None, conversationid=None, ticket=None, parent_contactid=None, company=None, contactid=None, customer=None, customer_firstname=None, customer_lastname=None, customer_systemname=None, agent_ticket=None, reportdate=None, reportedtime=None, reportedtime_hhmmss=None, spenttime=None, dateinvoiced=None, note=None, type=None):  # noqa: E501\n        \"\"\"TimeReportRow - a model defined in Swagger\"\"\"  # noqa: E501\n\n        self._id = None\n        self._agentid = None\n        self._agent = None\n        self._conversationid = None\n        self._ticket = None\n        self._parent_contactid = None\n        self._company = None\n        self._contactid = None\n        self._customer = None\n        self._customer_firstname = None\n        self._customer_lastname = None\n        self._customer_systemname = None\n        self._agent_ticket = None\n        self._reportdate = None\n        self._reportedtime = None\n        self._reportedtime_hhmmss = None\n        self._spenttime = None\n        self._dateinvoiced = None\n        self._note = None\n        self._type = None\n        self.discriminator = None\n\n        if id is not None:\n            self.id = id\n        if agentid is not None:\n            self.agentid = agentid\n        if agent is not None:\n            self.agent = agent\n        if conversationid is not None:\n            self.conversationid = conversationid\n        if ticket is not None:\n            self.ticket = ticket\n        if parent_contactid is not None:\n            self.parent_contactid = parent_contactid\n        if company is not None:\n            self.company = company\n        if contactid is not None:\n            self.contactid = contactid\n        if customer is not None:\n            self.customer = customer\n        if customer_firstname is not None:\n            self.customer_firstname = customer_firstname\n        if customer_lastname is not None:\n            self.customer_lastname = customer_lastname\n        if customer_systemname is not None:\n            self.customer_systemname = customer_systemname\n        if agent_ticket is not None:\n            self.agent_ticket = agent_ticket\n        if reportdate is not None:\n            self.reportdate = reportdate\n        if reportedtime is not None:\n            self.reportedtime = reportedtime\n        if reportedtime_hhmmss is not None:\n            self.reportedtime_hhmmss = reportedtime_hhmmss\n        if spenttime is not None:\n            self.spenttime = spenttime\n        if dateinvoiced is not None:\n            self.dateinvoiced = dateinvoiced\n        if note is not None:\n            self.note = note\n        if type is not None:\n            self.type = type\n\n    @property\n    def id(self):\n        \"\"\"Gets the id of this TimeReportRow.  # noqa: E501\n\n\n        :return: The id of this TimeReportRow.  # noqa: E501\n        :rtype: int\n        \"\"\"\n        return self._id\n\n    @id.setter\n    def id(self, id):\n        \"\"\"Sets the id of this TimeReportRow.\n\n\n        :param id: The id of this TimeReportRow.  # noqa: E501\n        :type: int\n        \"\"\"\n\n        self._id = id\n\n    @property\n    def agentid(self):\n        \"\"\"Gets the agentid of this TimeReportRow.  # noqa: E501\n\n\n        :return: The agentid of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._agentid\n\n    @agentid.setter\n    def agentid(self, agentid):\n        \"\"\"Sets the agentid of this TimeReportRow.\n\n\n        :param agentid: The agentid of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._agentid = agentid\n\n    @property\n    def agent(self):\n        \"\"\"Gets the agent of this TimeReportRow.  # noqa: E501\n\n\n        :return: The agent of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._agent\n\n    @agent.setter\n    def agent(self, agent):\n        \"\"\"Sets the agent of this TimeReportRow.\n\n\n        :param agent: The agent of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._agent = agent\n\n    @property\n    def conversationid(self):\n        \"\"\"Gets the conversationid of this TimeReportRow.  # noqa: E501\n\n\n        :return: The conversationid of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._conversationid\n\n    @conversationid.setter\n    def conversationid(self, conversationid):\n        \"\"\"Sets the conversationid of this TimeReportRow.\n\n\n        :param conversationid: The conversationid of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._conversationid = conversationid\n\n    @property\n    def ticket(self):\n        \"\"\"Gets the ticket of this TimeReportRow.  # noqa: E501\n\n\n        :return: The ticket of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._ticket\n\n    @ticket.setter\n    def ticket(self, ticket):\n        \"\"\"Sets the ticket of this TimeReportRow.\n\n\n        :param ticket: The ticket of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._ticket = ticket\n\n    @property\n    def parent_contactid(self):\n        \"\"\"Gets the parent_contactid of this TimeReportRow.  # noqa: E501\n\n\n        :return: The parent_contactid of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._parent_contactid\n\n    @parent_contactid.setter\n    def parent_contactid(self, parent_contactid):\n        \"\"\"Sets the parent_contactid of this TimeReportRow.\n\n\n        :param parent_contactid: The parent_contactid of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._parent_contactid = parent_contactid\n\n    @property\n    def company(self):\n        \"\"\"Gets the company of this TimeReportRow.  # noqa: E501\n\n\n        :return: The company of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._company\n\n    @company.setter\n    def company(self, company):\n        \"\"\"Sets the company of this TimeReportRow.\n\n\n        :param company: The company of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._company = company\n\n    @property\n    def contactid(self):\n        \"\"\"Gets the contactid of this TimeReportRow.  # noqa: E501\n\n\n        :return: The contactid of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._contactid\n\n    @contactid.setter\n    def contactid(self, contactid):\n        \"\"\"Sets the contactid of this TimeReportRow.\n\n\n        :param contactid: The contactid of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._contactid = contactid\n\n    @property\n    def customer(self):\n        \"\"\"Gets the customer of this TimeReportRow.  # noqa: E501\n\n\n        :return: The customer of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._customer\n\n    @customer.setter\n    def customer(self, customer):\n        \"\"\"Sets the customer of this TimeReportRow.\n\n\n        :param customer: The customer of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._customer = customer\n\n    @property\n    def customer_firstname(self):\n        \"\"\"Gets the customer_firstname of this TimeReportRow.  # noqa: E501\n\n\n        :return: The customer_firstname of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._customer_firstname\n\n    @customer_firstname.setter\n    def customer_firstname(self, customer_firstname):\n        \"\"\"Sets the customer_firstname of this TimeReportRow.\n\n\n        :param customer_firstname: The customer_firstname of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._customer_firstname = customer_firstname\n\n    @property\n    def customer_lastname(self):\n        \"\"\"Gets the customer_lastname of this TimeReportRow.  # noqa: E501\n\n\n        :return: The customer_lastname of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._customer_lastname\n\n    @customer_lastname.setter\n    def customer_lastname(self, customer_lastname):\n        \"\"\"Sets the customer_lastname of this TimeReportRow.\n\n\n        :param customer_lastname: The customer_lastname of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._customer_lastname = customer_lastname\n\n    @property\n    def customer_systemname(self):\n        \"\"\"Gets the customer_systemname of this TimeReportRow.  # noqa: E501\n\n\n        :return: The customer_systemname of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._customer_systemname\n\n    @customer_systemname.setter\n    def customer_systemname(self, customer_systemname):\n        \"\"\"Sets the customer_systemname of this TimeReportRow.\n\n\n        :param customer_systemname: The customer_systemname of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._customer_systemname = customer_systemname\n\n    @property\n    def agent_ticket(self):\n        \"\"\"Gets the agent_ticket of this TimeReportRow.  # noqa: E501\n\n\n        :return: The agent_ticket of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._agent_ticket\n\n    @agent_ticket.setter\n    def agent_ticket(self, agent_ticket):\n        \"\"\"Sets the agent_ticket of this TimeReportRow.\n\n\n        :param agent_ticket: The agent_ticket of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._agent_ticket = agent_ticket\n\n    @property\n    def reportdate(self):\n        \"\"\"Gets the reportdate of this TimeReportRow.  # noqa: E501\n\n\n        :return: The reportdate of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._reportdate\n\n    @reportdate.setter\n    def reportdate(self, reportdate):\n        \"\"\"Sets the reportdate of this TimeReportRow.\n\n\n        :param reportdate: The reportdate of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._reportdate = reportdate\n\n    @property\n    def reportedtime(self):\n        \"\"\"Gets the reportedtime of this TimeReportRow.  # noqa: E501\n\n\n        :return: The reportedtime of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._reportedtime\n\n    @reportedtime.setter\n    def reportedtime(self, reportedtime):\n        \"\"\"Sets the reportedtime of this TimeReportRow.\n\n\n        :param reportedtime: The reportedtime of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._reportedtime = reportedtime\n\n    @property\n    def reportedtime_hhmmss(self):\n        \"\"\"Gets the reportedtime_hhmmss of this TimeReportRow.  # noqa: E501\n\n\n        :return: The reportedtime_hhmmss of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._reportedtime_hhmmss\n\n    @reportedtime_hhmmss.setter\n    def reportedtime_hhmmss(self, reportedtime_hhmmss):\n        \"\"\"Sets the reportedtime_hhmmss of this TimeReportRow.\n\n\n        :param reportedtime_hhmmss: The reportedtime_hhmmss of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._reportedtime_hhmmss = reportedtime_hhmmss\n\n    @property\n    def spenttime(self):\n        \"\"\"Gets the spenttime of this TimeReportRow.  # noqa: E501\n\n\n        :return: The spenttime of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._spenttime\n\n    @spenttime.setter\n    def spenttime(self, spenttime):\n        \"\"\"Sets the spenttime of this TimeReportRow.\n\n\n        :param spenttime: The spenttime of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._spenttime = spenttime\n\n    @property\n    def dateinvoiced(self):\n        \"\"\"Gets the dateinvoiced of this TimeReportRow.  # noqa: E501\n\n\n        :return: The dateinvoiced of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._dateinvoiced\n\n    @dateinvoiced.setter\n    def dateinvoiced(self, dateinvoiced):\n        \"\"\"Sets the dateinvoiced of this TimeReportRow.\n\n\n        :param dateinvoiced: The dateinvoiced of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._dateinvoiced = dateinvoiced\n\n    @property\n    def note(self):\n        \"\"\"Gets the note of this TimeReportRow.  # noqa: E501\n\n\n        :return: The note of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._note\n\n    @note.setter\n    def note(self, note):\n        \"\"\"Sets the note of this TimeReportRow.\n\n\n        :param note: The note of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n\n        self._note = note\n\n    @property\n    def type(self):\n        \"\"\"Gets the type of this TimeReportRow.  # noqa: E501\n\n\n        :return: The type of this TimeReportRow.  # noqa: E501\n        :rtype: str\n        \"\"\"\n        return self._type\n\n    @type.setter\n    def type(self, type):\n        \"\"\"Sets the type of this TimeReportRow.\n\n\n        :param type: The type of this TimeReportRow.  # noqa: E501\n        :type: str\n        \"\"\"\n        allowed_values = [\"auto\", \"manual\"]  # noqa: E501\n        if type not in allowed_values:\n            raise ValueError(\n                \"Invalid value for `type` ({0}), must be one of {1}\"  # noqa: E501\n                .format(type, allowed_values)\n            )\n\n        self._type = type\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.swagger_types):\n            value = getattr(self, attr)\n            if isinstance(value, list):\n                result[attr] = list(map(\n                    lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                    value\n                ))\n            elif hasattr(value, \"to_dict\"):\n                result[attr] = value.to_dict()\n            elif isinstance(value, dict):\n                result[attr] = dict(map(\n                    lambda item: (item[0], item[1].to_dict())\n                    if hasattr(item[1], \"to_dict\") else item,\n                    value.items()\n                ))\n            else:\n                result[attr] = value\n        if issubclass(TimeReportRow, dict):\n            for key, value in self.items():\n                result[key] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"Returns the string representation of the model\"\"\"\n        return pprint.pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, TimeReportRow):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","sub_path":"liveagent_api/models/time_report_row.py","file_name":"time_report_row.py","file_ext":"py","file_size_in_byte":17537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"374441126","text":"# exmaple of early stopping\nfrom numpy import loadtxt\nfrom xgboost import XGBClassifier\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.metrics import accuracy_score\n# load data\n# load data\nurl = \"../Datasets/stage_1_dataset_05.csv\"\ndataset = loadtxt(url, delimiter=\"|\")\n# split data into X and y\nX = dataset[:,0:22]\nY = dataset[:,22]\n# split data into train and test sets\nseed=7\ntest_size = 0.33\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size,\n    random_state=seed)\n# fit model no training data\nmodel = XGBClassifier()\neval_set = [(X_test, y_test)]\nmodel.fit(X_train, y_train, early_stopping_rounds=10, eval_metric=\"logloss\",\n    eval_set=eval_set, verbose=True)\n# make predictions for test data\ny_pred = model.predict(X_test)\npredictions = [round(value) for value in y_pred]\n# evaluate predictions\naccuracy = accuracy_score(y_test, predictions)\nprint(\"Accuracy: %.2f%%\" % (accuracy * 100.0))","sub_path":"stage1/xgboost_early_stopping.py","file_name":"xgboost_early_stopping.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"581146118","text":"import time\nimport logging\nfrom importlib import import_module\n\nimport deepquant.common.error as error\n\n# create logger\nlogging.basicConfig(format='%(message)s')\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\nch = logging.StreamHandler()\nch.setLevel(logging.DEBUG)\nlogger.addHandler(ch)\n\ndef get_dataset(bar_time, dataset_location, broker_id, model_name, symbol_name, timeframe, dataset_router_modulename=None):\n    dataset = None\n\n    try:\n        if dataset_location.lower() == 'local':\n            # Load dataset router module dynamically at runtime (Runtime binding)\n            dataset_router = import_module(dataset_router_modulename)\n\n            # Get dataset, dataset type is pandas DataFrame\n            is_new_dataset = False\n            wait_time = 0.5  # wait 0.5 second\n            max_retry = 60  # 30 second\n            retry_num = 0\n            while is_new_dataset == False and retry_num < max_retry:\n                dataset = dataset_router.get_dataset(broker_id, model_name, symbol_name, timeframe)\n                # Validate bar time of last row (last bar)\n                if dataset.iloc[-1, 0] == bar_time:\n                    is_new_dataset = True\n                else:\n                    time.sleep(wait_time)\n                    retry_num += 1\n\n            if retry_num == max_retry:\n                raise error.PredictiveModelError('Has no new dataset for {} - {} - {},  retry exceed max retry: {}'.format(\\\n                    model_name, symbol_name, timeframe, retry_num))\n\n    except error.PredictiveModelError as pe:\n        raise error.PredictiveModelError(pe)\n    except Exception as e:\n        raise Exception('Unexpected error in getting dataset by dataset gateway: {}'.format(e))\n\n    return dataset\n\n    \"\"\"\n    def backfill_dataset(bar_time, dataset_location, broker_id, model_name, symbol_name, timeframe, dataset_router_modulename=None):\n        dataset = None\n        \n        # Will add code later for import performance using Eager Acquisition Pattern\n\n        dt_format = '%Y-%m-%d %H:%M:%S'\n        d1 = datetime.datetime.strptime('2019-04-01 17:35:00', dt_format)\n        d2 = datetime.datetime.strptime('2019-05-27 14:40:00', dt_format)\n        minutes_diff = (d2 - d1).total_seconds() / 60.0\n        # Check\n\n        return dataset\n    \"\"\"\n","sub_path":"deepquant/common/dataset_gateway.py","file_name":"dataset_gateway.py","file_ext":"py","file_size_in_byte":2310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"611601458","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nfrom PyQt5.QtGui import *\n\n\nclass EditAuthorWindow(object):\n    def __init__(self, parent, author):\n        self.__par = parent\n        self.__author = author\n    def setupUi(self, MainWindow):\n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(600, 333)\n        MainWindow.setMinimumSize(QtCore.QSize(600, 333))\n        MainWindow.setMaximumSize(QtCore.QSize(600, 333))\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.label = QtWidgets.QLabel(self.centralwidget)\n        self.label.setGeometry(QtCore.QRect(104, 0, 391, 51))\n        self.label.setStyleSheet(\"font: 75 16pt \\\"MS Shell Dlg 2\\\";\")\n        self.label.setObjectName(\"label\")\n        self.pushButton = QtWidgets.QPushButton(self.centralwidget)\n        self.pushButton.setGeometry(QtCore.QRect(180, 270, 231, 51))\n        self.pushButton.setStyleSheet(\"font: 75 14pt \\\"MS Shell Dlg 2\\\";\")\n        self.pushButton.setObjectName(\"pushButton\")\n        self.pushButton.clicked.connect(self.update)\n        self.label_2 = QtWidgets.QLabel(self.centralwidget)\n        self.label_2.setGeometry(QtCore.QRect(20, 70, 161, 21))\n        self.label_2.setStyleSheet(\"font: 12pt \\\"MS Shell Dlg 2\\\";\")\n        self.label_2.setObjectName(\"label_2\")\n        self.lineEdit = QtWidgets.QLineEdit(self.centralwidget)\n        self.lineEdit.setGeometry(QtCore.QRect(190, 70, 211, 20))\n        self.lineEdit.setObjectName(\"lineEdit\")\n        self.label_3 = QtWidgets.QLabel(self.centralwidget)\n        self.label_3.setGeometry(QtCore.QRect(20, 120, 181, 21))\n        self.label_3.setStyleSheet(\"font: 12pt \\\"MS Shell Dlg 2\\\";\")\n        self.label_3.setObjectName(\"label_3\")\n        self.lineEdit_2 = QtWidgets.QLineEdit(self.centralwidget)\n        self.lineEdit_2.setGeometry(QtCore.QRect(220, 120, 211, 20))\n        self.lineEdit_2.setObjectName(\"lineEdit_2\")\n        self.label_4 = QtWidgets.QLabel(self.centralwidget)\n        self.label_4.setGeometry(QtCore.QRect(20, 170, 211, 21))\n        self.label_4.setStyleSheet(\"font: 12pt \\\"MS Shell Dlg 2\\\";\")\n        self.label_4.setObjectName(\"label_4\")\n        self.lineEdit_3 = QtWidgets.QLineEdit(self.centralwidget)\n        self.lineEdit_3.setGeometry(QtCore.QRect(250, 170, 211, 20))\n        self.lineEdit_3.setObjectName(\"lineEdit_3\")\n        self.load()\n        self.main = MainWindow\n        MainWindow.setCentralWidget(self.centralwidget)        \n        self.retranslateUi(MainWindow)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n        \n    def load(self):\n        try:\n            self.lineEdit.setText(self.__author['name'])\n            self.lineEdit_2.setText(self.__author['country'])\n            self.lineEdit_3.setText(self.__author['years'])\n        except:\n            pass\n    def update(self):\n       try:\n            name = self.lineEdit.text()\n            country = self.lineEdit_2.text()\n            years = self.lineEdit_3.text()\n            self.__par.authors.update({'name' : self.__author['name'], 'country' : self.__author['country'], 'years' : self.__author['years']}, {'$set' : {'name' : name, 'country' : country, 'years' : years}})            \n            self.__par.update_view()\n            QMessageBox.information(None, 'Успех', 'Данные обновлены')\n            self.main.close()\n       except:\n            pass\n    def retranslateUi(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MainWindow\"))\n        self.label.setText(_translate(\"MainWindow\", \"Редактировать информацию об авторе\"))\n        self.pushButton.setText(_translate(\"MainWindow\", \"Редактировать\"))\n        self.label_2.setText(_translate(\"MainWindow\", \"Введите имя автора\"))\n        self.label_3.setText(_translate(\"MainWindow\", \"Введите страну автора\"))\n        self.label_4.setText(_translate(\"MainWindow\", \"Введите годы жизни автора\"))\n\n","sub_path":"Lab4/ui/EditAuthorFormNoSql.py","file_name":"EditAuthorFormNoSql.py","file_ext":"py","file_size_in_byte":4192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"2112669","text":"from __future__ import annotations\n\nimport logging\nfrom io import BufferedReader\nfrom enum import IntEnum\nfrom typing import ClassVar, List\nfrom DyldExtractor.Structure import Structure\n\n\n__all__ = [\n\t\"Slide\",\n\t\"dyld_cache_header\",\n\t\"dyld_cache_image_info\",\n\t\"dyld_cache_local_symbols_entry\",\n\t\"dyld_cache_local_symbols_info\",\n\t\"dyld_cache_mapping_info\",\n\t\"dyld_cache_slide_info2\",\n\t\"dyld_cache_slide_info3\",\n\t\"dyld_cache_mapping_and_slide_info\",\n\t\"dyld_cache_slide_pointer3_plain\",\n\t\"dyld_cache_slide_pointer3_auth\",\n\t\"dyld_cache_slide_pointer3\"\n]\n\n\nclass Slide(IntEnum):\n\tDYLD_CACHE_SLIDE_PAGE_ATTRS = 0xC000 \t\t\t# high bits of uint16_t are flags\n\tDYLD_CACHE_SLIDE_PAGE_ATTR_EXTRA = 0x8000 \t\t# index is into extras array (not starts array)\n\tDYLD_CACHE_SLIDE_PAGE_ATTR_NO_REBASE = 0x4000 \t# page has no rebasing\n\tDYLD_CACHE_SLIDE_PAGE_ATTR_END = 0x8000 \t\t# last chain entry for page\n\n\tDYLD_CACHE_SLIDE_V3_PAGE_ATTR_NO_REBASE = 0xFFF # page has no rebasing\n\n\nclass dyld_cache_header(Structure):\n\t\n\theaderType: int\n\n\tmagic: bytes\t\t\t\t\t# e.g. \"dyld_v0    i386\"\n\tmappingOffset: int\t\t\t\t# file offset to first dyld_cache_mapping_info\n\tmappingCount: int\t\t\t\t# number of dyld_cache_mapping_info entries\n\timagesOffset: int\t\t\t\t# file offset to first dyld_cache_image_info\n\timagesCount: int\t\t\t\t# number of dyld_cache_image_info entries\n\tdyldBaseAddress: int\t\t\t# base address of dyld when cache was built\n\tcodeSignatureOffset: int\t\t# file offset of code signature blob\n\tcodeSignatureSize: int\t\t\t# size of code signature blob (zero means to end of file)\n\tslideInfoOffset: int\t\t\t# file offset of kernel slid info\n\tslideInfoSize: int\t\t\t\t# size of kernel slid info\n\tlocalSymbolsOffset: int\t\t\t# file offset of where local symbols are stored\n\tlocalSymbolsSize: int\t\t\t# size of local symbols information\n\tuuid: bytes\t\t\t\t\t\t# unique value for each shared cache file\n\tcacheType: int\t\t\t\t\t# 0 for development, 1 for production\n\tbranchPoolsOffset: int\t\t\t# file offset to table of uint64_t pool addresses\n\tbranchPoolsCount: int\t\t\t# number of uint64_t entries\n\taccelerateInfoAddr: int\t\t\t# (unslid) address of optimization info\n\taccelerateInfoSize: int\t\t\t# size of optimization info\n\timagesTextOffset: int\t\t\t# file offset to first dyld_cache_image_text_info\n\timagesTextCount: int\t\t\t# number of dyld_cache_image_text_info entries\n\tpatchInfoAddr: int \t\t\t\t# (unslid) address of dyld_cache_patch_info\n\tpatchInfoSize: int \t\t\t\t# Size of all of the patch information pointed to via the dyld_cache_patch_info\n\totherImageGroupAddrUnused: int \t# unused\n\totherImageGroupSizeUnused: int \t# unused\n\tprogClosuresAddr: int \t\t\t# (unslid) address of list of program launch closures\n\tprogClosuresSize: int \t\t\t# size of list of program launch closures\n\tprogClosuresTrieAddr: int \t\t# (unslid) address of trie of indexes into program launch closures\n\tprogClosuresTrieSize: int \t\t# size of trie of indexes into program launch closures\n\tplatform: int \t\t\t\t\t# platform number (macOS=1, etc)\n\tCacheInfoBitfield: int \t\t\t# a bitfield\n\t\t# formatVersion          : 8,  # dyld3::closure::kFormatVersion\n\t\t# dylibsExpectedOnDisk   : 1,  # dyld should expect the dylib exists on disk and to compare inode/mtime to see if cache is valid\n\t\t# simulator              : 1,  # for simulator of specified platform\n\t\t# locallyBuiltCache      : 1,  # 0 for B&I built cache, 1 for locally built cache\n\t\t# builtFromChainedFixups : 1,  # some dylib in cache was built using chained fixups, so patch tables must be used for overrides\n\t\t# padding                : 20; # TBD\n\tsharedRegionStart: int \t\t\t# base load address of cache if not slid\n\tsharedRegionSize: int \t\t\t# overall size of region cache can be mapped into\n\tmaxSlide: int \t\t\t\t\t# runtime slide of cache can be between zero and this value\n\tdylibsImageArrayAddr: int \t\t# (unslid) address of ImageArray for dylibs in this cache\n\tdylibsImageArraySize: int \t\t# size of ImageArray for dylibs in this cache\n\tdylibsTrieAddr: int \t\t\t# (unslid) address of trie of indexes of all cached dylibs\n\tdylibsTrieSize: int \t\t\t# size of trie of cached dylib paths\n\totherImageArrayAddr: int \t\t# (unslid) address of ImageArray for dylibs and bundles with dlopen closures\n\totherImageArraySize: int \t\t# size of ImageArray for dylibs and bundles with dlopen closures\n\totherTrieAddr: int \t\t\t\t# (unslid) address of trie of indexes of all dylibs and bundles with dlopen closures\n\totherTrieSize: int \t\t\t\t# size of trie of dylibs and bundles with dlopen closures\t\n\tmappingWithSlideOffset: int \t# file offset to first dyld_cache_mapping_and_slide_info\n\tmappingWithSlideCount: int \t\t# number of dyld_cache_mapping_and_slide_info entries\n\n\t_fields_ = (\n\t\t(\"magic\", 16),\n\t\t(\"mappingOffset\", \"<I\"),\n\t\t(\"mappingCount\", \"<I\"),\n\t\t(\"imagesOffset\", \"<I\"),\n\t\t(\"imagesCount\", \"<I\"),\n\t\t(\"dyldBaseAddress\", \"<Q\"),\n\t\t(\"codeSignatureOffset\", \"<Q\"),\n\t\t(\"codeSignatureSize\", \"<Q\"),\n\t\t(\"slideInfoOffset\", \"<Q\"),\n\t\t(\"slideInfoSize\", \"<Q\"),\n\t\t(\"localSymbolsOffset\", \"<Q\"),\n\t\t(\"localSymbolsSize\", \"<Q\"),\n\t\t(\"uuid\", 16),\n\t\t(\"cacheType\", \"<Q\"),\n\t\t(\"branchPoolsOffset\", \"<I\"),\n\t\t(\"branchPoolsCount\", \"<I\"),\n\t\t(\"accelerateInfoAddr\", \"<Q\"),\n\t\t(\"accelerateInfoSize\", \"<Q\"),\n\t\t(\"imagesTextOffset\", \"<Q\"),\n\t\t(\"imagesTextCount\", \"<Q\"),\n\t\t(\"patchInfoAddr\", \"<Q\"),\n\t\t(\"patchInfoSize\", \"<Q\"),\n\t\t(\"otherImageGroupAddrUnused\", \"<Q\"),\n\t\t(\"otherImageGroupSizeUnused\", \"<Q\"),\n\t\t(\"progClosuresAddr\", \"<Q\"),\n\t\t(\"progClosuresSize\", \"<Q\"),\n\t\t(\"progClosuresTrieAddr\", \"<Q\"),\n\t\t(\"progClosuresTrieSize\", \"<Q\"),\n\t\t(\"platform\", \"<I\"),\n\t\t(\"CacheInfoBitfield\", \"<I\"),\n\t\t(\"sharedRegionStart\", \"<Q\"),\n\t\t(\"sharedRegionSize\", \"<Q\"),\n\t\t(\"maxSlide\", \"<Q\"),\n\t\t(\"dylibsImageArrayAddr\", \"<Q\"),\n\t\t(\"dylibsImageArraySize\", \"<Q\"),\n\t\t(\"dylibsTrieAddr\", \"<Q\"),\n\t\t(\"dylibsTrieSize\", \"<Q\"),\n\t\t(\"otherImageArrayAddr\", \"<Q\"),\n\t\t(\"otherImageArraySize\", \"<Q\"),\n\t\t(\"otherTrieAddr\", \"<Q\"),\n\t\t(\"otherTrieSize\", \"<Q\"),\n\t\t(\"mappingWithSlideOffset\", \"<I\"),\n\t\t(\"mappingWithSlideCount\", \"<I\"),\n\t)\n\n\t@classmethod\n\tdef parse(cls, buffer: BufferedReader, fileOffset: int, loadData: bool = True) -> dyld_cache_header:\n\t\tinst = super().parse(buffer, fileOffset, loadData=loadData)\n\n\t\t# The mappingOffset is directly after this header, because of that\n\t\t# we can determine the correct size of this\n\t\tsearch = lambda field: inst.offsetOf(field[0]) == inst.mappingOffset\n\t\tfieldCutoff = next(filter(search, inst._fields_), None)\n\n\t\tif inst.mappingOffset == 0x140:\n\t\t\tinst.cutoffPoint = -1\n\t\telif not fieldCutoff:\n\t\t\tlogging.warning(\"Unable to determine dyld_cache_header length\")\n\t\t\tinst.cutoffPoint = -1\n\t\telse:\n\t\t\t# zero out the unused fields\n\t\t\tinst.cutoffPoint = inst._fields_.index(fieldCutoff)\n\t\t\ttargetFields = inst._fields_[inst.cutoffPoint:]\n\t\t\tfor field in targetFields:\n\t\t\t\tsetattr(inst, field[0], None)\n\n\t\treturn inst\n\t\n\tdef containsField(self, field: str) -> bool:\n\t\t\"\"\"Check that a field is available within the header.\n\n\t\targs:\n\t\t\tfield: the field to check\n\t\t\"\"\"\n\n\t\tfieldIndex = -1\n\t\tfor f in self._fields_:\n\t\t\tif f[0] == field:\n\t\t\t\tfieldIndex = self._fields_.index(f)\n\t\t\t\tbreak\n\t\t\n\t\tif fieldIndex == -1:\n\t\t\treturn False\n\n\t\tif self.cutoffPoint == -1:\n\t\t\treturn True\n\t\t\n\t\tif fieldIndex >= self.cutoffPoint:\n\t\t\treturn False\n\t\telse:\n\t\t\treturn True\n\n\tdef asBytes(self) -> bytes:\n\t\tdata = super().asBytes()\n\n\t\t# delete data after the cutoff point\n\t\tcutoffIndex = self.offsetOf(self._fields_[self.cutoffPoint])\n\t\tdata = data[0:cutoffIndex]\n\n\t\treturn data\n\n\nclass dyld_cache_mapping_info(Structure):\n\t\n\tSIZE: ClassVar[int] = 32\n\n\taddress: int\n\tsize: int\n\tfileOffset: int\n\tmaxProt: int\n\tinitProt: int\n\n\t_fields_ = (\n\t\t(\"address\", \"<Q\"),\n\t\t(\"size\", \"<Q\"),\n\t\t(\"fileOffset\", \"<Q\"),\n\t\t(\"maxProt\", \"<I\"),\n\t\t(\"initProt\", \"<I\"),\n\t)\n\n\nclass dyld_cache_image_info(Structure):\n\t\n\tSIZE: ClassVar[int] = 32\n\n\taddress: int\n\tmodTime: int\n\tinode: int\n\tpathFileOffset: int\n\tpad: int\n\n\t_fields_ = (\n\t\t(\"address\", \"<Q\"),\n\t\t(\"modTime\", \"<Q\"),\n\t\t(\"inode\", \"<Q\"),\n\t\t(\"pathFileOffset\", \"<I\"),\n\t\t(\"pad\", \"<I\"),\n\t)\n\n\nclass dyld_cache_slide_info2(Structure):\n\t\n\tversion: int\t\t\t# currently 2\n\tpage_size: int\t\t\t# currently 4096 (may also be 16384)\n\tpage_starts_offset: int\n\tpage_starts_count: int\n\tpage_extras_offset: int\n\tpage_extras_count: int\n\tdelta_mask: int\t\t\t# which (contiguous) set of bits contains the delta to the next rebase location\n\tvalue_add: int\n\n\t# uint16_t\tpage_starts[page_starts_count];\n\t# uint16_t\tpage_extras[page_extras_count];\n\n\tpageStartsData: bytes\n\tpageExtrasData: bytes\n\n\t_fields_ = (\n\t\t(\"version\", \"<I\"),\n\t\t(\"page_size\", \"<I\"),\n\t\t(\"page_starts_offset\", \"<I\"),\n\t\t(\"page_starts_count\", \"<I\"),\n\t\t(\"page_extras_offset\", \"<I\"),\n\t\t(\"page_extras_count\", \"<I\"),\n\t\t(\"delta_mask\", \"<Q\"),\n\t\t(\"value_add\", \"<Q\"),\n\t)\n\n\tdef loadData(self) -> None:\n\t\tself._buffer.seek(self._offset + self.page_starts_offset)\n\t\tself.pageStartsData = self._buffer.read(self.page_starts_count * 2)\n\n\t\tself._buffer.seek(self._offset + self.page_extras_offset)\n\t\tself.pageExtrasData = self._buffer.read(self.page_extras_count * 2)\n\n\nclass dyld_cache_slide_info3(Structure):\n\n\tSIZE: ClassVar[int] = 20\n\n\tversion: int \t\t\t# currently 3\n\tpage_size: int \t\t\t# currently 4096 (may also be 16384)\n\tpage_starts_count: int\n\tauth_value_add: int\n\n\t# page_starts[/* page_starts_count */]\n\tpageStartsData: bytes\n\n\t_fields_ = (\n\t\t(\"version\", \"<I\"),\n\t\t(\"page_size\", \"<I\"),\n\t\t(\"page_starts_count\", \"<I\"),\n\t\t(\"auth_value_add\", \"<Q\"),\n\t)\n\n\tdef loadData(self) -> None:\n\t\tself._buffer.seek(self._offset + self.SIZE)\n\t\tself.pageStartsData = self._buffer.read(self.page_starts_count * 2)\n\t\tpass\n\n\nclass dyld_cache_local_symbols_entry(Structure):\n\n\tSIZE: ClassVar[int] = 12\n\n\tdylibOffset: int \t\t# offset in cache file of start of dylib\n\tnlistStartIndex: int \t# start index of locals for this dylib\n\tnlistCount: int \t\t# number of local symbols for this dylib\n\n\t_fields_ = (\n\t\t(\"dylibOffset\", \"<I\"),\n\t\t(\"nlistStartIndex\", \"<I\"),\n\t\t(\"nlistCount\", \"<I\"),\n\t)\n\n\nclass dyld_cache_local_symbols_info(Structure):\n\t\n\tnlistOffset: int \t# offset into this chunk of nlist entries\n\tnlistCount: int \t# count of nlist entries\n\tstringsOffset: int \t# offset into this chunk of string pool\n\tstringsSize: int \t# byte count of string pool\n\tentriesOffset: int \t# offset into this chunk of array of dyld_cache_local_symbols_entry \n\tentriesCount: int \t# number of elements in dyld_cache_local_symbols_entry array\n\n\tnlistData: bytes\n\tstringData: bytes\n\n\tentries: List[dyld_cache_local_symbols_entry]\n\n\t_fields_ = (\n\t\t(\"nlistOffset\", \"<I\"),\n\t\t(\"nlistCount\", \"<I\"),\n\t\t(\"stringsOffset\", \"<I\"),\n\t\t(\"stringsSize\", \"<I\"),\n\t\t(\"entriesOffset\", \"<I\"),\n\t\t(\"entriesCount\", \"<I\"),\n\t)\n\n\tdef loadData(self) -> None:\n\t\tself._buffer.seek(self._offset + self.nlistOffset)\n\t\tself.nlistData = self._buffer.read(self.nlistCount * 16) # size of nlist_64\n\t\tself._buffer.seek(self._offset + self.stringsOffset)\n\t\tself.stringData = self._buffer.read(self.stringsSize)\n\n\t\tself.entries = []\n\t\tfor i in range(0, self.entriesCount):\n\t\t\toffset = (i * dyld_cache_local_symbols_entry.SIZE) + self._offset + self.entriesOffset\n\t\t\tself.entries.append(dyld_cache_local_symbols_entry.parse(self._buffer, offset))\n\n\nclass dyld_cache_mapping_and_slide_info(Structure):\n\n\tSIZE: ClassVar[int] = 56\n\t\n\taddress: int\n\tsize: int\n\tfileOffset: int\n\tslideInfoFileOffset: int\n\tslideInfoFileSize: int\n\tflags: int\n\tmaxProt: int\n\tinitProt: int\n\n\t_fields_ = (\n\t\t(\"address\", \"<Q\"),\n\t\t(\"size\", \"<Q\"),\n\t\t(\"fileOffset\", \"<Q\"),\n\t\t(\"slideInfoFileOffset\", \"<Q\"),\n\t\t(\"slideInfoFileSize\", \"<Q\"),\n\t\t(\"flags\", \"<Q\"),\n\t\t(\"maxProt\", \"<I\"),\n\t\t(\"initProt\", \"<I\"),\n\t)\n\n\nclass dyld_cache_slide_pointer3_plain(object):\n\t\"\"\"Represents a plain pointer\"\"\"\n\n\tpointerValue: int\n\toffsetToNextPointer: int\n\tunused: int\n\n\tdef __init__(self, raw: int) -> None:\n\t\tself.pointerValue = raw % 0x7FFFFFFFFFFFF\n\t\tself.offsetToNextPointer = (raw >> 51) & 0x7FF\n\t\tself.unused = raw >> 62\n\n\nclass dyld_cache_slide_pointer3_auth(object):\n\t\"\"\"Represents an authenticated pointer\"\"\"\n\n\toffsetFromSharedCacheBase: int\n\tdiversityData: int\n\thasAddressDiversity: int\n\tkey: int\n\toffsetToNextPointer: int\n\tunused: int\n\tauthenticated: int \t\t\t\t# = 1\n\n\tdef __init__(self, raw) -> None:\n\t\tself.offsetFromSharedCacheBase = raw & 0xFFFFFFFF\n\t\tself.diversityData = (raw >> 32) & 0xFFFF\n\t\tself.hasAddressDiversity = (raw >> 48) & 0x1\n\t\tself.key = (raw >> 49) & 0x3\n\t\tself.offsetToNextPointer = (raw >> 51) & 0x7FF\n\t\tself.unused = (raw >> 62) & 0x1\n\t\tself.authenticated = raw >> 63\n\n\nclass dyld_cache_slide_pointer3(Structure):\n\t\"\"\"Represents an unslid V3 rebase pointer.\n\t\n\tThis structure is STRICTLY immutable!\n\t\"\"\"\n\n\traw: int\n\n\t_fields_ = (\n\t\t(\"raw\", \"<Q\"),\n\t)\n\n\tplain: dyld_cache_slide_pointer3_plain\n\tauth: dyld_cache_slide_pointer3_auth\n\n\tdef loadData(self) -> None:\n\t\tsuper().loadData()\n\n\t\tself.plain = dyld_cache_slide_pointer3_plain(self.raw)\n\t\tself.auth = dyld_cache_slide_pointer3_auth(self.raw)","sub_path":"DyldExtractor/Dyld/DyldStructs.py","file_name":"DyldStructs.py","file_ext":"py","file_size_in_byte":12690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"213555964","text":"def find_short(s):\n\ts_list = []\n\tfor word in s.split():\n\t\ts_list.append(word)\n\n\tprint(len(min(filter(None, s_list), key=len)))\n\n\n\nfind_short(\"bitcoin take over the world maybe who knows perhaps\")","sub_path":"return_shortest_word.py","file_name":"return_shortest_word.py","file_ext":"py","file_size_in_byte":195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"339888309","text":"# encoding: utf-8\n__author__ = 'gpzim98'\n\nfrom django.db import models\nfrom core.models.conta import Conta\nfrom core.models.categoria import Categoria\nfrom django.contrib.auth.models import User\nfrom datetime import datetime, date\n\nSTATUS = (\n        ('D', 'Despesa'),\n        ('R', 'Receita'),\n)\n\n\nclass Movimento(models.Model):\n    descricao = models.CharField(max_length=200, null=False, blank=False, help_text='Descreve sua movimentação')\n    consolidado = models.BooleanField(default=False, help_text='Informe se a movimentação já foi consolidada')\n    data_previsao = models.DateField(null=True, blank=True, auto_now=False, default=date.today(),\n                                         verbose_name='Data de previsão',\n                                         help_text='Informe a data de previsão de consolidação da movimentação')\n    #Data do cadastro apenas\n    data_movimentacao = models.DateField(null=True, blank=True, auto_now=True, default=date.today(),\n                                             verbose_name='Data de movimentação',\n                                             help_text='Informe a data da movimentação')\n    valor = models.DecimalField(max_digits=11, decimal_places=2, verbose_name='Valor', default=0,\n                                help_text='Informe o valor da movimentação')\n    valor_parcial = models.DecimalField(max_digits=11, decimal_places=2, verbose_name='Valor pago', default=0,\n                                        help_text='Caso já tenha pago uma parte, informe o valor parcial quitado')\n    conta = models.ForeignKey(Conta, null=True, blank=True, verbose_name='Conta',\n                              help_text='Informe a conta a qual este movimento pertence')\n    categoria = models.ForeignKey(Categoria, null=True, blank=True, verbose_name='Categoria',\n                                  help_text='Informe a categoria que esta movimentação pertence')\n    usuario = models.ForeignKey(User, null=True, blank=True, verbose_name='Proprietário',\n                                help_text='Informe a conta a qual este movimento pertence')\n    tipo_movimentacao = models.CharField(max_length=10, choices=STATUS)\n\n    class Meta:\n        app_label = 'core'\n\n    def __unicode__(self):\n        if self.tipo_movimentacao == 'D':\n            tipo_movimentacao = u'Débito'\n        else:\n            tipo_movimentacao = u'Crédito'\n        return self.descricao[:20:] + ' - Parcial: ' + unicode(self.valor_parcial) + ' - Total: ' + unicode(self.valor) \\\n               + ' - Tipo: ' + tipo_movimentacao\n\n","sub_path":"core/models/movimento.py","file_name":"movimento.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"550041637","text":"#!/usr/bin/env python\nimport os\nimport pdb \nimport numpy as np\nimport pickle\nimport gc\nimport time\n\nbase_path = '/data1/qtang/samsung/part_reid/train/Samsung/alidfcd_ivabox_sambox_partnet_baseline'\n\ndef concat(path1,path2):\n  path1_1=os.path.join(path1,'concat_feature/0.4')\n  for weight in [3]:\n    weight = float(weight)/10\n    for file_name in os.listdir(path1_1):\n      npz1=np.load(os.path.join(path1_1,file_name))\n      npz2=np.load(os.path.join(path2,file_name))\n      assert (npz1['img']==npz2['img']).all()\n      imgs=npz1['img']\n      a=np.sqrt((npz2['feat']**2).sum(axis=1))[:,None]\n      feats=np.concatenate((npz1['feat'],npz2['feat']*weight/a),axis=1)\n      path3=os.path.join(path1,'concat_feature_all/'+str(weight))\n      if not os.path.exists(path3):\n        os.makedirs(path3)\n      with open(os.path.join(path3,file_name),'w') as f:\n        np.savez(f,feat=feats,img=imgs)\n\n\nfor folder in ['evaLib62','query62','evalib62']:#,'evaLib62','evalib62','query62']:\n  for iter in ['80000']:\n    path1 = base_path+'/val/feat_'+folder+'_top40_qf_senet/iter'+iter+'/'\n    path2 = base_path+'/forward_sftmax/feat_'+folder+'_sftmax/pool5_93'\n    concat(path1,path2)\n\n\n","sub_path":"part_reid/train/Samsung/alidfcd_ivabox_sambox_partnet_baseline/val/concat_feat_all_62.py","file_name":"concat_feat_all_62.py","file_ext":"py","file_size_in_byte":1175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"140135617","text":"\"\"\"\n// Time Complexity : O(m*n) \n// Space Complexity : O(m*n)\n// Did this code successfully run on Leetcode : Yes\n// Any problem you faced while coding this : No\n// Your code here along with comments explaining your approach\nAlgorithm explanation\nDFS \n- Updating the visited element in the grid by marking it as '0', since\nit is not part of the island\n- each DFS constitutes to be one count\n\"\"\"\nclass Solution(object):\n    def numIslands(self, grid):\n        \"\"\"\n        :type grid: List[List[str]]\n        :rtype: int\n        \"\"\"\n        \"\"\"\n        1. Iterate over the grid and run dfs on non visited 1\n        2. Dfs\n            - mark the element visited\n            - FOr valid direction and if ele is 1, call dfs\n        \"\"\"\n        def dfs(grid,i,j,m,n):\n            grid[i][j] = '0'\n            directions = [(1,0),(0,1),(-1,0),(0,-1)]\n            for x,y in directions:\n                valx = x + i\n                valy = y + j\n                if valx >=0 and valx < m and valy >=0 and valy < n and grid[valx][valy]!='0':\n                    dfs(grid,valx,valy,m,n)\n        \n        if not grid or not grid[0]:\n            return 0\n        m = len(grid)\n        n = len(grid[0])\n        count = 0\n        for i in range(m):\n            for j in range(n):\n                if grid[i][j] == '1':\n                    dfs(grid,i,j,m,n)\n                    count+=1\n        return count","sub_path":"number_of_islands.py","file_name":"number_of_islands.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"163688194","text":"\"\"\"PytSite Currency Plugin Widgets\n\"\"\"\n__author__ = 'Oleksandr Shepetko'\n__email__ = 'a@shepetko.com'\n__license__ = 'MIT'\n\nfrom plugins import widget as _widget\nfrom . import _api\n\n\nclass Select(_widget.select.Select):\n    \"\"\"Currency Select Widget\n    \"\"\"\n\n    def __init__(self, uid: str, **kwargs):\n        \"\"\"Init.\n        \"\"\"\n        super().__init__(uid, **kwargs)\n\n        all_c = sorted(_api.get_all())\n\n        # Exclude 'system' currencies\n        if kwargs.get('exclude_system', True):\n            all_c = [c for c in all_c if not c.startswith('_')]\n\n        # Exclude additional currencies\n        all_c = [c for c in all_c if c not in kwargs.get('exclude', ())]\n\n        self._items = zip(all_c, ['{} ({})'.format(c, _api.get_title(c)) for c in all_c])\n","sub_path":"_widget.py","file_name":"_widget.py","file_ext":"py","file_size_in_byte":766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"592665850","text":"import inspect\nimport sys\n\nimport ovh\n\nimport pyovh\nfrom pyovh.config import YamlConfig\nfrom pyovh.handler import PyOvhHandler\nfrom pyovh.operation import NetworkMaker\n\n\nclass Main:\n    def __init__(self):\n\n        self.config_handler = YamlConfig(sys.argv[1])\n        self.config = self.config_handler.get_config()\n        self.ovh_handler = ovh.Client(**self.config_handler.get_config()['credentials'])\n        self.request_handler = PyOvhHandler(self.ovh_handler)\n        self.in_memory_store = {}\n        self.commands = []\n\n        self.setup_operations()\n\n    def setup_operations(self):\n        config = self.config_handler.get_config()['public_cloud_project']\n        for operations in inspect.getmembers(pyovh.operation, predicate=inspect.isclass):\n            if operations[0].startswith('PyOvhHandler'):\n                continue\n            self.commands.append(operations[1](config, self.request_handler, self.in_memory_store))\n        self.commands = sorted(self.commands, key=lambda item: item.SEQUENCE_NUM)\n\n    def run(self):\n        for c in self.commands:\n            c.perform()\n\nif __name__ == \"__main__\":\n    program = Main()\n    program.run()\n","sub_path":"pyovh/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"160467143","text":"import cv2\nimport numpy as np\nimport os\nimport random\n\n\ndef get_data_mat(dir):\n    X = []\n    y = []\n    \n    files = os.listdir(dir)\n    \n    random.shuffle(files)\n    for file in files:\n        if file[-3:] == 'jpg':\n            img = cv2.imread(os.path.join(dir, file))\n            \n            img = cv2.resize(img, (250, 150))\n            \n            img = img / 255.\n            X.append(img)\n            if file[:3] == 'air':\n                y.append(0)\n            elif file[:3] == 'fer':\n                y.append(1)\n            elif file[:3] == 'min':\n                y.append(2)\n            elif file[:3] == 'bea':\n                y.append(3)\n            elif file[:3] == 'pan':\n                y.append(4)\n        else:\n            X, y = get_data_mat(os.path.join(dir, file))\n            \n    return np.array(X), np.array(y)\n","sub_path":"read_caltech.py","file_name":"read_caltech.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"422425917","text":"import re, sys, string\r\n\r\n#  Expression class and its subclasses\r\nstate = {}\r\n\r\nclass Expression( object ):\r\n        def __str__(self):\r\n                return \"\"\r\n            \r\nclass BinaryExpr( Expression ):\r\n\tdef __init__(self, op, left, right):\r\n\t\tself.op = op\r\n\t\tself.left = left\r\n\t\tself.right = right\r\n                \r\n\tdef __str__(self):\r\n\t\treturn str(self.op) + \" \" + str(self.left) + \" \" + str(self.right)\r\n\t\r\n\t#Adapted from example in semantics slides\r\n\tdef value(self, state): \r\n\t\tleft = self.left.value(state)\r\n\t\tright = self.right.value(state)\r\n\t\tif self.op == \"+\": \r\n\t\t\treturn left + right\r\n\t\tif self.op == \"-\": \r\n\t\t\treturn left - right\r\n\t\tif self.op == \"*\": \r\n\t\t\treturn left * right\r\n\t\tif self.op == \"/\": \r\n\t\t\treturn left / right\r\n\t\tif self.op == \">\": \r\n\t\t\tif left > right: \r\n\t\t\t\treturn True\r\n\t\t\telse: \r\n\t\t\t\treturn False\r\n\t\tif self.op == \">=\": \r\n\t\t\tif left >= right: \r\n\t\t\t\treturn True\r\n\t\t\telse: \r\n\t\t\t\treturn False\r\n\t\tif self.op == \"<\": \r\n\t\t\tif left < right: \r\n\t\t\t\treturn True\r\n\t\t\telse: \r\n\t\t\t\treturn False\r\n\t\tif self.op == \"<=\": \r\n\t\t\tif left <= right: \r\n\t\t\t\treturn True\r\n\t\t\tif left > right: \r\n\t\t\t\treturn False\r\n\t\tif self.op == \"==\": \r\n\t\t\tif left == right: \r\n\t\t\t\treturn True\r\n\t\t\telse: \r\n\t\t\t\treturn False\r\n\t\tif self.op == \"!=\": \r\n\t\t\tif left != right: \r\n\t\t\t\treturn True\r\n\t\t\telse: \r\n\t\t\t\treturn False\r\n\r\nclass Number( Expression ):\r\n        def __init__(self, val):\r\n                self.val = int(val)\r\n                \r\n        def __str__(self):\r\n                return str(self.val)\r\n\t\r\n        def value(self, state): \r\n\t        return self.val\r\n\r\nclass String( Expression ):\r\n        def __init__(self, val):\r\n                self.val = val\r\n                \r\n        def __str__(self):\r\n                return str(self.val)\r\n\t\r\n        def value(self, state): \r\n                return self.val\r\n        \r\nclass VariableRef( Expression ):\r\n\tdef __init__(self, ident):\r\n\t\tself.name = ident\r\n                \r\n\tdef __str__(self):\r\n\t\treturn self.name\r\n\t\r\n\t#Since it doesn't have an immediate integer value, references the state dictionary\r\n\tdef value(self, state): \r\n\t\treturn state[self.name] \r\n\t\t\r\n\r\n#  Statement class and its subclasses\r\nclass Statement( object ):\r\n        def __str__(self):\r\n                return \"\"\r\n            \r\nclass Assign( Statement ):\r\n\tdef __init__(self, var, expr):\r\n\t\tself.var = str(var)\r\n\t\tself.expr = expr\r\n                \r\n\tdef __str__( self ):\r\n\t\treturn \"= \" + self.var + \" \" + str(self.expr)\r\n\t\r\n\t#From the semantics slides\r\n\tdef meaning(self, state): \r\n\t\tstate[self.var] = self.expr.value(state)\r\n\t\treturn state\r\n\r\nclass Block( Statement ):\r\n\tdef __init__(self, stmts):\r\n\t\tself.stmts = stmts\r\n\t\t\r\n\tdef __str__(self):\r\n\t\tr = \"\"\r\n\t\tfor s in self.stmts:\r\n\t\t\tr += str(s) +'\\n'\r\n\t\treturn r\r\n\r\n\tdef meaning(self, state): \r\n\t\tfor element in self.stmts: \r\n\t\t\telement.meaning(state)\r\n\t\t\t\r\n\t\t\r\nclass WhileStmt( Statement ):\r\n\tdef __init__(self, expr, block):\r\n\t\tself.expr = expr\r\n\t\tself.body = block\r\n\t\t\r\n\tdef __str__(self):\r\n\t\treturn \"while \" + str(self.expr) + \"\\n\" + str(self.body) + \"endwhile\"\r\n\t\r\n\tdef meaning(self, state): \r\n\t\twhile self.expr.value(state) == True: \r\n\t\t\tself.body.meaning(state)\r\n\t\t\r\n\r\nclass IfStmt( Statement ):\r\n\t\r\n\t#Both then and elseblock are block types, self.expr is a binary expression\r\n\tdef __init__(self, expr, block, elseblock):\r\n\t\tself.expr = expr\r\n\t\tself.then = block\r\n\t\tself.elseblock = elseblock\r\n\t\t    \r\n\tdef __str__(self):\r\n\t\treturn \"if \" + str(self.expr) + \"\\n\" + str(self.then) + \"else\\n\" + str(self.elseblock) + \"endif\"\r\n\t\r\n\t#Checks Boolean value of test expression. Finds meaning of appropriate block of instructions afterward\r\n\tdef meaning(self, state): \r\n\t\tif self.expr.value(state) == True: \r\n\t\t\tself.then.meaning(state)\r\n\t\tif self.expr.value(state) == False: \r\n\t\t\tself.elseblock.meaning(state)\r\n\t\t\r\n    \r\n\r\ndebug = False\r\n\r\n# aux routine\r\n\r\ndef error( msg ):\r\n\t#print msg\r\n\tsys.exit(msg)\r\n\r\ndef match(matchtok, tokens):\r\n\ttok = tokens.peek( )\r\n\tif (tok != matchtok): error(\"Expecting \"+matchtok)\r\n\treturn tokens.next( )\r\n\r\n# The \"parse\" function. This builds a list of tokens from the input string,\r\n# and then hands it to a recursive descent parser for the PAL grammar.\r\n\r\ndef parseFactor(tokens):\r\n\t\"\"\" factor -> '(' expr ')' | ident | number | string \"\"\"\r\n\ttok = tokens.peek( )\r\n\tif debug: print(\"Factor: \" + str(tok))\r\n\tif re.match(Lexer.number, tok):\r\n\t\ttokens.next( )\r\n\t\treturn Number(tok)\r\n\tif re.match(Lexer.string, tok):\r\n\t\ttokens.next( )\r\n\t\treturn String(tok)\r\n\tif re.match(Lexer.identifier, tok):\r\n\t\ttokens.next( )\r\n\t\treturn VariableRef(tok)\r\n\tif tok == \"(\":\r\n\t\ttokens.next( )\r\n\t\texpr = parseExpr(tokens)\r\n\t\ttok = tokens.peek( )\r\n\t\tif tok != \")\":\r\n\t\t\terror(\"Missing )\");\r\n\t\ttokens.next( )\r\n\t\treturn expr\r\n\terror(\"Invalid operand\")\r\n\treturn None\r\n\r\ndef parseTerm(tokens):\r\n\t\"\"\" term -> factor { ('*' | '/') factor } \"\"\"\r\n\ttok = tokens.peek( )\r\n\tif debug: print(\"Term: \"+str(tok))\r\n\tleft = parseFactor(tokens)\r\n\ttok = tokens.peek( )\r\n\twhile tok == \"*\" or tok == \"/\":\r\n\t\tsavetok = tok\r\n\t\ttokens.next()\r\n\t\tright = parseFactor(tokens)\r\n\t\tleft = BinaryExpr(savetok, left, right)\r\n\t\ttok = tokens.peek( )\r\n\treturn left\r\n\r\ndef parseAddExpr(tokens):\r\n\t\"\"\" addExpr    = term { ('+' | '-') term } \"\"\"\r\n\ttok = tokens.peek( )\r\n\tif debug: print(\"Expr: \"+str(tok))\r\n\tleft = parseTerm(tokens)\r\n\ttok = tokens.peek( )\r\n\twhile tok == \"+\" or tok == \"-\":\r\n\t\tsavetok = tok\r\n\t\ttokens.next()\r\n\t\tright = parseTerm(tokens)\r\n\t\tleft = BinaryExpr(savetok, left, right)\r\n\t\ttok = tokens.peek( )\r\n\treturn left\r\n\r\ndef relExpr(tokens):\r\n\t\"\"\" relExpr    = addExpr { ('+' | '-') addExpr } \"\"\"\r\n\ttok = tokens.peek( )\r\n\tif debug: print(\"Expr: \" +str(tok))\r\n\tleft = parseAddExpr(tokens)\r\n\ttok = tokens.peek( )\r\n\tif tok == \"<\" or tok == \"<=\" or tok == \">\" or tok == \">=\" or tok == \"==\" or tok == \"!=\":\r\n\t\tsavetok = tok\r\n\t\ttokens.next()\r\n\t\tright = parseAddExpr(tokens)\r\n\t\tleft = BinaryExpr(savetok, left, right)\r\n\t\ttok = tokens.peek( )\r\n\treturn left\r\n\r\ndef andExpr(tokens):\r\n\t\"\"\" andExpr    = relExpr { 'and' relExpr } \"\"\"\r\n\ttok = tokens.peek( )\r\n\tif debug: print (\"Expr: \"+str(tok))\r\n\tleft = relExpr(tokens)\r\n\ttok = tokens.peek( )\r\n\twhile tok == \"and\":\r\n\t\tsavetok = tok\r\n\t\ttokens.next()\r\n\t\tright = relExpr(tokens)\r\n\t\tleft = BinaryExpr(savetok, left, right)\r\n\t\ttok = tokens.peek( )\r\n\treturn left\r\n\r\ndef parseExpr(tokens):\r\n\t\"\"\" Expr    = andExpr { 'or' andExpr } \"\"\"\r\n\ttok = tokens.peek( )\r\n\tif debug: print(\"Expr: \" + str(tok))\r\n\tleft = andExpr(tokens)\r\n\ttok = tokens.peek( )\r\n\twhile tok == \"or\":\r\n\t\tsavetok = tok\r\n\t\ttokens.next()\r\n\t\tright = andExpr(tokens)\r\n\t\tleft = BinaryExpr(savetok, left, right)\r\n\t\ttok = tokens.peek( )\r\n\treturn left\r\n\r\ndef parseAssign(tokens):\r\n\t\"\"\" assign -> ident '=' expr \"\"\"\r\n\tvar = tokens.peek( )\r\n\ttokens.next( )\r\n\tif tokens.peek() == \"=\":\r\n\t\tmatch(\"=\", tokens)\r\n\t\texpr = parseExpr(tokens)\r\n\t\tmatch(\";\", tokens)\r\n\t\treturn Assign(var, expr)\r\n\terror(\"Invalid assignment or function call\")\r\n\treturn None\r\n\t\r\ndef parseBlock(tokens):\r\n\ttok = match(\"@\", tokens)\r\n\tstmts = [ ]\r\n\twhile tok != \"~\":\r\n\t\tif debug: print(\"BlocK: \"+str(tok))\r\n\t\tstmts.append(parseStmt(tokens))\r\n\t\tif debug: print(\"Block stmt: \" + str(tokens.peek( )))\r\n\t\ttok = tokens.peek( ) # match(\";\", tokens)\r\n\ttokens.next( )\r\n\tif debug and tokens.peek() != None: print(\"End block: \" + str(tokens.peek()))\r\n\treturn Block(stmts)\r\n\r\ndef parseWhile(tokens):\r\n\t\"\"\" whileStmt = 'while' expression ':' eoln block \"\"\"\r\n\ttokens.next( )\r\n\texpr = parseExpr(tokens)\r\n\tmatch(\":\", tokens)\r\n\tmatch(\";\", tokens)\r\n\tstmts = parseBlock(tokens)\r\n\treturn WhileStmt(expr, stmts)\r\n\r\ndef parseIf(tokens):\r\n\t\"\"\" ifStmt = 'if' expression ':' eoln block\r\n\t\t[ else ':' eoln block ]  \"\"\"\r\n\ttokens.next( )\r\n\texpr = parseExpr(tokens)\r\n\tmatch(\":\", tokens)\r\n\tmatch(\";\", tokens)\r\n\tstmts = parseBlock(tokens)\r\n\telsestmts = Block([ ])\r\n\tif tokens.peek( ) == \"else\":\r\n\t\ttokens.next( )\r\n\t\tmatch(\":\", tokens)\r\n\t\tmatch(\";\", tokens)\r\n\t\telsestmts = parseBlock(tokens)\r\n\treturn IfStmt(expr, stmts, elsestmts)\r\n\r\ndef parseStmt(tokens):\r\n\ttok = tokens.peek( )\r\n\tif debug:\r\n\t\tprint(\"Stmt: \"+str(tok))\r\n\tif tok == \"while\":\r\n\t\treturn parseWhile(tokens)\r\n\tif tok == \"if\":\r\n\t\treturn parseIf(tokens)\r\n\tif re.match(Lexer.identifier, tok):\r\n\t\treturn parseAssign(tokens)\r\n\treturn None\r\n\r\ndef parseStmtList(tokens  ):\r\n\t\"\"\" gee = { Statement } \"\"\"\r\n\tstmts = [ ]\r\n\ttok = tokens.peek( )\r\n\twhile tok is not None:\r\n\t\tast = parseStmt(tokens)\r\n\t\tstmts.append(ast)\r\n\t\tif tokens.peek() == \";\":\r\n\t\t\tmatch(';', tokens)\r\n\t\ttok = tokens.peek();\r\n\treturn stmts\r\n\t\t\r\n\r\ndef parse( text ) :\r\n\tglobal tokens\r\n\tstateString = \"{\"\r\n\ttokens = Lexer( text )\r\n\tstmtlist = parseStmtList( tokens )\r\n\tfor element in stmtlist: \r\n\t\telement.meaning(state)\r\n\tcommaCounter = len(state)\r\n\tfor element in state: \r\n\t\tstateString = stateString + \"<\" + str(element) + \", \" + str(state[element]) + \">\"\r\n\t\tcommaCounter -= 1\r\n\t\tif commaCounter != 0: \r\n\t\t\tstateString += \", \"\t\t\r\n\tstateString += \"}\"\r\n\tprint (\"\\n\" + stateString)\r\n\treturn\r\n\r\n# Lexer, a private class that represents lists of tokens from a Gee\r\n# statement. This class provides the following to its clients:\r\n#\r\n#   o A constructor that takes a string representing a statement\r\n#       as its only parameter, and that initializes a sequence with\r\n#       the tokens from that string.\r\n#\r\n#   o peek, a parameterless message that returns the next token\r\n#       from a token sequence. This returns the token as a string.\r\n#       If there are no more tokens in the sequence, this message\r\n#       returns None.\r\n#\r\n#   o removeToken, a parameterless message that removes the next\r\n#       token from a token sequence.\r\n#\r\n#   o __str__, a parameterless message that returns a string representation\r\n#       of a token sequence, so that token sequences can print nicely\r\n\r\nclass Lexer :\r\n\t\r\n\t\r\n\t# The constructor with some regular expressions that define Gee's lexical rules.\r\n\t# The constructor uses these expressions to split the input expression into\r\n\t# a list of substrings that match Gee tokens, and saves that list to be\r\n\t# doled out in response to future \"peek\" messages. The position in the\r\n\t# list at which to dole next is also saved for \"nextToken\" to use.\r\n\t\r\n\tspecial = r\"\\(|\\)|\\[|\\]|,|:|;|@|~|\\$\"\r\n\trelational = \"<=?|>=?|==?|!=\"\r\n\tarithmetic = \"\\+|\\-|\\*|/\"\r\n\t#char = r\"'.\"\r\n\tstring = r\"'[^']*'\" + \"|\" + r'\"[^\"]*\"'\r\n\tnumber = r\"\\-?\\d+(?:\\.\\d+)?\"\r\n\tliteral = string + \"|\" + number\r\n\t#idStart = r\"a-zA-Z\"\r\n\t#idChar = idStart + r\"0-9\"\r\n\t#identifier = \"[\" + idStart + \"][\" + idChar + \"]*\"\r\n\tidentifier = \"[a-zA-Z]\\w*\"\r\n\tlexRules = literal + \"|\" + special + \"|\" + relational + \"|\" + arithmetic + \"|\" + identifier\r\n\t\r\n\tdef __init__( self, text ) :\r\n\t\tself.tokens = re.findall( Lexer.lexRules, text )\r\n\t\tself.position = 0\r\n\t\tself.indent = [ 0 ]\r\n\t\r\n\t\r\n\t# The peek method. This just returns the token at the current position in the\r\n\t# list, or None if the current position is past the end of the list.\r\n\t\r\n\tdef peek( self ) :\r\n\t\tif self.position < len(self.tokens) :\r\n\t\t\treturn self.tokens[ self.position ]\r\n\t\telse :\r\n\t\t\treturn None\r\n\t\r\n\t\r\n\t# The removeToken method. All this has to do is increment the token sequence's\r\n\t# position counter.\r\n\t\r\n\tdef next( self ) :\r\n\t\tself.position = self.position + 1\r\n\t\treturn self.peek( )\r\n\t\r\n\t\r\n\t# An \"__str__\" method, so that token sequences print in a useful form.\r\n\t\r\n\tdef __str__( self ) :\r\n\t\treturn \"<Lexer at \" + str(self.position) + \" in \" + str(self.tokens) + \">\"\r\n\r\n\r\n\r\n\r\n# The following functions form a recursive descent parser for Gee, with the start\r\n# symbol being script.\r\n\r\n\r\n# \"makeString,\" a utility function that breaks a string constant into a list\r\n# of syntax trees for its individual characters. This list can then be used as\r\n# the elements of an array (since a Gee string is just a shorthand for an array\r\n# of characters). Since the first and last 2 characters of the token are the\r\n# string delimiters, and everything else is meaningful text, this just makes a\r\n# list of \"Atomtrees\" for everything except those first and last characters.\r\n\r\n#def makeString( token ) :\r\n#\treturn [ AtomTree(c) for c in token[2:-2] ]\r\n\r\n\r\n#  Main program if called by itself\r\n\r\n#def main():\r\n#        \"\"\"main program for testing\"\"\"\r\n#        if len(sys.argv) < 2:\r\n#                print \"Usage:  %s filename\" % sys.argv[0]\r\n#                return\r\n#        inn = open(sys.argv[1], \"r\")\r\n#        lines = inn.readlines()\r\n#        parse(string.join(lines, \";\"))\r\n#        # print str(ast)\r\n#        return\r\n\r\ndef chkIndent(line):\r\n        ct = 0\r\n        for ch in line:\r\n                if ch != \" \": return ct\r\n                ct += 1\r\n        return ct\r\n                \r\n\r\ndef delComment(line):\r\n        pos = line.find(\"#\")\r\n        if pos > -1:\r\n                line = line[0:pos]\r\n                line = line.rstrip()\r\n        return line\r\n\r\ndef mklines(filename):\r\n        inn = open(filename, \"r\")\r\n        lines = [ ]\r\n        pos = [0]\r\n        ct = 0\r\n        for line in inn:\r\n                print (line.rstrip())\r\n                ct += 1\r\n                line = line.rstrip( )+\";\"\r\n                line = delComment(line)\r\n                if len(line) == 0 or line == \";\": continue\r\n                indent = chkIndent(line)\r\n                line = line.lstrip( )\r\n                if indent > pos[-1]:\r\n                        pos.append(indent)\r\n                        line = '@' + line\r\n                elif indent < pos[-1]:\r\n                        while indent < pos[-1]:\r\n                                del(pos[-1])\r\n                                line = '~ ' + line\r\n                #print(str(ct)+ \"\\t\"+ line)\r\n                lines.append(line)\r\n\t# print len(pos)\r\n        undent = \"\"\r\n        for i in pos[1:]:\r\n                undent += \" ~ \"\r\n        lines.append(undent)\r\n\t# print undent\r\n        return lines\r\n\r\ndef main():\r\n\t\"\"\"main program for testing\"\"\"\r\n\tglobal debug\r\n\tct = 0\r\n\tfor opt in sys.argv[1:]:\r\n\t\tif opt[0] != \"-\": break\r\n\t\tct = ct + 1\r\n\t\tif opt == \"-d\":\r\n\t\t\tdebug = True\r\n\tif len(sys.argv) < 2+ct:\r\n                print (\"Usage:  %s filename\" % sys.argv[0])\r\n                return\r\n\tparse(\"\".join(mklines(sys.argv[1+ct])))\r\n\treturn\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n\r\n","sub_path":"Semantics/semantics.py","file_name":"semantics.py","file_ext":"py","file_size_in_byte":14092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"102497699","text":"# Copyright (C) 2002-2020 CERN for the benefit of the ATLAS collaboration\n\nfrom __future__ import print_function\n\n__doc__ = \"Factories to lazy instantiate Private/Public Tools and Algorithms\"\n__authors__ = \"Bruno Lenzi, Christos Anastopoulos, Jovan Mitrevski\"\nfrom AthenaCommon.Logging import logging\nfrom AthenaCommon.Resilience import treatException\n\n\ndef factoriesInfo(log):\n    logFactories = logging.getLogger('Factories')\n    logFactories.info(log)\n\n# tools\n\n\ndef isAlreadyInToolSvc(name):\n    \"\"\"isAlreadyInToolSvc (name of the tool)\n    --> check if the tool with name is already in the service\"\"\"\n    from AthenaCommon.AppMgr import ToolSvc\n    if hasattr(ToolSvc, name):\n        return True\n    else:\n        return False\n\n\ndef getFromToolSvc(name):\n    \"\"\"getFromToolSvc (name of the tool)\n    --> Get the tool from toolSvc by name \"\"\"\n    from AthenaCommon.AppMgr import ToolSvc\n    return getattr(ToolSvc, name)\n\n\ndef addToToolSvc(tool):\n    \"\"\"addToToolSvc(tool)\n    --> add tool to ToolSvc\"\"\"\n    from AthenaCommon.AppMgr import ToolSvc\n    ToolSvc += tool\n    return tool\n\n# services\n\n\ndef isAlreadyInServiceMgr(name):\n    \"\"\"isAlreadyInServiceMgr (name of the service)\n    --> check if the service with name is already in the manager\"\"\"\n    from AthenaCommon.AppMgr import ServiceMgr\n    if hasattr(ServiceMgr, name):\n        return True\n    else:\n        return False\n\n\ndef getFromServiceMgr(name):\n    \"\"\"getFromServiceMgr (name of the service)\n    --> Get the service from manager by name \"\"\"\n    from AthenaCommon.AppMgr import ServiceMgr\n    return getattr(ServiceMgr, name)\n\n\ndef addToServiceMgr(service):\n    \"\"\"addToServiceMgr( service )\n    --> add service to ServiceMgr\"\"\"\n    from AthenaCommon.AppMgr import ServiceMgr\n    ServiceMgr += service\n    return service\n\n# algs\n\n\ndef isAlreadyInTopSequence(name):\n    \"\"\"isAlreadyInTopSequence ( mane of the alg )\n    --> check if the alg  with name is already in the Alg sequence\"\"\"\n    from AthenaCommon.AlgSequence import AlgSequence\n    topSequence = AlgSequence()\n    if hasattr(topSequence, name):\n        return True\n    else:\n        return False\n\n\ndef getFromTopSequence(name):\n    \"\"\"getFromTopSequence (name of the alg)\n    --> Get the alg  from TopSequence  by name \"\"\"\n    from AthenaCommon.AlgSequence import AlgSequence\n    topSequence = AlgSequence()\n    return getattr(topSequence, name)\n\n\ndef addToTopSequence(alg):\n    \"addToTopSequence(alg) --> add alg to TopSequence\"\n    from AthenaCommon.AlgSequence import AlgSequence\n    topSequence = AlgSequence()\n    topSequence += alg\n    return alg\n\n\ndef getPropertyValue(tool, property):\n    \"\"\"getPropertyValue(tool, property)\n    --> return the set value or the default value\n      of the given property for the given tool or alg\"\"\"\n    try:\n        return getattr(tool, property)\n    except AttributeError:\n        return tool.getDefaultProperty(property)\n\n\nclass FcnWrapper:\n    \"\"\"A simple wrapper to call a function with no arguments\"\"\"\n\n    def __init__(self, fcn=None):\n        self.fcn = fcn\n\n    def __call__(self):\n        return self.fcn()\n\n\nclass Factory:\n    \"\"\"Factory: base class of ToolFactory and AlgFactory.\n    Allows to \"lazy\" instantiate the configuration of tools/algs/services.\n    Properties are set in the c-tor,\n    and can include other factories,tools.\n    One can also define preInit/postInit methods.\n    The end result is that the configuration\n    can be defined/formed/composed before\n    before its actual instantiation  which happens at demand\n    via the () (__call__()) method.\n    \"\"\"\n\n    def __init__(self, iclass, **defaults):\n        \"\"\"\n        @param iclass Tool / Alg class\n        @param defaults default values for configurables,\n        can be overridden at instantiation.\n        Special parameters:\n        - preInit: list of functions to be called before tool/alg\n          instantiation, take no arguments\n        - preInit: list of functions to be called after tool/alg\n        instantiation, take tool/alg as argument\n        - doAdd: add tool (alg) to ToolSvc (TopSequence) (default: True)\n        - doPrint: print tool/alg after instantiation (default: False)\n        \"\"\"\n        self.iclass = iclass\n        self.defaults = defaults\n\n    def copy(self, name, **kw):\n        \"\"\"copy(self, name, **kw)\n        --> return a new instance of the factory with new name and defaults\"\"\"\n        kw['name'] = name\n        deflt = self.defaults.copy()\n        deflt.update(kw)\n        return self.__class__(self.iclass, **deflt)\n\n    def __call__(self, name='', **kw):\n        \"\"\"Call preInit functions, instantiate tool (alg),\n        call postInit functions and add to ToolSvc (TopSequence)\"\"\"\n        params = self.defaults.copy()\n        params.update(kw)\n        params['name'] = name or params.get('name', self.iclass.__name__)\n        del name, kw  # to avoid silly mistakes\n\n        # Get special parameters (or default values)\n        # and remove them from dictionary\n        preInit = params.pop('preInit', [])\n        postInit = params.pop('postInit', [])\n        doAdd = params.pop('doAdd', True)\n        doPrint = params.pop('doPrint', False)\n\n        # Call preInit functions\n        for fcn in preInit:\n            try:\n                fcn()\n            except Exception:\n                treatException(\n                    'calling preInit function %s on %s instantiation\\n'\n                    % (fcn.__name__, params['name']))\n                raise\n\n        # Instantiate the Factory  parameters or call the Function wrappers.\n        # (if they are inside a list -->ToolHandleArray loop over them)\n        classes = (FcnWrapper, ToolFactory, PublicToolFactory, ServiceFactory)\n        for paramName, value in params.items():\n            if isinstance(value, classes) or \\\n                    (isinstance(value, list) and\n                     any(isinstance(v, classes) for v in value)):\n                try:\n                    params[paramName] = value() \\\n                        if not isinstance(value, list) else \\\n                        [v() if isinstance(v, classes) else v for v in value]\n                except Exception:\n                    treatException('setting property %s :: %s\\n' %\n                                   (params['name'], paramName))\n                    raise\n\n        # Instantiate tool / alg\n        try:\n            obj = self.iclass(**params)\n        except Exception:\n            treatException('instantiating %s, args: %s\\n' %\n                           (self.iclass.__name__, params))\n            raise\n\n        # Call postInit methods\n        for fcn in postInit:\n            try:\n                fcn(obj)\n            except Exception:\n                treatException(\n                    'calling postInit function %s on %s instantiation\\n' % (\n                        fcn.__name__, params['name']))\n                raise\n\n        # Add to ToolSvc or TopSequence\n        if doAdd:\n            self.add(obj)\n\n        # Print\n        if doPrint:\n            print(obj)\n\n        return obj\n\n    def add(self, obj):\n        pass\n\n\nclass ToolFactory(Factory):\n    \"\"\"ToolFactory: to instantiate tools.\n    Does not add in ToolSvc (private tools). See Factory\"\"\"\n\n    def __init__(self, iclass, **defaults):\n        self.iclass = iclass\n        self.defaults = {'doAdd': False}\n        self.defaults.update(defaults)\n\n    def add(self, obj):\n        factoriesInfo(\n            \"Tool with name ==> %s  is a Private Tool,\"\n            \"will not be added in ToolSvc\" % obj.getFullName())\n        pass\n\n    def copyPublic(self, name, **kw):\n        \"\"\"copy(self, name, **kw)\n        --> return a new instance of the factory\n        as a puclic tool with new name and defaults\"\"\"\n        deflt = self.defaults.copy()\n        deflt.update(kw)\n        deflt['name'] = name\n        deflt['doAdd'] = True\n        return PublicToolFactory(self.iclass, **deflt)\n\n\nclass PublicToolFactory(Factory):\n    \"\"\"ToolFactory: to instantiate tools.\n    Adds in ToolSvc (public tools). See Factory\"\"\"\n\n    def __init__(self, iclass, **defaults):\n        self.iclass = iclass\n        self.defaults = {'doAdd': True}\n        self.defaults.update(defaults)\n\n    def add(self, obj):\n        if not isAlreadyInToolSvc(obj.getName()):\n            factoriesInfo(\"Adding new Public Tool ===> %s\" % obj.getFullName())\n            addToToolSvc(obj)\n        else:\n            factoriesInfo(\n                \"Public Tool with name == > % s  already \"\n                \"in ToolSvc using existing instance\" % obj.getFullName())\n\n\nclass ServiceFactory(Factory):\n    \"\"\"ServiceFactory: to instantiate services.\n    Adds to ServiceMgr. See Factory\"\"\"\n\n    def __init__(self, iclass, **defaults):\n        self.iclass = iclass\n        self.defaults = {'doAdd': True}\n        self.defaults.update(defaults)\n\n    def add(self, obj):\n        if not isAlreadyInServiceMgr(obj.getName()):\n            factoriesInfo(\"Adding new Service ===> %s\" % obj.getFullName())\n            addToServiceMgr(obj)\n        else:\n            factoriesInfo(\n                \"Service with name ==> %s  already in ServiceMgr,\"\n                \"using existing instance\" % obj.getFullName())\n\n\nclass AlgFactory(Factory):\n    \"\"\"AlgFactory: to instantiate algs and\n    add them to TopSequence. See Factory\"\"\"\n\n    def __init__(self, iclass, **defaults):\n        self.iclass = iclass\n        self.defaults = {'doAdd': True}\n        self.defaults.update(defaults)\n\n    def add(self, obj):\n        factoriesInfo(\"Adding new Algorithm ==> %s \" % obj.getFullName())\n        addToTopSequence(obj)\n\n\ndef instantiateAll(module=None):\n    \"\"\"Instantiate all tools/algs defined by Factories in a given module.\n    WARNING: in alphabetical order, not in the order they are defined\"\"\"\n    return [obj() for obj in vars(module).values() if isinstance(obj, Factory)]\n","sub_path":"Reconstruction/egamma/egammaRec/python/Factories.py","file_name":"Factories.py","file_ext":"py","file_size_in_byte":9818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"45895591","text":"# -*- coding: utf-8 -*-\nimport os, utils, shutil, pyprind\nfrom copy import copy\nfolder = r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\external\\PEF\\PEF_chemicals\\technical\\cullet'\nfilename = 'missing_MD.txt'\nf = open(os.path.join(folder, filename))\nlines = f.readlines()\nf.close()\nmissing_MD = {}\nfor line in lines:\n    line = line.split('; ')\n    if 'sources' in line[-1] or 'source' in line[-1]:\n        key = 'sources'\n    elif 'unit group' in line[-1]:\n        key = 'unitgroups'\n    elif 'contact' in line[-1]:\n        key = 'contacts'\n    elif 'flow property' in line[-1]:\n        key = 'flowproperties'\n    elif 'flow' in line[-1]:\n        key = 'flows'\n    elif 'external_docs' in line[-1]:\n        key = 'external_docs'\n    else:\n        1/0\n    if key not in missing_MD:\n        missing_MD[key] = set()\n    u = utils.find_uuid(line[-1])\n    if len(u) == 0:\n        if key == 'external_docs':\n            filename = line[-1]\n        else:\n            filename = ''\n    elif len(u) == 1:\n        filename = copy(u[0])\n    else:\n        1/0\n    if '.xml' not in filename:\n        filename = filename + '.xml'\n    missing_MD[key].add(filename)\npotential_sources = [\n    r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\releases\\other_versions\\chemicals\\Allocation, cut-off\\datasets\\on the node\\ILCD', \n    r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\releases\\other_versions\\chemicals\\Allocation, cut-off\\datasets\\ILCD', \n    r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\releases\\other_versions\\chemicals_corrected\\Allocation, cut-off\\datasets\\ILCD', \n    r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\releases\\other_versions\\chemicals_corrected - 2018\\Allocation, cut-off\\datasets\\ILCD', \n    ]\nif 'external_docs' in missing_MD:\n    missing_MD['external_docs'] = ['']\nmissed = set()\nfor key, filenames in missing_MD.items():\n    for filename in pyprind.prog_bar(filenames, title = key):\n        if len(filename) > 4:\n            destination = os.path.join(r'C:\\Dropbox (ecoinvent)\\ei-int\\technical\\releases\\other_versions\\chemicals_corrected - 2018\\Allocation, cut-off\\datasets\\cullet', \n                key, filename)\n            for s in potential_sources:\n                source = os.path.join(s, key, filename)\n                try:\n                    shutil.copy(source, destination)\n                    break\n                except:\n                    if s == potential_sources[-1]:\n                        missed.add((key, filename))","sub_path":"projects/PEF_correction/3_cullet_to_ILCD_MD.py","file_name":"3_cullet_to_ILCD_MD.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"374234317","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n\"\"\"test_bid_ask.py: simulation for bid_ask.py\"\"\"\n\n__author__ = \"Niall O'Connor\"\n\nimport unittest\nimport bid_ask\n\nclass TestBidAsk(unittest.TestCase):\n    \"\"\"\n    Test the bid and ask generators.\n    Test order matching.\n    Test spread determination.\n    \"\"\"\n    def test_bids(self):\n        \"\"\"\n        Given a reference price use a random function to seed a number of prices.\n        \"\"\"\n        reference_price = 10.12\n        sigma = .1\n        total = 50\n        bids = bid_ask.generate_prices(reference_price, sigma, total)\n        self.assertEqual(len(bids), total)\n    \n    def test_order_matching(self):\n        \"\"\"\n        Given a list of bids and a list of asks. Find all matches\n        \n        logic.\n        \n        1. Find the lowest Ask.\n        2. Find the first bid that is >= to this ask.\n        3. Remove the bid and ask from each list.\n        4. When complete return the matched orders, remaining asks and remaining bids.\n        \"\"\"\n        # Inputs are bids and asks, expect output is matches and remainders.\n        test_harness = (\n            (   # inputs\n                {\n                    'bids': [10.01, 10.02, 10.04, 10.05, 10.06],\n                    'asks': [10.04, 10.05, 10.07, 10.09, 10.10]\n                },\n                # expected outputs\n                {\n                    'matches': [(10.04, 10.04,), (10.05, 10.05,)],\n                    'remaining_bids': [10.01, 10.02, 10.06],\n                    'remaining_asks': [10.07, 10.09, 10.10]\n                },\n            ),            \n        )\n        for given, expect in test_harness:\n            outputs = bid_ask.match_orders(given['bids'], given['asks'])\n            self.assertEqual(expect['remaining_bids'], outputs['remaining_bids'])\n            self.assertEqual(expect['remaining_asks'], outputs['remaining_asks'])\n            self.assertEqual(expect['matches'], outputs['matches'])\n\n","sub_path":"finance/montecarlo/test_bid_ask.py","file_name":"test_bid_ask.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"110749528","text":"import tornado.web\nimport re\nfrom fabric.api import *\nimport os\n\nimport logging\n\ntry:\n    from urllib.parse import unquote\nexcept ImportError:\n    # Python 2.\n    from urllib import unquote\n\nlogging.basicConfig(level=logging.INFO)\n\nFILE_DIR_PREFIX = \"/data/tfcodes/upload/\"\n\n\n# FILE_DIR_PREFIX = \"f:/test/\"\n\n\nclass FileHandler(tornado.web.RequestHandler):\n    def get(self):\n        self.write(\"Hello, world\")\n\n    def post(self):\n        for field_name, files in self.request.files.items():\n            for info in files:\n                filename, content_type = info[\"filename\"], info[\"content_type\"]\n                body = info[\"body\"]\n                filename = re.sub(\"\\\\\\\\\", \"/\", filename)\n                filename = re.split(\"/\", filename)[-1]\n                file = FILE_DIR_PREFIX + filename\n                logging.info(\"storing file:%s...\" % file)\n                if not os.path.exists(FILE_DIR_PREFIX):\n                    os.system(\"mkdir -p %s\" % FILE_DIR_PREFIX)\n                with open(file, 'wb') as f:\n                    f.write(body)\n                # print(body)\n                logging.info(\n                    'POST \"%s\" \"%s\" %d bytes', filename, content_type, len(body)\n                )\n                # to avoid path which contains \\ add get pure filename for source codes\n                # code_file = re.sub(\"\\\\\\\\\", \"/\")\n                # code_file = code_file.split(\"/\")[-1]\n\n        self.write(\"OK\")\n","sub_path":"lenovotf/web/handler/fileHandler.py","file_name":"fileHandler.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"445421230","text":"mealCost = float(input())\ntipPercent = int(input())\ntaxPercent = int(input())\nimport math\ntip = (mealCost/100)*tipPercent\ntax = (12/100)*taxPercent\nTotalCost =int(round(mealCost + tip + tax))\nprint(tip, tax)\n\nprint (\"The total meal cost is %i dollars.\" %TotalCost)\n","sub_path":"Day 2 - TotalMealCost - Hackerrank.py","file_name":"Day 2 - TotalMealCost - Hackerrank.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"412995982","text":"import logging\nimport logging.config\nimport platform\nimport docker\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker, scoped_session\nfrom hpotter.tables import Base\n\nlogging.config.fileConfig('hpotter/logging.conf')\nlogger = logging.getLogger('hpotter')\n\n# note sqlite:///:memory: can't be used, even for testing, as it\n# doesn't work with threads.\ndb = 'sqlite:///main.db'\nengine = create_engine(db)\n# engine = create_engine(db, echo=True)\nBase.metadata.create_all(engine)\nSession = scoped_session(sessionmaker(engine))\n\n# a start, for a Pi 0.\nmachine = 'arm32v6/' if platform.machine() == 'armv6l' else ''\n\nbusybox = True\nshell_container = None\n\ndef get_busybox():\n    return busybox\n\ndef get_shell_container():\n    return shell_container\n\ndef start_shell():\n    global shell_container\n    if shell_container:\n        logger.info('Shell container already started')\n        return\n\n    logger.info('Starting shell container')\n    client = docker.from_env()\n    if busybox:\n        shell_container = client.containers.run(machine + 'busybox:latest', \\\n            command=['/bin/ash'], tty=True, detach=True, read_only=True)\n    else:\n        shell_container = client.containers.run(machine + 'alpine:latest', \\\n            command=['/bin/ash'], user='guest', tty=True, detach=True, \\\n                read_only=True)\n\n    client.networks.get('bridge').disconnect(shell_container)\n\ndef stop_shell():\n    if not shell_container:\n        return\n\n    logger.info('Stopping shell container')\n    shell_container.stop()\n    logger.info('Removing shell container')\n    shell_container.remove()\n\njsonserverport = 8000\n\n# some singletons\ntelnet_server = None\nhttp500_server = None\nssh_server_thread = None\n","sub_path":"hpotter/env.py","file_name":"env.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"441721430","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for\n# license information.\n#\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is\n# regenerated.\n# --------------------------------------------------------------------------\n\nfrom msrest.serialization import Model\n\n\nclass Response(Model):\n    \"\"\"The response to a metrics query.\n\n    All required parameters must be populated in order to send to Azure.\n\n    :param cost: The integer value representing the cost of the query, for\n     data case.\n    :type cost: float\n    :param timespan: Required. The timespan for which the data was retrieved.\n     Its value consists of two datatimes concatenated, separated by '/'.  This\n     may be adjusted in the future and returned back from what was originally\n     requested.\n    :type timespan: str\n    :param interval: The interval (window size) for which the metric data was\n     returned in.  This may be adjusted in the future and returned back from\n     what was originally requested.  This is not present if a metadata request\n     was made.\n    :type interval: timedelta\n    :param namespace: The namespace of the metrics been queried\n    :type namespace: str\n    :param resourceregion: The region of the resource been queried for\n     metrics.\n    :type resourceregion: str\n    :param value: Required. the value of the collection.\n    :type value: list[~azure.mgmt.monitor.models.Metric]\n    \"\"\"\n\n    _validation = {\n        'cost': {'minimum': 0},\n        'timespan': {'required': True},\n        'value': {'required': True},\n    }\n\n    _attribute_map = {\n        'cost': {'key': 'cost', 'type': 'float'},\n        'timespan': {'key': 'timespan', 'type': 'str'},\n        'interval': {'key': 'interval', 'type': 'duration'},\n        'namespace': {'key': 'namespace', 'type': 'str'},\n        'resourceregion': {'key': 'resourceregion', 'type': 'str'},\n        'value': {'key': 'value', 'type': '[Metric]'},\n    }\n\n    def __init__(self, *, timespan: str, value, cost: float=None, interval=None, namespace: str=None, resourceregion: str=None, **kwargs) -> None:\n        super(Response, self).__init__(**kwargs)\n        self.cost = cost\n        self.timespan = timespan\n        self.interval = interval\n        self.namespace = namespace\n        self.resourceregion = resourceregion\n        self.value = value\n","sub_path":"azure-mgmt-monitor/azure/mgmt/monitor/models/response_py3.py","file_name":"response_py3.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"494263171","text":"\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# def get_max_close(symbol):\n    # df=pd.read_csv(\"data/{}.csv\".format(symbol))\n    # return df['Close'].max()\n    \ndef plot_data(df,title='Stock Prices'):\n    ax=df.plot(title=title,fontsize=10)\n    ax.set_xlabel(\"Date\")\n    ax.set_ylabel(\"Price\")\n    plt.show()\n    \ndef normalize_data(df):\n    \n    return df/df.ix[0,:]\n\ndef get_data(symbols,dates):\n    df=pd.DataFrame(index=dates)\n    dfSHARAK=pd.read_csv(\"data/SHARAK.csv\",index_col=\"GDate\",parse_dates=True,\n                          usecols=['GDate','ClosePrice'],na_values=['nan'])\n                          \n    dfSHARAK=dfSHARAK.rename(columns={'ClosePrice':'SHARAK'})\n    \n    # print(dfSHARAK)\n    df=df.join(dfSHARAK,how='inner')\n\n    for symbol in symbols:\n        df_temp=pd.read_csv(\"data/{}.csv\".format(symbol),index_col='GDate',\n                             parse_dates=True,usecols=['GDate','ClosePrice'],\n                             na_values=['nan'])\n                             \n        df_temp=df_temp.rename(columns={'ClosePrice':symbol})\n        df=df.join(df_temp)\n\n    return df\n\ndef get_rolling_mean(df,window):\n    r=df.rolling(window)\n    rm=r.mean()\n    return rm\n\ndef get_rolling_std(df,window):\n    s=df.rolling(window)\n    rstd=s.std()\n    return rstd\n\ndef get_bollinger_bands(rm,rstd):\n    upper_band=rm+2*rstd\n    lower_band=rm-2*rstd\n    return upper_band,lower_band\n    \ndef compute_daily_returns(df):\n    # daily_returns=df.copy()\n    # daily_returns[1:]=(df[1:]/df[:-1].values)-1\n    # daily_returns.ix[0,:]=0\n    daily_returns=(df/df.shift(1))-1\n    return daily_returns\n\ndef compute_cumulative_return(df):\n    print(df)\n    cumulative_return=df.copy()\n    cumulative_return[1:]=(df[1:]/df[0,:].values)-1\n    cumulative_return.ix[0,:]=0\n    return cumulative_return\n\ndef test_run():\n    start_date='2019-03-06'\n    end_date='2019-03-16'\n    dates=pd.date_range(start_date,end_date)\n    \n    # symbols=['VBMELLAT','SHPETRO','KHODRO','FAMLI']\n    symbols=['VBMELLAT','KHODRO']\n\n    df=get_data(symbols,dates)\n            \n    cumulative_return=compute_cumulative_return(df)\n    print(cumulative_return)\n\n\t\n    \n    # ax1=df[symbols].plot(title=\"Bollinger Bands\",label=\"sABER\")\n    # rm_SMBL.plot(label=\"Rolling mean\",ax=ax1)\n    # upper_band.plot(label=\"upper band\",ax=ax1)\n    # lower_band.plot(label=\"lower band\",ax=ax1)\n    # plt.show()\n\n\t\n\t\nif __name__ == \"__main__\":\n    test_run()","sub_path":"CumulativeReturn.py","file_name":"CumulativeReturn.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"483645028","text":"from __future__ import division\r\nimport sys\r\nsys.path.append('/home/sciortino/ML')\r\n\r\nimport numpy as np\r\nimport gptools\r\nimport scipy\r\n\r\n#Input independent variable x and y=f(x) (same dimensions), along with the number of splines to be\r\n#fit, N. \r\n\r\ndef spline_MC(xin,yin,xout,yin_err,n_trial, plot=True):\r\n\t''' FS version '''\r\n\tyin = np.atleast_2d(yin)\r\n\tyin_err = np.atleast_2d(yin_err)\r\n\tif yin.shape == 1: \r\n\t\tyin=yin.T\r\n\tif yin_err.shape == 1: \r\n\t\tyin_err=yin_err.T\r\n\r\n\ty =  np.tile(yin,(n_trial,1))\r\n\r\n\t# Randomly assign y values to the input data within the error bounds\r\n\ty_perturbed = y + yin_err*np.random.standard_normal(y.shape)\r\n\r\n\t# check which times are giving nans:\r\n\tnan_bool = np.isnan(y_perturbed[0])  # assume all channels behave the same\r\n\txin_clean = xin[~nan_bool]\r\n\ty_perturbed_clean = y_perturbed[:,~nan_bool]\r\n\r\n\t# Interpolate onto locations specified in 'xout'\r\n\tf = scipy.interpolate.interp1d(xin_clean, y_perturbed_clean, kind='cubic')\r\n\ty_fit = f(xout)\r\n\r\n\t# Calculate mean and standard deviation at each xout point:\r\n\tm = np.mean(y_fit,axis=0)\r\n\tsigma = np.std(y_fit,axis=0)\r\n\r\n\t# Plot\r\n\tif plot:\r\n\t\tgptools.univariate_envelope_plot(xout, m, sigma)\r\n\r\n\treturn m, sigma\r\n\r\n# # Function defined to plot the error of the spline fit\r\n# def errorfill(x, y, yerr, color=None, alpha_fill=0.3, ax=None):\r\n#     ax = ax if ax is not None else plt.gca()\r\n#     if color is None:\r\n#         color = ax._get_lines.color_cycle.next()\r\n#     if np.isscalar(yerr) or len(yerr) == len(y):\r\n#         ymin = y - yerr\r\n#         ymax = y + yerr\r\n#     elif len(yerr) == 2:\r\n#         ymin, ymax = yerr\r\n#     ax.plot(x, y, color=color)\r\n#     ax.fill_between(x, ymax, ymin, color=color, alpha=alpha_fill)","sub_path":"spline_MC.py","file_name":"spline_MC.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"342988499","text":"# coding=utf-8\nimport os\n\nPAGE_SIZE = 20\n\nTEST_SERVER = 'http://' + os.environ.get(\"config_test_uri\")  # 'http://192.168.132.79'\n_IS_LOCAL_DEBUG_ENV = True if os.environ.get('webspider_local_debug','no') == 'yes' else False\n_IS_ALPHA_ENV = True if os.environ.get('visual_env') == 'alpha' else False\n\nXGSJ_DOMAIN = 'https://xgsj3-alpha.istarshine.com' if _IS_ALPHA_ENV else \"https://xgsj.istarshine.com\"\n\nif _IS_LOCAL_DEBUG_ENV:\n    STATIC_TEMP_DIR = \"http://172.16.254.15:8000\"\nelif _IS_ALPHA_ENV:\n    STATIC_TEMP_DIR = \"https://webspider-alpha.istarshine.com\"\nelse:\n    STATIC_TEMP_DIR = \"https://webspider.istarshine.com\"\n","sub_path":"webSpider/setting.py","file_name":"setting.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"551832921","text":"import tensorflow as tf\nimport sys\nimport utils\nimport numpy as np\n\nslim = tf.contrib.slim\n\nfilePath = \"C:/Users/s123028/TIMIT_ref_3k_butter/\"\n\n#wavSamplesFeature, wavSamplesLabel = utils.inputFn(filePath)\n\n#np.save('wavSamplesFeature.npy',wavSamplesFeature)\n#np.save('wavSamplesLabel.npy',wavSamplesLabel)\n\nwavSamplesFeature = np.load('wavSamplesFeature.npy')\nwavSamplesLabel = np.load('wavSamplesLabel.npy')\n\n\n### Apply muLaw comprsiion to all files\nassert wavSamplesFeature.shape == wavSamplesLabel.shape\nd0, d1 = wavSamplesFeature.shape\nfor i in range(0,d0):\n\twavSamplesFeature[i,:] = utils.muLawCompression(wavSamplesFeature[i,:])\n\twavSamplesLabel[i,:] = utils.muLawCompression(wavSamplesLabel[i,:])\nassert wavSamplesFeature.shape == wavSamplesLabel.shape\nprint(np.min(wavSamplesLabel))\n###\n\n\n# Constructing dataset with placeholders\nfeatures_placeholder = tf.placeholder(wavSamplesFeature.dtype, wavSamplesFeature.shape)\n\nlabels_placeholder = tf.placeholder(wavSamplesLabel.dtype, wavSamplesLabel.shape)\n\n# Converts the features and labels to tf.datasets\ndataFeatures = tf.data.Dataset.from_tensor_slices(features_placeholder)\ndataLabels = tf.data.Dataset.from_tensor_slices(labels_placeholder)\n\n# Combines the features and labels in one Dataset\ndataset = tf.data.Dataset.zip((dataFeatures, dataLabels))\n\n### Batching of the dataset\ndataset = dataset.shuffle(buffer_size=20000)\nbatched_dataset = dataset.batch(1)\niterator = batched_dataset.make_initializable_iterator()\nnext_feat, next_label = iterator.get_next()\n\nnext_element = (next_feat,next_label)\n\nnext_feat_pl = tf.placeholder(next_feat.dtype,next_feat.shape)\nnext_label_pl = tf.placeholder(next_label.dtype,next_label.shape)\n\n\n\n\n\n### The dilated CNN model\nfeatures = tf.cast(next_feat_pl,tf.float32)\nprint(tf.shape(features))\n\nwhile True:\n\ttry:\n\n\t\tpredictions = tf.nn.conv1d(features, 32.0, stride=1, padding='VALID')\n\texcept tf.errors.OutOfRangeError:\n\t\tbreak\n\n\n\n\n\n\n### Training ops\nlabels = list(range(0,255))\nprint(type(labels))\noneHot = tf.one_hot(labels,len(labels))\n\nprint(oneHot)\n\n\nloss = slim.losses.softmax_cross_entropy(predictions, labels)\noptimizer = tf.train.GradientDescentOptimizer(0.01)\ntrain_op = optimizer.minimize(loss)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n### The training loop\nforCount = 0\nwith tf.Session() as sess:\n\tsess.run(tf.global_variables_initializer())\n\tfor epoch in range(1):\n\t\tsess.run(iterator.initializer, feed_dict={features_placeholder: wavSamplesFeature, labels_placeholder: wavSamplesLabel})\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tforCount +=1\n\t\t\t\tnext_feat,next_label = sess.run(next_element)\n\t\t\t\t#fetches_train = [train_op, loss,y]\n\t\t\t\t#feed_dict_train = {next_feat_pl: next_feat, next_label_pl: next_label}\n\n\t\t\t\tprint(np.max(next_label))\n\t\t\t\t#print(next_label)\n\n\t\t\t\t#res_train = sess.run(fetches=fetches_train, feed_dict=feed_dict_train)\n\t\t\texcept tf.errors.OutOfRangeError:\n\t\t\t\tbreak\n\n\nprint(np.max(next_label))\n","sub_path":"D-CNN.py","file_name":"D-CNN.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"445766046","text":"\nclass LeagueTableStanding(object):\n    \"\"\"\n    League Table standing.\n    \"\"\"\n    def __init__(self,\n                 position,\n                 team_name,\n                 played_games,\n                 crest_url,\n                 points,\n                 goals,\n                 goals_against,\n                 goal_difference,\n                 wins,\n                 draws,\n                 loses,\n                 home_goals,\n                 home_against,\n                 home_wins,\n                 home_draws,\n                 home_loses,\n                 away_goals,\n                 away_against,\n                 away_wins,\n                 away_draws,\n                 away_loses):\n        \"\"\"\n\n        :param position:\n        :param team_name:\n        :param played_games:\n        :param crest_url:\n        :param points:\n        :param goals:\n        :param goals_against:\n        :param goal_difference:\n        :param wins:\n        :param draws:\n        :param loses:\n        :param home_goals:\n        :param home_against:\n        :param home_wins:\n        :param home_draws:\n        :param home_loses:\n        :param away_goals:\n        :param away_against:\n        :param away_wins:\n        :param away_draws:\n        :param away_loses:\n        :return:\n        \"\"\"\n        self.position = position\n        self.team_name = team_name\n        self.played_games = played_games\n        self.crest_url = crest_url\n        self.points = points\n        self.goals = goals\n        self.goals_against = goals_against\n        self.goal_difference = goal_difference\n        self.wins = wins\n        self.draws = draws\n        self.loses = loses\n        self.home_goals = home_goals\n        self.home_against = home_against\n        self.home_wins = home_wins\n        self.home_draws = home_draws\n        self.home_loses = home_loses\n        self.away_goals = away_goals\n        self.away_against = away_against\n        self.away_wins = away_wins\n        self.away_draws = away_draws\n        self.away_loses = away_loses\n","sub_path":"football_data_dot_org/data_objects/league_table_standing.py","file_name":"league_table_standing.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"199562454","text":"import signal\nimport subprocess\nfrom django.core.management.base import BaseCommand, CommandError\nimport logging\nimport atexit\nimport time\nimport os\nimport sys\nlogging.basicConfig(format=u'%(filename)s[LINE:%(lineno)d]# %(levelname)-8s [%(asctime)s]  %(message)s',\n                    level=logging.DEBUG, filename='/var/log/cryptopiper/krakenAPI.log')\n\n\ndef poloniex_subprocess():\n    try:\n        script_path = os.path.join(os.path.dirname(__file__), '../../../Exchanges/ExchangeAPI/KrakenAPI.py')\n    except FileNotFoundError:\n        logging.error(u'KrakenAPI process could not be reached. Check File path')\n        sys.exit(-1)\n    poloniex_command = [sys.executable, script_path]\n    try:\n        poloniex_pipe = subprocess.Popen(poloniex_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE)\n    except OSError:\n        logging.error(u'KrakenAPI process could not be started. Check logs.')\n        sys.exit(-1)\n    # PID Declaration\n    poloniex_pid = poloniex_pipe.pid\n    return poloniex_pid\n\n\n# Log config should be changed\nclass Command(BaseCommand):\n    def handle(self, *args, **options):\n        logging.info(u'Process manager started')\n        try:\n            # windows/mac debug starter\n            if sys.platform == 'win32' or 'darwin':\n                try:\n                    process = poloniex_subprocess()\n                    logging.info(u\"Process is successfully started\")\n                    while True:\n                        time.sleep(0.1)\n                except OSError:\n                    logging.error(u'Process was not started\\nTerminating command')\n                    sys.exit(0)\n            else:\n                # Production starter\n                logging.info(u'Production process starter successfully started')\n                parent_pipe, child_pipe = os.pipe()\n                try:\n                    pid = os.fork()\n                    if pid == 0:\n                        os.close(child_pipe)\n                        sys.stdin.close()\n                        sys.stdout.close()\n                        sys.stderr.close()\n                        process = poloniex_subprocess()\n                        while True:\n                            time.sleep(0.1)\n                    else:\n                        os._exit(0)\n                except OSError:\n                    logging.error(u'Process was not started on production\\nTerminating command')\n                    sys.exit(-1)\n        except OSError:\n            logging.critical(u'Process manager is unreachable. Check CODE')\n        try:\n            atexit.register(os.kill(process, signal.SIGTERM))\n        except OSError:\n            logging.critical(u'Process manager cannot kill processes\\nUse `kill` command in terminal')\n","sub_path":"Exchanges/management/commands/kraken_start.py","file_name":"kraken_start.py","file_ext":"py","file_size_in_byte":2732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"638164272","text":"from threading import Thread, Lock\nfrom collections import OrderedDict\nfrom multiprocessing import cpu_count\nfrom logging import getLogger, FileHandler, StreamHandler, Formatter, DEBUG\nfrom os.path import isdir\nfrom time import sleep\nfrom os import mkdir\n\nfrom . import cache, conform, CacheResult, ConformResult\n\nfrom . import excerpt, ExcerptResult\n\ndef run_all_caches(source_files, source_extras, s3):\n    ''' Run cache() for all source files in parallel, return a dict of results.\n    '''\n    source_queue, results = source_files[:], OrderedDict()\n    args = Lock(), source_queue, source_extras, results, s3\n    thread_count = min(cpu_count() * 2, len(source_files))\n\n    threads = [Thread(target=_run_cache, args=args)\n               for i in range(thread_count)]\n    \n    if len(source_files) > thread_count:\n        threads.append(Thread(target=_run_timer, args=(source_queue, 15)))\n\n    _wait_for_threads(threads, source_queue)\n    \n    return results\n\ndef _run_cache(lock, source_queue, source_extras, results, s3):\n    ''' Single queue worker for source files to conform().\n    \n        Keep going until source_queue is empty.\n    '''\n    while True:\n        with lock:\n            if not source_queue:\n                return\n            path = source_queue.pop(0)\n            extras = source_extras.get(path, dict())\n    \n        try:\n            if not isdir('out'):\n                mkdir('out')\n        \n            getLogger('openaddr').info(path)\n            result = cache(path, 'out', extras, s3)\n        except:\n            result = CacheResult.empty()\n        \n        with lock:\n            results[path] = result\n\ndef run_all_conforms(source_files, source_extras, s3):\n    ''' Run conform() for all source files in parallel, return a dict of results.\n    '''\n    source_queue, results = source_files[:], OrderedDict()\n    args = Lock(), source_queue, source_extras, results, s3\n    thread_count = min(cpu_count(), len(source_files))\n\n    threads = [Thread(target=_run_conform, args=args)\n               for i in range(thread_count)]\n    \n    if len(source_files) > thread_count:\n        threads.append(Thread(target=_run_timer, args=(source_queue, 15)))\n\n    _wait_for_threads(threads, source_queue)\n    \n    return results\n\ndef _run_conform(lock, source_queue, source_extras, results, s3):\n    ''' Single queue worker for source files to conform().\n    \n        Keep going until source_queue is empty.\n    '''\n    while True:\n        with lock:\n            if not source_queue:\n                return\n            path = source_queue.pop(0)\n            extras = source_extras.get(path, dict())\n    \n        try:\n            if not isdir('out'):\n                mkdir('out')\n        \n            getLogger('openaddr').info(path)\n            result = conform(path, 'out', extras, s3)\n        except:\n            result = ConformResult.empty()\n        \n        with lock:\n            results[path] = result\n\ndef run_all_excerpts(source_files, source_extras, s3):\n    ''' Run excerpt() for all source files in parallel, return a dict of results.\n    '''\n    source_queue, results = source_files[:], OrderedDict()\n    args = Lock(), source_queue, source_extras, results, s3\n    thread_count = min(cpu_count(), len(source_files))\n\n    threads = [Thread(target=_run_excerpt, args=args)\n               for i in range(thread_count)]\n    \n    if len(source_files) > thread_count:\n        threads.append(Thread(target=_run_timer, args=(source_queue, 15)))\n\n    _wait_for_threads(threads, source_queue)\n    \n    return results\n\ndef _run_excerpt(lock, source_queue, source_extras, results, s3):\n    ''' Single queue worker for source files to excerpt().\n    \n        Keep going until source_queue is empty.\n    '''\n    while True:\n        with lock:\n            if not source_queue:\n                return\n            path = source_queue.pop(0)\n            extras = source_extras.get(path, dict())\n    \n        try:\n            if not isdir('out'):\n                mkdir('out')\n        \n            getLogger('openaddr').info(path)\n            result = excerpt(path, 'out', extras, s3)\n        except:\n            result = ExcerptResult.empty()\n        \n        with lock:\n            results[path] = result\n\ndef _run_timer(source_queue, interval):\n    ''' Natter on and on about how much of the queue is left.\n    '''\n    sleep(interval)\n\n    while source_queue:\n        getLogger('openaddr').debug('{0} source files remain'.format(len(source_queue)))\n        sleep(interval)\n\ndef setup_logger(logfile):\n    ''' Set up logging stream and optional file for 'openaddr' logger.\n    '''\n    format = '%(threadName)11s  {0} %(levelname)06s: %(message)s'\n    getLogger('openaddr').setLevel(DEBUG)\n    \n    for old_handler in getLogger('openaddr').handlers:\n        getLogger('openaddr').removeHandler(old_handler)\n    \n    handler1 = StreamHandler()\n    handler1.setFormatter(Formatter(format.format('%(relativeCreated)10.1f')))\n    getLogger('openaddr').addHandler(handler1)\n\n    if logfile:\n        handler2 = FileHandler(logfile, mode='w')\n        handler2.setFormatter(Formatter(format.format('%(asctime)s')))\n        getLogger('openaddr').addHandler(handler2)\n\ndef _wait_for_threads(threads, queue):\n    ''' Run all the threads and wait for them, but catch interrupts.\n        \n        If a keyboard interrupt is caught, empty the queue and let threads finish.\n    '''\n    try:\n        # Start all the threads.\n        for thread in threads:\n            thread.start()\n\n        # Check with each thread once per second, to stay interruptible.\n        while threads:\n            for thread in threads:\n                thread.join(1)\n                if not thread.isAlive():\n                    # Bury the dead.\n                    threads.remove(thread)\n                    break\n\n    except (KeyboardInterrupt, SystemExit):\n        getLogger('openaddr').info('Cancel with {0} tasks left'.format(len(queue)))\n        while queue:\n            # Empty the queue to stop the threads.\n            queue.pop()\n        raise\n","sub_path":"openaddr/jobs.py","file_name":"jobs.py","file_ext":"py","file_size_in_byte":6026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"118396852","text":"import logging\n\n#logger 收集日志\n#handdler 输出日志的渠道 指定的文件 还是控制台（默认控制台）\n# logging.debug()\n# logging.info()\n# logging.warning()\n# logging.error()\n# logging.critical()\n\n#定义一个日志收集器 my_logger\nmy_logger=logging.getLogger()\n#设定级别\nmy_logger.setLevel('DEBUG')\n\n#创建一个我们自己的输出渠道\nch=logging.StreamHandler()\nch.setLevel('DEBUG')\nfh=logging.FileHandler('log.txt',encoding='utf-8')\nfh.setLevel('DEBUG')\n\n#formatter:决定日志记录的最终输出格式（不然日志会乱七八糟的）Formatter对象定义了最终log信息的顺序，结构和内容\nformatter=logging.Formatter('%(asctime)s-%(levelname)s-%(filename)s-%(name)s-日志信息：%(message)s')\nch.setFormatter(formatter)\nfh.setFormatter(formatter)\n\n#两者对接\nmy_logger.addHandler(ch)\nmy_logger.addHandler(fh)\n#收集日志\nmy_logger.debug('python自动化测试学习')","sub_path":"tools/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"257140217","text":"from centrale.taskly import Task\n\n# run on the second working day of the month\n\nno_prod = True\n\n# args = ['sibling', '--no-prod', '-f', '2011-09']\n# if no_prod: args.append['--no-prod']\n# task = Task(args)\n# task.do()\n\n# args = ['ethe']\n# if no_prod: args.append('--no-prod')\n# task = Task(args)\n# task.do()\n# task.handler.storage.close()\n\nargs = ['disseminate', '-k', 'csdb']\nif no_prod: args.append('--no-prod')\ntask = Task(args)\ntask.do()\n","sub_path":"csdb_disseminate_script.py","file_name":"csdb_disseminate_script.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"68198012","text":"from itertools import chain, combinations\nfrom operator import mul\nfrom functools import reduce\nimport csv\nfrom mpmath import mp\nmp.dps=30\nmp.pretty=True\n\ndef append_data(filename, rows):\n    with open(filename, 'a', newline='') as csvfile:\n        writer = csv.writer(csvfile, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n        writer.writerows(rows)\n\ndef alpha1(s):\n    return 0.5/s + 1/(s - 1) + 0.5 * mp.log(s/(2 * mp.pi()))\n\ndef H01(s):\n    return 0.5 * s * (s-1) * mp.power(mp.pi(), -0.5 * s) * mp.sqrt(2 * mp.pi()) * mp.exp((0.5 * s - 0.5) * mp.log(0.5 * s)-0.5 * s)\n\ndef habbbound(N,y=0.4,t=0.4):\n    xN = 4*mp.pi()*N*N - t*mp.pi()/4.0\n    xNp1 = 4*mp.pi()*(N+1)*(N+1) - t*mp.pi()/4.0\n    T1 = xN/2.0\n    T2 = xNp1/2.0\n    Tdash1 = T1 + t*mp.pi()/8.0\n    Tdash2 = T2 + t*mp.pi()/8.0\n    sNminus = 0.5*(1-y + 0.5j*(xN+xNp1))\n    sNplus = 0.5*(1+y + 0.5j*(xN+xNp1))\n    alph_sNminus, alph_sNplus = alpha1(sNminus), alpha1(sNplus)\n    K = (xNp1 - xN)/(4.0*(xN-6))\n    delta = mp.pi()*y/(2*(xN - 6 - (14 + 2*y)/mp.pi())) + 2*y*(7+y)*mp.log(abs(1+y+1j*xNp1)/(4*mp.pi))/(xN*xN)\n    lambdafac = mp.exp(delta)/mp.power(N,y)\n    a0 = mp.sqrt(Tdash1/(2*mp.pi()))\n    \n    epsdash1, epsdash2 = 0.0, 0.0\n    for n in range(1,N+1):\n        nf = mp.mpf(n)\n        modterm_sNminus, modterm_sNplus = abs(alph_sNminus - mp.log(n)), abs(alph_sNplus - mp.log(n)) \n        cnminus = (t*t/4.0)*(modterm_sNminus**2 + 2*K*modterm_sNminus + K*K) + (1/3.0) + t\n        cnplus = (t*t/4.0)*(modterm_sNplus**2 + 2*K*modterm_sNplus + K*K) + (1/3.0) + t\n        epsdash1+= cnminus*mp.exp(cnminus/(2*(T1-3.33)))/mp.power(nf,0.5*(1-y) + 0.5*t*(alph_sNminus.real - K) - 0.25*t*mp.log(n))\n        epsdash2+= cnplus*mp.exp(cnplus/(2*(T1-3.33)))/mp.power(nf,0.5*(1+y) + 0.5*t*(alph_sNplus.real - K) - 0.25*t*mp.log(n))\n    normalized_E1 = lambdafac*0.5*epsdash1/(T1-3.33)\n    normalized_E2 = 0.5*epsdash2/(T1-3.33)\n    \n    E3_by_E3_main_decay = (1/8.0)*mp.sqrt(mp.pi()) * mp.exp(-1*(t/64.0)*(mp.pi()**2))*mp.exp(0.181/(Tdash1 - 3.33))*(1+5.15/(a0-1.25)) \n    E3_main_decay_term = mp.power(Tdash1, 1.5) * mp.exp(-1 * mp.pi() * T1/4.0)\n    s2 = 0.5*(1+y) + 1j*T1\n    alph2 = alpha1(s2).conjugate()\n    B0_expo = (t/4.0)*alph2*alph2\n    H01_est2 = H01(s2).conjugate()\n    B0_eff = (1/8.0)*mp.exp(B0_expo) * H01_est2\n    E3_main_decay_by_B0eff = abs(E3_main_decay_term/B0_eff)\n    normalized_E3 = E3_by_E3_main_decay*E3_main_decay_by_B0eff\n    normalized_E = normalized_E1 + normalized_E2 + normalized_E3\n    \n    return [N, normalized_E1, normalized_E2,E3_by_E3_main_decay,E3_main_decay_by_B0eff,normalized_E3,normalized_E]\n\n'''for N in range(1,501):\n    print(habbbound(N))'''\n\ndef normalized_E3_xbound(x,y=0.4,t=0.4):\n    T = x/2.0\n    Tdash = T + t*mp.pi()/8.0\n    a0 = mp.sqrt(Tdash/(2*mp.pi()))\n            \n    E3_C0 = (1/8.0)*mp.sqrt(mp.pi()) * mp.exp(-1*(t/64.0)*(mp.pi()**2)) * mp.power(Tdash, 1.5) * mp.exp(-1 * mp.pi() * T/4.0)\n    E3_vwf_bound = mp.exp(0.181/(Tdash - 3.33))*(1+5.15/(a0-1.25))\n    E3 = E3_C0*E3_vwf_bound\n    s2 = 0.5*(1+y) + 1j*T\n    alph2 = alpha1(s2).conjugate()\n    B0_expo = (t/4.0)*alph2*alph2\n    H01_est2 = H01(s2).conjugate()\n    B0_eff = (1/8.0)*mp.exp(B0_expo) * H01_est2\n    normalized_E3 = abs(E3/B0_eff)\n    return [x, normalized_E3]\n\n'''import time\ns=time.time()\nmp.pretty=True\ny=0.4\nt=0.4\nxstart = 1.0\nxend = xstart + 6*(10**5)\nmesh_size = 0.1\ncsvfile=\"normalized_e3_data.csv\"\nevaldata=[]\ni=0\nx=xstart\nwhile x < xend: \n    zrow = [t,y]+normalized_E3_xbound(x)\n    evaldata.append(zrow)\n    #print(zrow)\n    if i % 5000 == 0: append_data(csvfile, evaldata); print(i,x); evaldata = []\n    x += mesh_size\n    i += 1\n\ne=time.time()\ne-s'''\n\n\n","sub_path":"dbn_upper_bound/python/research/habb_bound.py","file_name":"habb_bound.py","file_ext":"py","file_size_in_byte":3666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"581160384","text":"#!/usr/bin/env python\nimport sys\nfrom subprocess import Popen\n\nif sys.version_info[0] < 3:  # python 2\n    import BaseHTTPServer\nelse:  # python 3\n    import http.server as BaseHTTPServer\n\nPORT = 7531\nARGS = sys.argv[1:]\n\nif sys.version_info[0] < 3:\n    class CompatibilityMixin:\n        def send_body(self, msg):\n            self.wfile.write(msg+'\\n')\n            self.wfile.close()\nelse:\n    class CompatibilityMixin:\n        def send_body(self, msg):\n            self.wfile.write(bytes(msg+'\\n', 'utf-8'))\n\n\nclass Handler(BaseHTTPServer.BaseHTTPRequestHandler, CompatibilityMixin):\n    def respond(self, code, body=None):\n        self.send_response(code)\n        self.send_header(\"Content-type\", \"text/plain\")\n        self.end_headers()\n        if body:\n            self.send_body(body)\n\n    def do_GET(self):\n        url = self.path.split(\"?\",1)[1].split(\"=\",1)\n        print(url)\n        if url[0] == \"play_url\":\n            url = url[1]\n            if url.startswith('magnet:') or url.endswith('.torrent'):\n                pipe = Popen(['peerflix', '-k',  url, '--'] + ARGS)\n            else:\n                pipe = Popen(['mpv', url] + ARGS)\n            self.respond(200, \"playing...\")\n        elif url[0] == \"cast_url\":\n            url = url[1]\n            if url.startswith('magnet:') or url.endswith('.torrent'):\n                print(\" === WARNING: Casting torrents not yet fully supported!\")\n                with Popen(['mkchromecast', '--video',\n                            '--source-url', 'http://localhost:8888']):\n                    pass\n                pipe.terminate()\n            else:\n                pipe = Popen(['mkchromecast', '--video', '-y', url])\n            self.respond(200, \"casting...\")\n\n        else:\n            self.respond(400)\n\n\ndef start():\n    httpd = BaseHTTPServer.HTTPServer((\"\", PORT), Handler)\n    print(\"serving at port {}\".format(PORT))\n    print(\"with args {}\".format(ARGS))\n    try:\n        httpd.serve_forever()\n    except KeyboardInterrupt:\n        print(\" shutting down...\")\n        httpd.shutdown()\n\n\nif __name__ == '__main__':\n    start()\n","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"464625864","text":"import os\r\nimport sys\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\n\r\ncur_path = os.path.abspath(__file__)\r\ncur_dir = os.path.dirname(cur_path)\r\npar_dir = os.path.dirname(cur_dir)\r\ngra_dir = os.path.dirname(par_dir)\r\nsys.path.append(cur_dir)\r\nsys.path.append(par_dir)\r\nsys.path.append(gra_dir)\r\n\r\nfrom math23k.gnn.GraphConvolution import GraphConvolution\r\n\r\n\r\nclass GCN(nn.Module):\r\n    def __init__(self, in_feat_dim, nhid, out_feat_dim, dropout):\r\n        super(GCN, self).__init__()\r\n        '''\r\n        ## Inputs:\r\n        - graph_nodes (batch_size, K, in_feat_dim): input features\r\n        - adjacency matrix (batch_size, K, K)\r\n        ## Returns:\r\n        - gcn_enhance_feature (batch_size, K, out_feat_dim)\r\n        '''\r\n        self.gc1 = GraphConvolution(in_feat_dim, nhid)\r\n        self.gc2 = GraphConvolution(nhid, out_feat_dim)\r\n        self.dropout = dropout\r\n\r\n    def forward(self, x, adj):\r\n        # x:   [batch_size, seq_len, hidden_size]\r\n        # adj: [batch_size, seq_len, seq_len]\r\n        x = F.relu(self.gc1(x, adj))\r\n        x = F.dropout(x, self.dropout, training=self.training)\r\n        x = self.gc2(x, adj)\r\n        return x","sub_path":"math23k/gnn/GCN.py","file_name":"GCN.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"35129449","text":"# Created by TheGullibleKid at 8/09/2021\n# @author: mumble-07\n\nfrom tkinter import *\nimport board\nimport busio\nimport math\nfrom math import pow\nfrom time import sleep, strftime\nfrom datetime import datetime\nimport numpy as np\nimport adafruit_ads1x15.ads1115 as ADS\nfrom adafruit_ads1x15.analog_in import AnalogIn\nfrom firebase import firebase\n\ni2c =busio.I2C(board.SCL,board.SDA)\nads = ADS.ADS1115(i2c)\nchan = AnalogIn(ads,ADS.P0)\n\n\nTGS_2600 = 1 #use to declare sensor type\nTGS_2602 = 2 \nTGS_2611 = 3\nTGS_2620 = 4\n#================= Constants =================\nVc = 5 #volts from data sheet\ntime_interval = .1 #in secs\niteration = 10 #times\n#================= TGS-2600 Definition =================\nRL_2600 = 20000 #in ohm RL min\nRo_2600 = 50600 #in Ohm\nRo_factor_2600 = 1\ngas_CO = np.array([1.144997421, -0.21687423], dtype=float)\n#================= TGS-2602 Definition =================\nRL_2602 = 4700 #in ohm RL min\nRo_2602 = 5040 #in Ohm\nRo_factor_2602 = 1\ngas_toulene = np.array([0.163063, -1.63343], dtype=float)\ngas_ammonia = np.array([0.93178, -3.10356], dtype=float)\n#================= TGS-2611 Definition =================\nRL_2611 = 4700 #in ohm RL min\nRo_2611 = 18200 #in Ohm\nRo_factor_2611 = 9\ngas_methane = np.array([5090.43, -2.26059], dtype=float)\n#================= TGS-2620 Definition =================\nRL_2620 = 4700 #in ohm RL min\nRo_2620 = 8500 #in Ohm\nRo_factor_2620 = 20\ngas_ethanol = np.array([321.229, -1.57881], dtype=float)\n#================= Functions =================\n\n#================= RS CALCULATION =================\ndef sensor_read():\n    Rs_read= np.array([0,0,0,0],dtype=float)\n    Rs_read[0] = rs_read_sensor (TGS_2600, Vc, RL_2600, time_interval, iteration) #Rs_read_2600\n    Rs_read[1] = rs_read_sensor (TGS_2602, Vc, RL_2602, time_interval, iteration) #Rs_read_2602\n    Rs_read[2] = rs_read_sensor (TGS_2611, Vc, RL_2611, time_interval, iteration) #Rs_read_2611\n    Rs_read[3] = rs_read_sensor (TGS_2620, Vc, RL_2620, time_interval, iteration) #Rs_read_2620\n    return Rs_read\n\n\ndef rs_read_sensor (gaspin, sv, rl, ti, times):\n    Rs_sum = 0\n    i_times = 0\n    while times > i_times:\n         if gaspin == 1:\n            tgs_value = AnalogIn(ads,ADS.P0)\n         elif gaspin == 2:\n           tgs_value = AnalogIn(ads,ADS.P1)\n         elif gaspin ==3:\n           tgs_value = AnalogIn(ads,ADS.P2)\n         elif gaspin == 4:\n           tgs_value = AnalogIn(ads,ADS.P3)        \n        \n         Rs = ((sv*rl)/((tgs_value.value * sv) / 37750))-rl\n         Rs_sum = Rs_sum + Rs\n         sleep(ti)\n         i_times = i_times + 1\n    #print (Rs,\" \", Rs_sum)\n \n    Ave_Rs = Rs_sum/times\n  \n    return Ave_Rs\n\ndef v_read_sensor (RSarray, sv):\n    rs = RSarray\n      \n    Ave_volts = np.array([0,0,0,0],dtype=float)  \n    Ave_volts[0]  = (sv*20000)/(RSarray[0]+20000)\n    Ave_volts[1]  = (sv*4700)/(RSarray[1]+4700)\n    Ave_volts[2]  = (sv*4700)/(RSarray[2]+4700)\n    Ave_volts[3]  = (sv*4700)/(RSarray[3]+4700)\n    \n  \n    return Ave_volts\n\n#================= SENSOR CURVES =================\ndef gas_ppm (rs, ro, gas_type, ro_factor):\n    \n    ro_prime = ro * math.exp(math.log(gas_type[0]/ro_factor)/gas_type[1])\n    ppm = gas_type[0] * pow((rs/ro_prime), gas_type[1])\n    \n    \n    return ppm\n\ndef gas_write():\n    Rs_read_value = sensor_read()\n    ppm_gas= np.array([0,0,0,0,0],dtype=float, )\n    ppm_gas[0]= gas_ppm (Rs_read_value[0], Ro_2600, gas_CO, Ro_factor_2600)\n    ppm_gas[1] = gas_ppm (Rs_read_value[1], Ro_2602, gas_toulene, Ro_factor_2602)\n    ppm_gas[2] = gas_ppm (Rs_read_value[1], Ro_2602, gas_ammonia, Ro_factor_2602)\n    ppm_gas[3] = gas_ppm (Rs_read_value[2], Ro_2611, gas_methane, Ro_factor_2611)\n    ppm_gas[4] = gas_ppm (Rs_read_value[3], Ro_2620, gas_ethanol, Ro_factor_2620)\n    return np.around(ppm_gas, 3)\n\n\n# GUI FUNCTIONS\n\ndef Reset_on_Click():\n    gas_write()\n    print(\"Force Refresh\")\n\n\ndef close_window():\n    root.destroy()\n    print( \"Window closed\")\n\ndef screen_display():\n    rs = sensor_read()\n    ppm_gasarray= gas_write()\n    volts= v_read_sensor(rs,Vc)\n    \n    raw_CO = ppm_gasarray[0]\n    raw_Toulene = ppm_gasarray[1]\n    raw_Ammonia = ppm_gasarray[2]\n    raw_Methane = ppm_gasarray[3]\n    raw_Ethanol = ppm_gasarray[4]\n    \n    print (\"CO: \", raw_CO, \"Toulene: \", raw_Toulene, \"Ammonia: \", raw_Ammonia, \"Methane: \", raw_Methane, \"Ethanol: \", raw_Ethanol)\n\n    GL_CO = Label(root, text= raw_CO, fg='blue', font=('calibri', 12), anchor='center')\n    GL_CO.grid(row=1, column=3)\n    GL_Toulene = Label(root, text= raw_Toulene, fg='blue', font=('calibri', 12), anchor='center')\n    GL_Toulene.grid(row=2, column=3)\n    GL_Ammonia = Label(root, text= raw_Ammonia, fg='blue', font=('calibri', 12), anchor='center')\n    GL_Ammonia.grid(row=3, column=3)\n    GL_Methane = Label(root, text= raw_Methane, fg='blue', font=('calibri', 12), anchor='center')\n    GL_Methane.grid(row=4, column=3)\n    GL_Ethanol=Label(root, text= raw_Ethanol, fg='blue', font=('calibri', 12), anchor='center')\n    GL_Ethanol.grid(row=5, column=3)\n    \n    \n    firebase_data = firebase.FirebaseApplication(\"https://gassensor-db-default-rtdb.firebaseio.com/\", None)\n\n    data = {\n        'Ammonia': raw_Ammonia,\n        'CO': raw_CO,\n        'Ethanol': raw_Ethanol,\n        'Methane': raw_Methane,\n        'Toulene': raw_Toulene,\n        'X_Timestamp': datetime.today().strftime(\"%m-%d-%Y %H:%M:%S\")\n            }\n\n    result = firebase_data.post('/gassensor-db-default-rtdb/gasdata:', data)\n    print(result)\n    get_result = firebase_data.get('/gassensor-db-default-rtdb/gasdata:', '')\n    print(get_result)\n    \n    if ppm_gasarray[0] is not None and ppm_gasarray[1] is not None and ppm_gasarray[2] is not None and ppm_gasarray[3] is not None:\n        log = open(\"/home/pi/Desktop/log.csv\",\"a\")  \n        log.write(\"V\" + \" , \" +\"{0:0.3f}\".format(volts[0]) +\" , \"+\"{0:0.3f}\".format(volts[1]) +\" , \"+\"{0:0.3f}\".format(volts[2]) +\" , \"+\"{0:0.3f}\".format(volts[3]) +\" , \" +\"RS\" + \" , \" +\"{0:0.3f}\".format(rs[0]) +\" , \"+\"{0:0.3f}\".format(rs[1]) +\" , \"+\"{0:0.3f}\".format(rs[2]) +\" , \"+\"{0:0.3f}\".format(rs[3]) +\" , \" + \"PPM\" +\" , \" + \"{0:0.3f}\".format(ppm_gasarray[0]) +\" , \" + \"{0:0.3f}\".format(ppm_gasarray[1]) + \" , \"+ \"{0:0.3f}\".format(ppm_gasarray[2])+ \" , \"+ \"{0:0.3f}\".format(ppm_gasarray[3])+ \" , \"+ \"{0:0.3f}\".format(ppm_gasarray[4])+ \" , \")\n            \n    else:\n        log =open(\"/home/pi/Desktop/log.csv\",\"a\") \n        log.write(\"NAN   \"+ \",\")\n            \n    log.write(datetime.today().strftime(\"%m-%d-%Y %H:%M:%S\")+ \"\\n\")\n    log.close()\n    sleep(0.1)\n    \n    GL_CO.after(100, screen_display)\n    \nroot = Tk()\nroot.title(\"Gas Sensor GUI\")\nroot.geometry(\"480x320\")\n\nwindowTitle = Label(root, text=\"IED E-Nose System\", fg='red', font=('calibri', 30), anchor =\"center\").grid(row=0, column=2, columnspan=2)\n\n\nGASLEVEL = Label(root, text=\"GAS LEVEL: \", fg=\"green\", font=('calibri', 20))\nGASLEVEL.grid(row=7, column=2)\n\nSTATUS = Label(root, text=\"SAFE\", fg=\"green\", font=('calibri', 20))\nSTATUS.grid(row=7, column=3)\n#GAS TYPE\n\nCO = Label(root, text=\"Carbon Monoxide (CO)\", fg='blue', font=('calibri', 12)).grid(row=1, column=2)\nToulene = Label(root, text=\"Toulene\", fg='blue', font=('calibri', 12)).grid(row=2, column=2)\nAmmonia = Label(root, text=\"Ammonia\", fg='blue', font=('calibri', 12)).grid(row=3, column=2)\nMethane = Label(root, text=\"Methane\", fg='blue', font=('calibri', 12)).grid(row=4, column=2)\nEthanol=Label(root, text=\"Ethanol\", fg='blue', font=('calibri', 12)).grid(row=5, column=2)\n\n\nGL_CO = Label(root, text= 0, fg='blue', font=('calibri', 12))\nGL_CO.grid(row=1, column=3)\nGL_Toulene = Label(root, text= 0, fg='blue', font=('calibri', 12)).grid(row=2, column=3)\nGL_Ammonia = Label(root, text= 0, fg='blue', font=('calibri', 12)).grid(row=3, column=3)\nGL_Methane = Label(root, text= 0, fg='blue', font=('calibri', 12)).grid(row=4, column=3)\nGL_Ethanol=Label(root, text= 0, fg='blue', font=('calibri', 12)).grid(row=5, column=3)\nGL_CO.after(500, screen_display)\n    \n#for clean up exit\nroot.protocol(\"WM_DELETE_WINDOW\", close_window)\nroot.mainloop()    \n\n   \n   ","sub_path":"rpi_desktop/gas_sensorv4_Test.py","file_name":"gas_sensorv4_Test.py","file_ext":"py","file_size_in_byte":8030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"515326970","text":"from django.conf.urls import include, url\n\nfrom eforum.didatica import views\n\nurlpatterns = [\n\turl(r'^cadastroPost/(?P<pk>\\d+)/$', views.cadastroPost, name='cadastroPost'),\n\turl(r'^listarPost/(?P<pk>\\d+)/$', views.listarPost, name='listarPost'),\n\turl(r'^exibirPost/(?P<pk>\\d+)/$', views.exibirPost, name='exibirPost'),\n\turl(r'^removerPost/(?P<pk>\\d+)/$', views.removerPost, name='removerPost'),\n\turl(r'^editarPost/(?P<pk>\\d+)/$', views.editarPost, name='editarPost'),\n\turl(r'^removerComent/(?P<pk>\\d+)/$', views.removerComent, name='removerComent'),\n\turl(r'^editarComent/(?P<pk>\\d+)/$', views.editarComent, name='editarComent'),\n\turl(r'^cadastroAula/(?P<pk>\\d+)/$', views.cadastroAula, name='cadastroAula'),\n\turl(r'^listarAula/(?P<pk>\\d+)/$', views.listarAula, name='listarAula'),\n\turl(r'^exibirAula/(?P<pk>\\d+)/$', views.exibirAula, name='exibirAula'),\n\turl(r'^removerAula/(?P<pk>\\d+)/$', views.removerAula, name='removerAula'),\n\turl(r'^editarAula/(?P<pk>\\d+)/$', views.editarAula, name='editarAula'),\n\turl(r'^removerMaterial/(?P<pk>\\d+)/$', views.removerMaterial, name='removerMaterial'),\n\turl(r'^cadastroMaterial/(?P<pk>\\d+)/$', views.cadastroMaterial, name='cadastroMaterial'),\n\turl(r'^cadastroMaterialVideo/(?P<pk>\\d+)/$', views.cadastroMaterialVideo, name='cadastroMaterialVideo'),\n\turl(r'^exibirVideo/(?P<pk>\\d+)/$', views.exibirVideo, name='exibirVideo'),\n]","sub_path":"eforum/didatica/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"110055548","text":"import cv2\nimport numpy as np\nfrom color_picker import ColorPicker\n\ndef nothing(x):\n  pass\n\ndef pick_color(img):\n  \"\"\" Creamos el TrackBar y elegimos el color\n      img debe ser HSV!\n  \"\"\"\n  cv2.namedWindow(\"hsv_picker\")\n  # defino lower\n  cv2.createTrackbar(\"Low H\", \"hsv_picker\", 0, 255, nothing)\n  cv2.createTrackbar(\"Low S\", \"hsv_picker\", 0, 255, nothing)\n  cv2.createTrackbar(\"Low V\", \"hsv_picker\", 0, 255, nothing)\n  # defino upper\n  cv2.createTrackbar(\"Up H\", \"hsv_picker\", 255, 255, nothing)\n  cv2.createTrackbar(\"Up S\", \"hsv_picker\", 255, 255, nothing)\n  cv2.createTrackbar(\"Up V\", \"hsv_picker\", 255, 255, nothing)\n\n  low_h = cv2.getTrackbarPos(\"Low H\", \"hsv_picker\")\n  low_s = cv2.getTrackbarPos(\"Low S\", \"hsv_picker\")\n  low_v = cv2.getTrackbarPos(\"Low V\", \"hsv_picker\")\n\n  up_h = cv2.getTrackbarPos(\"Up H\", \"hsv_picker\")\n  up_s = cv2.getTrackbarPos(\"Up S\", \"hsv_picker\")\n  up_v = cv2.getTrackbarPos(\"Up V\", \"hsv_picker\")\n\n  lower_color = np.array([low_h, low_s, low_v])\n  upper_color = np.array([up_h, up_s, up_v])\n  \n  return lower_color, upper_color\n\ndef rescale_frame(frame, percent=75):\n    scale_percent = percent / 100\n    width = int(frame.shape[1] * scale_percent )\n    height = int(frame.shape[0] * scale_percent )\n    dim = (width, height)\n    return cv2.resize(frame, dim, interpolation=cv2.INTER_AREA)\n\ndef select_roi(img):\n    r = cv2.selectROI(\"Image\", img, False, False)\n    # return x, y, w, h = roi\n    return r\n\ndef select_mask(frame):\n    \"\"\" Elegimos el color y devolvemos con la mask \"\"\"\n    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n    #color_picker = ColorPicker()\n    #lower_color, upper_color = color_picker.pick_color(hsv)\n    lower_color = (105,10,100)\n    upper_color = (115,40,255)\n    \n    mask = cv2.inRange(hsv, lower_color, upper_color)\n\n    return mask\n\ndef get_silver_mask(frame):\n  \"\"\" Devolvemos la silver mask \"\"\"\n  hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n  mask1 = cv2.inRange(hsv, (150,3,210), (170,40,255))\n  mask2 = cv2.inRange(hsv, (110,0,100), (130,40,200))\n  mask3 = cv2.inRange(hsv, (8,12,140), (28,26,255))\n  #mask4 = cv2.inRange(hsv, (10,5,95), (30,40,255))\n  mask = cv2.inRange(hsv, (105,0,80), (115,50,255))\n  #mask = mask1+mask2+mask3\n  \n  return mask\n\ndef get_range(corte):\n  \"\"\" Retorna los limites de la mask \"\"\"\n  h,s,v = cv2.split(corte)\n  h_med = np.median(h)\n  s_max = s.max()\n  s_min = s.min()\n  v_max = v.max()\n  v_min = v.min()\n  lower_color = (h_med-10, s_min, v_min)\n  upper_color = (h_med+10, s_max, v_max)\n  \n  return lower_color, upper_color\n\n# Leer el archivo\n#cap = cv2.VideoCapture('./Videos/Videos/10-22-2019 16-09 con luz led de punta de linea.avi')\ncap = cv2.VideoCapture('./Videos/Videos/2019-11-01 12-30 rejilla1.avi')\n\n# Check if camera opened successfully\nif (cap.isOpened()== False): \n  print(\"Error opening video stream or file\")\n\ncv2.namedWindow(\"hsv_picker\")\n# defino lower\ncv2.createTrackbar(\"Low H\", \"hsv_picker\", 0, 255, nothing)\ncv2.createTrackbar(\"Low S\", \"hsv_picker\", 0, 255, nothing)\ncv2.createTrackbar(\"Low V\", \"hsv_picker\", 0, 255, nothing)\n# defino upper\ncv2.createTrackbar(\"Up H\", \"hsv_picker\", 255, 255, nothing)\ncv2.createTrackbar(\"Up S\", \"hsv_picker\", 255, 255, nothing)\ncv2.createTrackbar(\"Up V\", \"hsv_picker\", 255, 255, nothing)\n\nband = False\n\n# Read until video is completed\nwhile(cap.isOpened()):\n  # Capture frame-by-frame\n  ret, frame = cap.read()\n  if ret == True:\n    \n    k = cv2.waitKey(1)\n\n    # Resize for seeing\n    frame = rescale_frame(frame, percent=40)\n\n    # Seleccionamos la ROI presionando \"r\" en el teclado\n    if  k == ord('r'):\n        # obtengo la roi\n        x, y, w, h = cv2.selectROI(\"Cam\", frame, False, False)\n        print(\"ROI: \")\n        print(x, y, w, h)\n        band = True\n\n    if band:\n      corte = frame[y:y+h, x:x+w]\n      hsv = cv2.cvtColor(corte, cv2.COLOR_BGR2HSV)\n\n      low_h = cv2.getTrackbarPos(\"Low H\", \"hsv_picker\")\n      low_s = cv2.getTrackbarPos(\"Low S\", \"hsv_picker\")\n      low_v = cv2.getTrackbarPos(\"Low V\", \"hsv_picker\")\n\n      up_h = cv2.getTrackbarPos(\"Up H\", \"hsv_picker\")\n      up_s = cv2.getTrackbarPos(\"Up S\", \"hsv_picker\")\n      up_v = cv2.getTrackbarPos(\"Up V\", \"hsv_picker\")\n\n      lower_color = np.array([low_h, low_s, low_v])\n      upper_color = np.array([up_h, up_s, up_v])\n\n      mask = cv2.inRange(hsv, lower_color, upper_color)\n      #res = cv2.bitwise_and(img, img, mask=mask)\n\n      cv2.imshow(\"Mask\", mask)\n      #cv2.imshow(\"Result\", res)\n            \n      #band = False\n    \n\n    cv2.imshow(\"Cam\", frame)\n    \n    # Press Q on keyboard to  exit\n    if k == ord('q'):\n      break\n \n  # Break the loop\n  else: \n    cap = cv2.VideoCapture('./Videos/Videos/2019-11-01 12-30 rejilla1.avi')\n \n# When everything done, release the video capture object\ncap.release()\n \n# Closes all the frames\ncv2.destroyAllWindows()","sub_path":"pruebas/mask_rejilla.py","file_name":"mask_rejilla.py","file_ext":"py","file_size_in_byte":4798,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"315930766","text":"import heapq\nimport sys\nreadline = sys.stdin.readline\n\nN = int(readline())\nq=[]\n\nfor _ in range(N):\n    a = int(readline())\n\n    if a == 0:\n        if len(q)==0:\n            print(0)\n        else:\n            print(heapq.heappop(q))\n    else:\n        heapq.heappush(q, a)","sub_path":"boj/stepbystep/heap/p1927.py","file_name":"p1927.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"138360571","text":"#!/usr/bin/env python3\n# _*_ coding:utf-8 _*_\n# @Date    : 2018-07-15 14:55:29\n# @Author  : Pagliacii (huangmianrui0310@outlook.com)\n# @Link    : https://github.com/Pagliacii\n# @Version : 1.0.0\n\nimport sys\n\nfrom PyQt5.Qt import QColor\nfrom PyQt5.QtCore import pyqtSignal, Qt\nfrom PyQt5.QtWidgets import (\n    QApplication, QColorDialog, QMainWindow, QPushButton, QWidget\n)\n\n\nclass QColorButton(QPushButton):\n    '''\n    Custom Qt Widget to show a chosen color.\n\n    Left-clicking the button shows the color-chooser, while\n    right-clicking resets the color to None (no-color).\n\n    refer: https://martinfitzpatrick.name/article/qcolorbutton-a-color-selector-tool-for-pyqt/  # noqa\n    '''\n\n    colorChanged = pyqtSignal()\n\n    def __init__(self, width, height, name, *args, **kwargs):\n        super(QColorButton, self).__init__(*args, **kwargs)\n\n        self._color = None\n        self.setMaximumWidth(width)\n        self.setMaximumHeight(height)\n        self.pressed.connect(self.onColorPicker)\n\n    def setColor(self, color):\n        if color != self._color:\n            self._color = color\n            self.colorChanged.emit()\n\n        if self._color:\n            self.setStyleSheet(\n                \"\"\"\n                QColorButton {\n                    background-color: %s;\n                    border: 1px solid rgb(112, 214, 210);\n                    border-radius: 0px;\n                    color: rgb(0, 0, 0);\n                }\n                \"\"\" % self._color\n            )\n\n    def color(self):\n        return self._color\n\n    def onColorPicker(self):\n        '''\n        Show color-picker dialog to select color.\n\n        Qt will use the native dialog by default.\n\n        '''\n        dlg = QColorDialog(self)\n        dlg.setObjectName(\"colorBtnPicker\")\n        dlg.setStyleSheet(\n            \"\"\"\n            background-color: rgb(255, 255, 255);\n            border: 1px solid rgb(160, 160, 160);\n            border-radius: 0px;\n            color: rgb(0, 0, 0);\n            \"\"\"\n        )\n\n        buttons = dlg.findChildren(\n            QPushButton, options=Qt.FindChildrenRecursively\n        )\n        for btn in buttons:\n            if btn.text() in (\"OK\", \"Cancel\", ):\n                btn.setStyleSheet(\n                    \"\"\"\n                    QPushButton {\n                        background-color: white;\n                        border: 1px solid rgb(160, 160, 160);\n                        border-radius: 4px;\n                        color: black;\n                        min-width: 78;\n                        min-height: 20;\n                    }\n                    QPushButton:hover {\n                        background-color: rgb(25, 130, 250);\n                        border: 1px solid rgb(106, 178, 248);\n                        color: white;\n                    }\n                    \"\"\"\n                )\n\n        if self._color:\n            dlg.setCurrentColor(QColor(self._color))\n\n        if dlg.exec_():\n            self.setColor(dlg.currentColor().name())\n\n    def mousePressEvent(self, e):\n        if e.button() == Qt.RightButton:\n            self.setColor(None)\n\n        return super(QColorButton, self).mousePressEvent(e)\n\n\nclass TestWindow(QMainWindow):\n\n    def __init__(self, *args):\n        super(TestWindow, self).__init__(*args)\n        self.init_ui()\n\n    def init_ui(self):\n        self.centerWidget = QWidget(self)\n        self.setCentralWidget(self.centerWidget)\n        self.colorBtn = QColorButton(self.centerWidget)\n        self.colorBtn.resize(32, 32)\n        self.show()\n\n\ndef main(*args):\n    app = QApplication(*args)\n    window = TestWindow()\n    sys.exit(app.exec_())\n\n\nif __name__ == '__main__':\n    main(sys.argv)\n","sub_path":"ui/ColorButton.py","file_name":"ColorButton.py","file_ext":"py","file_size_in_byte":3676,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"80159261","text":"from typing import List, Union, Optional\n\nimport numpy as np\n\nfrom utilities.numpyutils import is_numpy_vector\nfrom utilities.typingutils import is_typed_list\n\n\ndef strcmp(lst: List[str], s: str) -> List[bool]:\n    assert is_typed_list(lst, str)\n    assert isinstance(s, str)\n\n    return [x == s for x in lst]\n\n\ndef find(b: Union[List[bool], np.ndarray], n: Optional[int] = None) -> List[int]:\n    assert is_typed_list(b, bool) or is_numpy_vector(b)\n    assert isinstance(n, int) or n is None\n\n    if n is None:\n        # Quick and easy\n        return [i for i, bi in enumerate(b) if bi]\n\n    # n is not None\n    idxs: List[int] = []\n    count: int = 0\n    for i, bi in enumerate(b):\n        if bi:\n            idxs.append(i)\n            count += 1\n            if count == n:\n                break\n\n    return idxs\n\n\ndef find_unique(b: List[bool]) -> int:\n    idx: List[int] = find(b)\n\n    if len(idx) == 1:\n        return idx[0]\n    else:\n        raise ValueError(\"Cannot find unique (sum(b)=\" + str(sum(b)) + \")\")\n\n\ndef integer2path(n: int, max_files: int, files_ord: int = 3, folders_ord: int = 2) -> str:\n    # Max digits\n    k = len(str(max_files - 1))\n\n    s = str(n).zfill(k)\n\n    if len(s) <= files_ord:\n        return s\n\n    file_part = s[-files_ord:]\n    folders_part = s[:-files_ord]\n\n    p = len(folders_part) - files_ord\n    while p > 0:\n        folders_part = folders_part[:p] + '/' + folders_part[:p]\n        p = p - folders_ord\n\n    return folders_part + '/' + file_part\n\n\ndef labels2intervals(labels: np.ndarray) -> (np.ndarray, np.ndarray):\n    \"\"\"\n    Convert a sequence of labels to a matrix of intervals.\n\n    :param labels: The labels sequence\n    :return: A two-column (start-stop index) matrix of the intervals\n    \"\"\"\n    assert is_numpy_vector(labels)\n\n    unset: int = -1\n    n: int = labels.size\n\n    # Initialize states\n    intervals = [[0, unset]]\n    intervals_labels = [labels[0]]\n\n    # Loop labels\n    for i in range(1, n):\n        if labels[i] == labels[i - 1]:\n            # Same label, continue\n            continue\n        intervals[-1][1] = i - 1\n        intervals.append([i, unset])\n        intervals_labels.append(labels[i])\n\n    intervals[-1][1] = n\n\n    return np.array(intervals), np.array(intervals_labels)\n","sub_path":"src/utilities/matlabutils.py","file_name":"matlabutils.py","file_ext":"py","file_size_in_byte":2252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"447671488","text":"from json import JSONDecodeError\nfrom flask import request, Response\nfrom flask_restful import Resource, reqparse\nfrom model.device_model import DeviceModel\n\nclass DevicesResource(Resource):\n\n    def __init__(self, **kwargs):\n        self.dataManager = kwargs['data_manager']\n\n    def get(self):\n        device_list = []\n        for device in self.dataManager.device_dictionary.values():\n            device_list.append(device.__dict__)\n        return device_list, 200\n\n    def post(self):\n        try:\n            json_data = request.get_json(force=True)\n            deviceModel = DeviceModel(**json_data)\n            if deviceModel.uuid in self.dataManager.device_dictionary:\n                return {'error': \"Device UUID already exists\"}, 409\n            else:\n                self.dataManager.add_device(deviceModel)\n                return Response(status=201, headers={\n                    \"Location\": request.url + \"/\" + deviceModel.uuid})\n        except JSONDecodeError:\n            return {'error': \"Invalid JSON ! Check the request\"}, 400\n        except Exception as e:\n            return {'error': \"Generic Internal Server Error ! Reason: \" + str(e)}, 500\n","sub_path":"resources/devices_resource.py","file_name":"devices_resource.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"160859097","text":"#!/usr/bin/python\nimport sys\nimport json\nimport requests\n\nwelcomeMessage = \"Welkom bij de bootcamp toelatings opdracht. Werk alsjeblieft zorgvuldig en netjes. Succes!!\"\n\ndef printWelcomeText():\n    welcome = welcomeMessage\n    return welcome\n\ndef printExercise():\n    response = requests.get('http://flask-motivation-ws:5000/motivation')\n    motivation = response.json()['message']\n    return motivation\n\nif len(welcomeMessage) != 91:\n    sys.exit(\"Nice try, but not quite right :)\")\n\nprint(printWelcomeText())\nprint(printExercise())\n","sub_path":"exercises/exerciseOne.py","file_name":"exerciseOne.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"531048592","text":"#基于数组实现循环队列\nclass CirculQueue(object):\n\n    def __init__(self,capacity):\n        self.items=[None]*capacity\n        self.n=capacity\n        self.head=0\n        self.tail=0\n\n    #入队\n    def enqueue(self,val):\n        #队列已满\n        if (self.tail+1)%self.n==self.head:\n            return False\n        self.items[self.tail]=val\n        self.tail=(self.tail+1)%self.n\n        return True\n    #出队\n    def dequeue(self):\n        #队列为空\n        if self.tail==self.head:\n            return  False\n        ret=self.items[self.head]\n        self.head=(self.head+1)%self.n\n        return ret\n\n\nq=CirculQueue(8)\nq.enqueue(5)\nq.enqueue(7)\nprint(q.dequeue())","sub_path":"learn_notes/leetcode/circul_queue.py","file_name":"circul_queue.py","file_ext":"py","file_size_in_byte":687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"553578242","text":"import torch\nimport os\nfrom network.resnet import *\nfrom utils.data_setup import *\nimport numpy as np\nfrom torch.utils.data import *\nimport time\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\nimport copy\nfrom utils.utils import *\nfrom torch.backends import cudnn\nfrom utils.MyDataSet import *\n\n#from network import resnet\n\ndevice = torch.device(\"cuda:1\" if torch.cuda.is_available() else \"cpu\")\n\nmodel_r18 = net_set()\n#model_r18 = resnet.resnet18()\n#####\n#pre = torch.load('./model/Res18_PRE_15_10step300.ckpt')\n#pre = torch.load('./model/RADB_resnet18_PRE_50_23step.ckpt')\n#model_r18.load_state_dict(pre)\n######\n#print(model_r18)\n\n#params = torch.load('./test/model/lfwa_no_load1_res18.pth')\n#model_r18.load_state_dict(params)\n\ntransform = transforms.Compose([\n        transforms.RandomAffine(15),\n        #transforms.RandomVerticalFlip(p=0.5),\n        transforms.RandomHorizontalFlip(p=0.5),\n        transforms.RandomRotation(10),\n        #transforms.ColorJitter(brightness=0.1, contrast=0.1, saturation=0.1, hue=0.1),\n        #transforms.RandomGrayscale(),\n        \n\ttransforms.CenterCrop(256),\n\ttransforms.ToTensor(),\n        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n])\n\ntransform1 = transforms.Compose([\n    \ttransforms.CenterCrop(256),\n\ttransforms.ToTensor(),\n        transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])\n        #transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n])\n#data_dir = '/home/chenjun/Desktop/Dataset/data_no_lfw'\n#data_dir = '/home/chenjun/Desktop/Dataset/data_ff'\n#data_dir = '/home/chenjun/Desktop/DEA/data'\n\n#data_dir = '/home/chenjun/Desktop/Dataset/fdea'\n#data_dir = '/home/chenjun/Desktop/Dataset/DEA'\n\n#data_dir = '/home/chenjun/Desktop/Dataset/FairFace'\n#data_dir = '/home/chenjun/Desktop/Dataset/UTKFace'\ndata_dir = '/home/chenjun/Desktop/Dataset/lfwa'\nprint('dir = ',data_dir)\n\n#data_dir = '/home/liuting/DAF/dataset/'\n#image_datasets = {x: get_Folder(os.path.join(data_dir, x),\n#\t\t\t\ttransform)\n#\t\t\t\t\tfor x in ['train', 'val']}\n\n#image_datasets = {'train': get_Folder(os.path.join(data_dir, 'train'), transform), \n#        'val': get_Folder(os.path.join(data_dir, 'val'), transform1)}\n#image_datasets = get_Folder((data_dir), transform)\nimage_datasets = {'train': mydataset(os.path.join(data_dir, 'train'), transform), \n        'val': mydataset(os.path.join(data_dir, 'val'), transform1)}\nbatchSize =  32\ndataloaders = {x: DataLoader(image_datasets[x], batch_size=batchSize,\n\t\t\t\t\t\tshuffle=True, \n\t\t\t\t\t\tnum_workers=4)\n\t\t\t\t\tfor x in ['train', 'val']}\n\nprint(\"batch size: \", batchSize)\n#class_names = image_datasets['train'].classes\n#print(class_names)\n\n#class_names = image_datasets['val'].classes\n#print(class_names)\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=30):\n\tsince = time.time()\n\tprint(\"Epochs: \", num_epochs)\n\tbest_model_wts = copy.deepcopy(model.state_dict())\n\tbest_acc = 0.0\n\tlabels = []\n\tclasses = ('African', 'Caucasian', 'Asian')\n\n\tfor epoch in range(num_epochs):\n\t\tprint('Epoch {}/{}'.format(epoch, num_epochs - 1))\n\t\tprint('-' * 10)\n\n\t\tfor phase in ['train', 'val']:\n\t\t\tif phase == 'train':\t\t\n\t\t\t\t#scheduler.step()\n\t\t\t\tmodel.train()\n\t\t\telse:\n\t\t\t\tmodel.eval()\n\n\t\t\trunning_loss = 0.0\n\t\t\trunning_corrects = 0\n\n\t\t\tclass_num = 3\n\t\t\tclass_correct = list(0. for i in range(class_num))\n\t\t\tclass_total = list(0. for i in range(class_num))\n\n\t\t\tfor inputs, labels, names in dataloaders[phase]:\n                              \n\t\t\t\tinputs = torch.FloatTensor(inputs)\n\t\t\t\tinputs = inputs.to(device)\n\t\t\t\tlabels = labels.to(device)\n\t\t\t\t\n\t\t\t\t#print(inputs)\n\t\t\t\toptimizer.zero_grad()\n \n\t\t\t\twith torch.set_grad_enabled(phase == 'train'):\n\t\t\t\t\toutputs = model(inputs)\n\t\t\t\t\t_, preds = torch.max(outputs, 1)\n\t\t\t\t\tloss = criterion(outputs, labels)\n\t\t\t\t\tif phase == 'train':\n\t\t\t\t\t\tloss.backward()\n\t\t\t\t\t\toptimizer.step()\t\t\n\t\t\t\t\t\t#scheduler.step()\n\t\t\t\t#res = (preds == labels).squeeze()\n\t\t\t\t\t\t#scheduler.step()\n\t\t\t\tres =  preds == labels\n\t\t\t\tfor label_idx in range(len(labels)):\n\t\t\t\t\tlabel_single = labels[label_idx] \n\t\t\t\t\tclass_correct[label_single] += res[label_idx].item()\n\t\t\t\t\tclass_total[label_single] += 1\n\t\t\t\t\n\n\t\t\t\t#correct += (preds == labels).sum().float()\n\t\t\t\t#total += len(labels)\n\n\t\t\t\trunning_loss += loss.item() * inputs.size()[0]\n\t\t\t\trunning_corrects += torch.sum(preds == labels.data)\n\n\t\t\t#print(\"count:\",count)\n\t\t\t#print(\"black_corrects:\",black_corrects,\"yellow_corrects:\",yellow_corrects,\"white_corrects:\",white_corrects)\n\t\t\t#print(\"sum_cor:\",sum_cor,\"running_corrects:\",running_corrects)\n\n\n\t\t\t#acc_str = 'acc_str: %f'%(sum(correct)/sum(total))\n\n\t\t\tepoch_loss = running_loss / len(image_datasets[phase])\n\t\t\tepoch_acc = running_corrects.double() / len(image_datasets[phase])\n\t\t\t\n\t\t\t#print('acc_str:',acc_str)\n\t\t\tprint(' {} Loss: {:.4f} Acc: {:.4f}'.format(\n\t\t\t\tphase, epoch_loss, epoch_acc))\n\n\t\t\tif phase == 'val' and epoch_acc > best_acc:\n\t\t\t\tbest_acc = epoch_acc\n\t\t\t\tbest_model_wts = copy.deepcopy(model.state_dict())\n\n\t\t\tfor i in range(class_num):\n\t\t\t\tprint('Accuracy of %5s : %3f %%' % (classes[i], 100 * class_correct[i] / class_total[i]))\n\n\n\t\tscheduler.step()\n\t\t#print()\n\t\n\ttime_use = time.time() - since\n\tprint('Train complete in {:.0f}m {:.0f}s'.format( \n\t\t\ttime_use // 60, time_use % 60))\n\tprint('Best val Acc: {:4f}'.format(best_acc))\n\n\tmodel.load_state_dict(best_model_wts)\n\treturn model\n\nmodel_r18 = model_r18.to(device)\nlearn_rate = 0.01\nstep = 30\nprint(\"learning rate: \", learn_rate, \" scheduler_step: \", step)\ncriterion = nn.CrossEntropyLoss()\n\noptimizer_r18 = optim.SGD(model_r18.parameters(), lr=learn_rate, momentum=0.9)\n#optimizer_r18 = optim.Adam(model_r18)\n\nexp_lr_scheduler = lr_scheduler.StepLR(optimizer_r18, step_size=step, gamma=0.1)\n\nmodel_r18 = train_model(model_r18, criterion,optimizer_r18, exp_lr_scheduler, \n\t\t\t\t\tnum_epochs=30)\n\n\n\n\n#visualize_model(model=model_r18, dataloader=dataloaders['train'], classes = class_names,\n#                        device=device,\n#                        path='./utils/showpic/Rest_pic.jpg')\n#visualize_model(model=model_r18, dataloader=dataloaders['val'], classes = class_names,\n#                        device=device, \n#                        path='./utils/showpic/Resv_pic.jpg')\n\n\n##\n#torch.save(model_r18.state_dict(), './test/model/fdea_res18_three.pth')\n\n\t\t\t\n\t\t\t\t\n","sub_path":"train_three_acc.py","file_name":"train_three_acc.py","file_ext":"py","file_size_in_byte":6354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"616784389","text":"#!/usr/bin/env python\n\nimport contextlib\nimport glob\nimport os\nimport subprocess\nimport sys\n\n\nSOURCE_ROOT = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))\n\n\ndef main():\n  natives = os.path.abspath(sys.argv[1])\n  js_source_files = glob.glob('{0}/*.js'.format(sys.argv[2]))\n\n  call_js2c(natives, js_source_files)\n\n\ndef call_js2c(natives, js_source_files):\n  js2c = os.path.join(SOURCE_ROOT, 'vendor', 'node', 'tools', 'js2c.py')\n  src_dir = os.path.dirname(js_source_files[0])\n  with scoped_cwd(src_dir):\n    subprocess.check_call(\n        [sys.executable, js2c, natives] +\n        [os.path.basename(source) for source in js_source_files])\n\n\n@contextlib.contextmanager\ndef scoped_cwd(path):\n  cwd = os.getcwd()\n  os.chdir(path)\n  try:\n    yield\n  finally:\n    os.chdir(cwd)\n\n\nif __name__ == '__main__':\n  sys.exit(main())\n","sub_path":"tools/js2c.py","file_name":"js2c.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"217208788","text":"'''\n@author: Alexander Bendeck\n\n'''\n\nimport pandas as pd\nimport numpy as np\nimport os\n\ndef formatDate(date):\n    '''\n    Re-formats dates from daily activity\n    file in format M/D/YYYY to be the same as\n    YYYY/MM/DD to match surveys and SMS data\n    '''\n    m, day, yr = date.split(\"/\")\n    if len(m) < 2:\n        m = '0'+m\n    if len(day) < 2:\n        day = '0'+day\n    return \"-\".join([yr, m, day])\n\ndef dateToUnix(date):\n    '''\n    Converts date to Unix timestamp\n    '''\n    if date == \"NA\":\n        return date\n    else:\n        return (pd.to_datetime([date]).astype(int) / 10**9)[0].astype(int)\n\ndef mergeFilesForUser(uid, write_csv=False):\n    '''\n    Merge the following: \n       * daily activity FitBit data (one file per subject)\n       * daily survey data (one combined file)\n       * fMRI data (one file per run, per subject)\n       * SMS data (one file per subject)\n    \n    All input data should be in a directory named:\n        data_raw\n    which is in the same directory as this script.\n    \n    All ouput data will be placed in a directory named:\n        data_clean\n    which should be created beforehand in the same \n    directory as this script.\n    '''\n    path_to_data = os.path.join(\"data_raw\", \"\")\n    \n    # Find activity file for user, load dataframe, and re-format date\n    activityFile = [f for f in os.listdir(path_to_data) if f.startswith(uid)][0]\n    userActivity = pd.read_csv(path_to_data + activityFile)\n    userActivity['ActivityDate'] = userActivity['ActivityDate'].apply(formatDate)\n\n    # Find SMS data file, load dataframe, clean up subject day numbers,\n    # and throw out survey timestamp (keep date)\n    smsData = pd.read_csv(path_to_data + \"sub-\" + uid + \"_sms-times.csv\") \n    smsData.rename(columns={'Unnamed: 0':'subj_day_num'}, inplace=True)\n    smsData['subj_day_num'] = smsData['subj_day_num'].apply(lambda x: x+1)\n    smsData['SmsDate'] = smsData['timestamp'].apply(lambda x: x.split()[0])\n    \n    # Merge survey and SMS rows on date\n    act_SMS = pd.merge(userActivity, smsData, left_on='ActivityDate', right_on='SmsDate')\n    \n    # Re-order columns of final_merged dataframe\n    cols = act_SMS.columns.tolist()\n    cols.remove('subj_day_num')\n    act_SMS = act_SMS[['subj_day_num']+cols]\n    \n    # Find combined survey data file, load dataframe, and throw out survey timestamp (keep date)\n    surveyData = pd.read_csv(path_to_data + \"DailySurveys_DATA_2019-09-11_1204.csv\") \n    surveyData['SurveyDate'] = surveyData['daily_survey_timestamp'].apply(lambda x: x.split()[0])\n    \n    # Find this user's rows in the survey dataframe and store in new dataframe\n    surveyDataForUser = surveyData.loc[surveyData['subject_id'] == int(uid)]\n\n    # Merge activity/SMS and survey rows on date, fill survey cols with NA if missing surveys\n    act_SMS_surveys = pd.merge(act_SMS, surveyDataForUser, how='left', left_on='ActivityDate', right_on='SurveyDate')\n    act_SMS_surveys = act_SMS_surveys.fillna(\"NA\")\n    \n    # Create combined message ID column (currenly not used in output)\n    valence = act_SMS_surveys['valence']\n    valence_short = valence.apply(lambda x: x[0:3])\n    s_ns = act_SMS_surveys['s_ns']\n    s_ns_short = s_ns.apply(lambda x: x[0:3] if x[0] == 's' else x[0:6])\n    msg_num = act_SMS_surveys['id'].astype(str)\n    act_SMS_surveys['msg_id'] = valence_short + \"_\" + s_ns_short + \"_\" + msg_num\n    \n    # Read in the subject's two fMRI runs as dataframes and label rows with run number\n    run1 = pd.read_csv(path_to_data + \"sub-\" + uid + \"_task-HealthMessage_run-01_events.tsv\", sep='\\t')\n    run1['run'] = '01'\n    run2 = pd.read_csv(path_to_data + \"sub-\" + uid + \"_task-HealthMessage_run-02_events.tsv\", sep='\\t')\n    run2['run'] = '02'\n    \n    # Concatenate dataframes for different runs\n    runs = pd.concat([run1, run2])\n    \n    # Create final merged dataframe\n    final_merged = pd.merge(act_SMS_surveys, runs, how='inner', left_on=['msg_id'], right_on=['id'])\n    \n    # Normalize TotalSteps and RestingHeartRate\n    totSteps = final_merged['TotalSteps'].apply(lambda x: int(x) if x != \"NA\" else np.NaN)\n    restingHR = final_merged['RestingHeartRate'].apply(lambda x: int(x) if x != \"NA\" else np.NaN)\n    final_merged['TotalSteps_norm'] = (final_merged['TotalSteps'] - totSteps.mean()) / totSteps.std()\n    final_merged['RestingHeartRate_norm'] = (restingHR - restingHR.mean()) / restingHR.std()\n    \n    # Re-format column names and null values\n    final_merged.rename(columns={'msg_id':'msg_id_combined'}, inplace=True)\n    final_merged.rename(columns={'unix_timestamp':'sms_timestamp',\n                            'valence_x':'valence',\n                            's_ns_x':'s_ns',\n                            'id_x':'msg_id',\n                            'daily_survey_timestamp':'survey_complete_timestamp'}, inplace=True)\n    final_merged = final_merged.fillna('NA')\n    finalCols = ['sub', 'run', 'onset', 'duration', 'trial', 'trial_type', 'rating', 'resp_time',\n                 'subj_day_num', 'sms_timestamp',  'TotalSteps', 'TotalSteps_norm', 'TotalDistance', 'TrackerDistance', 'LoggedActivitiesDistance',\n                 'VeryActiveDistance', 'ModeratelyActiveDistance', 'LightActiveDistance', 'SedentaryActiveDistance',\n                 'VeryActiveMinutes', 'FairlyActiveMinutes', 'LightlyActiveMinutes', 'SedentaryMinutes',\n                 'Calories', 'Floors', 'CaloriesBMR', 'MarginalCalories', 'RestingHeartRate', 'RestingHeartRate_norm',\n                 'valence', 's_ns', 'msg_id', 'message',\n                 'survey_complete_timestamp', 'location', 'lap', 'hap', 'han', 'lan', 'la', 'p', 'n', 'ha', 'self_efficacy_daily']\n    final_merged = final_merged[finalCols]\n    final_merged['survey_complete_timestamp'] = final_merged['survey_complete_timestamp'].apply(dateToUnix)\n    \n    # Write CSV files (one per fMRI run) for this subject, if desired\n    if write_csv:\n        run01 = final_merged.loc[final_merged['run'] == '01'].sort_values(by=['trial', 'onset'])\n        fname01 = \"sub-\" + uid + \"_task-HealthMessage_run-01_events_all_vars\"\n        pd.DataFrame.to_csv(run01, os.path.join(\"data_clean\" , fname01) + \".csv\", index=False)\n        run02 = final_merged.loc[final_merged['run'] == '02'].sort_values(by=['trial', 'onset'])\n        fname02 = \"sub-\" + uid + \"_task-HealthMessage_run-02_events_all_vars\"\n        pd.DataFrame.to_csv(run02, os.path.join(\"data_clean\" , fname02) + \".csv\", index=False)\n    \n    # Return the final merged dataframe\n    return final_merged\n\ndef getCombinedFile(uids):\n    '''\n    Create one ouput file with\n    all runs of all subjects\n    '''\n    dataframes = []\n    for uid in uids:\n        dataframes.append(mergeFilesForUser(uid, write_csv = False))\n    allInOne = pd.concat(dataframes)\n    pd.DataFrame.to_csv(allInOne, os.path.join(\"data_clean\" ,\"final_merged_data_all_norm.csv\"), index=False)\n\ndef getSeparateFiles(uids):\n    '''\n    Create two ouput files per subject,\n    one for each fMRI run\n    '''\n    for uid in uids:\n        mergeFilesForUser(uid, write_csv = True)\n\nif __name__ == \"__main__\":\n    path_to_data = os.path.join(\"data_raw\", \"\")\n    \n    # Only run for subjects where we have both SMS and fMRI data\n    uids_sms = [f[4:8] for f in os.listdir(path_to_data) if 'sms-times' in f]\n    uids_fmri = [f[4:8] for f in os.listdir(path_to_data) if 'HealthMessage' in f]\n    uids = sorted(set(uids_sms) & set(uids_fmri), key = lambda x: int(x))\n    uids = ['1071']\n    # Output separate files for these subjects\n    getSeparateFiles(uids)\n","sub_path":"data-cleaning/mergeData.py","file_name":"mergeData.py","file_ext":"py","file_size_in_byte":7494,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"104212033","text":"# -*- coding: utf-8 -*-\n\nimport unittest\nfrom iktomi.forms import *\nfrom iktomi.forms.media import FormJSRef, FormCSSRef, FormMedia\nfrom iktomi.forms.widgets import Widget\n\n\nclass MediaTests(unittest.TestCase):\n\n    def test_form_media_empty(self):\n        'Simple form get_media() method'\n        class F(Form):\n            fields=[Field('name')]\n        form = F()\n        self.assertEqual(form.get_media(), FormMedia())\n\n    def test_form_media_simple(self):\n        'Simple form with multiple fields get_media() method'\n        class F(Form):\n            fields=[Field('name'),\n                    Field('name1')]\n        form = F()\n        self.assertEqual(form.get_media(), FormMedia())\n\n    def test_form_media_one(self):\n        'Custom media in one of field'\n        class F(Form):\n            fields=[Field('name',\n                          widget=Widget(media=FormCSSRef('field.css'))),\n                    Field('name1')]\n        form = F()\n        self.assertEqual(form.get_media(), \n                         FormMedia(FormCSSRef('field.css')))\n\n    def test_form_media_same(self):\n        'Same media in both fields'\n        class F(Form):\n            fields=[Field('name',\n                          widget=Widget(media=FormCSSRef('field.css'))),\n                    Field('name1',\n                          widget=Widget(media=FormCSSRef('field.css')))]\n        form = F()\n        self.assertEqual(form.get_media(), \n                         FormMedia(FormCSSRef('field.css')))\n\n    def test_form_media_multi(self):\n        'Multi media atoms in field'\n        class F(Form):\n            fields=[Field('name', \n                          widget=Widget(media=[\n                              FormCSSRef('field.css'),\n                              FormJSRef('field.js')\n                          ])),\n                    Field('name1', \n                          widget=Widget(media=FormCSSRef('field.css')))]\n        form = F()\n        self.assertEqual(form.get_media(), \n                         FormMedia(items=[FormCSSRef('field.css'),\n                                          FormJSRef('field.js')]))\n\n    def test_fieldset(self):\n        'Fieldset media'\n        class F(Form):\n            fields=[\n                FieldSet('set', fields=[\n                    Field('name', widget=Widget(media=[\n                            FormCSSRef('field.css'),\n                            FormJSRef('field.js')])),\n                    Field('name1', widget=Widget(media=FormCSSRef('field.css')))\n                ], widget=widgets.FieldSetWidget(media=FormCSSRef('fieldset.css')))\n            ]\n        form = F()\n        self.assertEqual(form.get_media(), \n                         FormMedia(items=[FormCSSRef('fieldset.css'),\n                                          FormCSSRef('field.css'),\n                                          FormJSRef('field.js'),]))\n\n","sub_path":"tests/forms/media.py","file_name":"media.py","file_ext":"py","file_size_in_byte":2869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"141739507","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport autoslug.fields\nimport django.utils.timezone\nimport utils.model_fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('common', '0005_auto_20150519_1539'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Article',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', primary_key=True, auto_created=True, serialize=False)),\n                ('h1_title', utils.model_fields.StrippedCharField(max_length=255, blank=True, verbose_name='заголовок страницы <H1>')),\n                ('h2_title', utils.model_fields.StrippedCharField(max_length=255, blank=True, verbose_name='заголовок описания <H2>')),\n                ('descr', utils.model_fields.StrippedTextField(blank=True, verbose_name='описание')),\n                ('head_title', utils.model_fields.StrippedCharField(max_length=255, blank=True, verbose_name='заголовок окна браузера')),\n                ('meta_description', utils.model_fields.StrippedCharField(max_length=255, blank=True, verbose_name='[META] Описание')),\n                ('meta_keywords', utils.model_fields.StrippedCharField(max_length=255, blank=True, verbose_name='[META] Ключевые слова')),\n                ('is_active', models.BooleanField(default=True, verbose_name='активность')),\n                ('name', utils.model_fields.StrippedCharField(max_length=100, verbose_name='название')),\n                ('url_slug', autoslug.fields.AutoSlugField(max_length=100, blank=True, unique=True, help_text='Оставьте пустым, чтобы сгенерировать автоматически.', verbose_name='URL')),\n                ('date', models.DateField(default=django.utils.timezone.now, verbose_name='дата')),\n                ('short_descr', utils.model_fields.StrippedTextField(verbose_name='краткое описание')),\n                ('processed_text', utils.model_fields.StrippedTextField(editable=False)),\n                ('position', models.PositiveSmallIntegerField(default=0, verbose_name='cортировка', help_text='Индекс сортировки (по возрастанию).')),\n            ],\n            options={\n                'verbose_name': 'статья',\n                'verbose_name_plural': 'статьи',\n                'ordering': ('position',),\n            },\n        ),\n    ]\n","sub_path":"common/migrations/0006_article.py","file_name":"0006_article.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"49587350","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /Users/hugo/.virtualenvs/gitinspector/lib/python2.7/site-packages/gitinspector/gravatar.py\n# Compiled at: 2015-08-05 12:31:15\nfrom __future__ import unicode_literals\ntry:\n    from urllib.parse import urlencode\nexcept:\n    from urllib import urlencode\n\nimport format, hashlib\n\ndef get_url(email, size=20):\n    md5hash = hashlib.md5(email.encode(b'utf-8').lower().strip()).hexdigest()\n    base_url = b'https://www.gravatar.com/avatar/' + md5hash\n    params = None\n    if format.get_selected() == b'html':\n        params = {b'default': b'identicon', b'size': size}\n    elif format.get_selected() == b'xml':\n        params = {b'default': b'identicon'}\n    return base_url + b'?' + urlencode(params)","sub_path":"pycfiles/gitinspector-0.3.2.macosx-10.10-x86_64.tar/gravatar.py","file_name":"gravatar.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"202954387","text":"from django.urls import path\n\nfrom . import views\n\napp_name = \"Application\"\nurlpatterns = [\n    path('', views.apply, name='apply', ),\n    path('submitted/', views.submitted, name='submitted'),\n    path(\"view/\", views.view_application, name='view'),\n    path('result/', views.result, name='result')\n]\n","sub_path":"Application/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"257096706","text":"#!/usr/bin/python\n\nimport sys\n\ndef pyramid(n):\n    counter=0\n    for i in range(1,n+1):\n        if (i%2 == 1):\n           counter=counter+1\n        else:\n           counter=counter+2\n\n        print(\"* \"*counter)\n\ndef usage():\n   print(\"Usage: ./pyramid.py NUMBER_OF_LINES [e.g. ./pyramid.py 6]\\n\")\n   sys.exit()\n\n# Main - Execution Starts here \nif(len(sys.argv) < 2):\n   usage()\n\nn=int(sys.argv[1])\npyramid(n)\n","sub_path":"TASK-1/pyramid.py","file_name":"pyramid.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"206672950","text":"#yelp_api.py in flask\nfrom yelp.client import Client\nfrom yelp.oauth1_authenticator import Oauth1Authenticator\nimport os\n\nfrom dotenv import load_dotenv, find_dotenv\nload_dotenv(find_dotenv())\n\n#def get_yelp(term, location):\ndef get_businesses(address, term):\n\tauth = Oauth1Authenticator(\n    \tconsumer_key=os.environ['CONSUMER_KEY'],\n    \tconsumer_secret=os.environ['CONSUMER_SECRET'],\n    \ttoken=os.environ['TOKEN'],   \t\n    \ttoken_secret=os.environ['TOKEN_SECRET']\n\t)\n\n\tclient = Client(auth)\n\n\tparams = {\n    'term': 'food',\n    'lang': 'en',\n    'limit': 3\n\t}\n\n\tresponse = client.search(address, **params)\n\n\tbusinesses = []\n\n\tfor business in response.businesses:\n\t\tbusinesses.append({\"name\": business.name, \"rating\": business.rating, \"phone\": business.phone})\n\treturn businesses\n\n# businesses = get_businesses('New York City', 'food')\n# print(businesses)\n# get_businesses('New York City', 'food')\t\n\n","sub_path":"yelp_api.py","file_name":"yelp_api.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"450319167","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom itertools import count\nfrom collections import OrderedDict\nimport pandas as pd\nimport datetime\nimport time  #잠시 기다리는 sleep 이걸 안해주면 구문이 안먹힐때가 있다. \nfrom selenium import webdriver\nfrom selenium.common.exceptions import NoSuchElementException,StaleElementReferenceException\n\noption = webdriver.ChromeOptions() \nchrome_prefs = {} \noption.experimental_options[\"prefs\"] = chrome_prefs \nchrome_prefs[\"profile.default_content_settings\"] = { \"popups\": 1 }\n\ndef crawling(query):\n    exit_num = 1\n    url = \"https://search.naver.com/search.naver\"\n    driver = webdriver.Chrome('C:\\chromedriver.exe')    \n    article_dict = OrderedDict() #중복여부를 체크하기 위한 딕셔너리 변수\n    cnt = 1 #갯수를 세기위한 변수\n    num = 1 #갯수를 세기위한 변수\n    break_count = 1\n    break_for = False\n    break_2_for = False\n\n    number = []\n    url_list =[]\n    title = []\n    day = []\n    contents = []\n    comment =[]\n    temp2 =[]\n    cafe_name = []\n    domain =[]\n    data = pd.DataFrame(data = None,columns = ['No.','검색명','카페명','url','title','작성날짜','게시글','댓글'])\n    df = pd.DataFrame(data = None,columns = ['No.','검색명','카페명','url','title','작성날짜','게시글','댓글'])\n\n    date_from_start = datetime.datetime(2018, 6, 1) # 검색 시작날짜 초기화 \n    date_to_start = datetime.datetime(2018, 6, 7) # # 검색 종료날짜 초기화 원래는 7\n    d = datetime.timedelta(days=7) # 7일씩 더해줘서 검색할 예정\n#     for page in range(0,1):\n\n    for date in count(1,1):\n\n        date_from = date_from_start.strftime(\"%Y%m%d\")\n        date_to = date_to_start.strftime(\"%Y%m%d\")\n#         print(date_from)\n#         print(date_to)\n\n\n#         for page in range(0,1):\n\n        for page in count(1,1):\n            try:\n    #             print(page) 페이지 확인\n                param = {'where' : 'articleg',\n                         'query' : query,\n                         'date_from' :date_from,\n                         'date_to' : date_to,\n                         'date_option': '6',\n                         'start': (page -1) * 10 +1\n                         }\n\n                response = requests.get(url, params = param)\n                # time.sleep(2)\n                soup = BeautifulSoup(response.text, 'html.parser')\n    #             time.sleep(3)\n    #             time.sleep()\\n #잠시멈추지 않으면 데이터가 , \n\n                area = soup.find(\"div\",{\"class\":\"cafe_article section _cafeBase\"}).find_all(\"li\",{\"class\":\"sh_cafe_top\"}) #url\n\n\n    ##################################################################################################################################\n                for tag1 in area:\n                    area2 = tag1.find_all(\"a\",{\"class\":\"url\"}) #url\n            \n\n                    for tag in area2:\n                        url1 = tag.get('href')\n#                         print(\"{:} {:}\".format(cnt, url1))\n#                         print(response.url) #매개변수가 포함된 url 형태 출력\n\n\n                        if tag['href'] in article_dict: #article에 리스트가 있다면, 제외\n                            print(\"같은 url 주소가 있습니다. pass 하겠습니다.\")\n\n                            break_for = True\n                            break_count += 1\n                            if break_count >= 30:\n                                break_2_for = True\n                                break\n                            continue                            \n\n        ##################################################################################################################################\n                        try:                \n\n                            if break_for == False: #article에 리스트가 없다면 테그에 text를 article_dict 에 추가\n                                \n                \n                                \n                                driver.get(response.url)\n                                print(\"{:} {:}\".format(cnt, url1))\n                                xpath = '//*[@id=\"elThumbnailResultArea\"]/li['+ str(num) +']/dl/dt/a' # n번째 카페 접속하는 코드\n                                driver.find_element_by_xpath(xpath).click()\n                                driver.implicitly_wait(4)\n\n                                try:\n\n                                    driver.switch_to.window(driver.window_handles[-1])\n                                    driver.implicitly_wait(2)        \n                                    try:\n                                        iframe = driver.find_element_by_id('cafe_main')\n                                        driver.switch_to_frame(iframe)\n                                        webpage_source = driver.page_source\n                                        cafe_article = BeautifulSoup(webpage_source,\"html.parser\")\n                                        \n                                    except NoSuchElementException:\n                                        print(\"nosuch에러 발생\")\n                                        num +=1\n                                        driver.close()   \n                                        driver.switch_to.window(driver.window_handles[0])\n                                        continue\n                                    \n                                    #새로운 페이지 정보\n                                    try:\n                                        title.append(cafe_article.find('span', {\"class\":\"b m-tcol-c\"}).text)\n                                        domain.append(query)\n                                        exit_num = 1\n                                    except AttributeError:\n                                        print(\"게시글 검색 에러\")\n                                        num +=1\n                                        exit_num +=1\n                                        driver.close()   \n                                        driver.switch_to.window(driver.window_handles[0])\n                                        print(\"에러 발생, 다음으로 이동\")\n                                        if exit_num > 5:\n                                            break_2_for = True\n                                            print(\"exit_10개 이상\")\n                                            break                                              \n                                        continue\n                                        \n                                    article_dict[tag['href']] = tag.text\n                                    cnt +=1\n                                    \n                                    \n                                    if cafe_article.find('td', {\"class\":\"m-tcol-c date\"}).text:\n                                        day.append(cafe_article.find('td', {\"class\":\"m-tcol-c date\"}).text)\n                                    else:\n                                        day.append(\"\")\n                                    if cafe_article.find('div', {\"class\":\"tbody m-tcol-c\"}).text:\n                                        contents.append(cafe_article.find('div', {\"class\":\"tbody m-tcol-c\"}).text)\n                                    else:\n                                        contents.append(\"\")\n                                    \n                                    #이전 페이지 정보\n                                    number.append(str(cnt-1))\n                                    url_list.append(url1)\n                                    cafe_name.append(tag1.find(\"a\",{\"class\":\"txt84\"}).text) #url)\n                                    # print(\"{:} {:}\".format(cnt, response.url))\n\n                                    try:\n                                        temp = cafe_article.findAll('p', {\"class\":\"comm m-tcol-c\"})\n                                        for n in temp:\n                                            temp2.append(n.text)                            \n\n                                    except AttributeError:\n                                        temp2 = []\n                                        pass                                           \n                                    comment.append(temp2)\n                                    temp2 = []\n\n                                    driver.close()                    \n                                    driver.switch_to.window(driver.window_handles[0])\n                                    driver.implicitly_wait(1)\n                        \n                                    num +=1\n                                except AttributeError:\n                                    print(\"프레임 에러 발생 패스합니다.\")\n                                    driver.implicitly_wait(1)  \n                                    driver.close() \n                                    driver.switch_to.window(driver.window_handles[0])\n                                    num +=1\n                                    pass\n\n                        except AttributeError:\n                            print(\"에러 발생\")\n                            pass\n\n                        break_for = False\n                    if break_count >= 30:\n                        break_2_for = True\n                        break    \n                num = 1\n\n    ##################################################################################################################################\n        # 조건을 만족하면 종료 \n                if break_2_for == True:\n                    print(\"종료되었습니다.\")\n                    print(date_to+\"까지 완료 되었습니다.\")\n                    break\n            except AttributeError:\n                print(\"페이지 에러 발생\")\n                pass\n    # 날짜를 변경하고 다시 for문                    \n        date_from_start = date_from_start + d #7일씩 더해가는 코드\n        date_to_start = date_to_start + d #7일씩 더해가는 코드\n        exit_num = 1\n\n#         if date_to_start > datetime.datetime.today(): # 오늘날짜 이후면 for문 종료        \n\n    ##################################################################################################################################\n    # 임시저장\n        df['No.'] = number\n        df['검색명'] = domain\n        df['카페명'] = cafe_name\n        df['url'] = url_list\n        df['title'] = title\n        df['작성날짜'] = day\n        df['게시글'] = contents\n        df['댓글'] = comment        \n        df.to_csv('크롤링_'+query+'.csv',encoding ='utf-8')\n        df = pd.DataFrame(data = None,columns = ['No.','검색명','카페명','url','title','작성날짜','게시글','댓글'])\n\n        if date_to_start > datetime.datetime(2019,6,30): # 오늘날짜 이후면 for문 종료 # 마지막 날짜 설정\n\n            print('2019.06.30까지 데이터를 수집했습니다.')\n\n            break        \n    ##################################################################################################################################\n        break_count = 1\n        break_for = False\n        break_2_for = False\n\n        \n# =============================================================================================================================                \n\n    print(len(number))\n    print(len(cafe_name))\n    print(len(url_list))\n    print(len(title))\n    print(len(day))\n    print(len(contents))\n    print(len(comment))\n    \n\n    data['No.'] = number\n    data['카페명'] = cafe_name\n    data['url'] = url_list\n    data['title'] = title\n    data['작성날짜'] = day\n    data['게시글'] = contents\n    data['댓글'] = comment\n    \n    data.set_index(\"No.\", inplace = True)\n    \n    return(data)\n\n\nsearch_list = ['KT 티비','SK 인터넷','SK 티비']\n\nfor a in search_list:\n    df = crawling(a)\n    df['검색명'] = a\n    s = str(datetime.datetime.now().strftime(\"%y%m%d\"))\n    df.to_csv('크롤링_'+a+''+s+'.csv', encoding='utf-8')\n    \n\n","sub_path":"01.Crawl/01.Crawling.py","file_name":"01.Crawling.py","file_ext":"py","file_size_in_byte":12142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"530848907","text":"import argparse\nimport csv\nimport ipaddress\nimport datetime\nimport subprocess\nfrom enum import Enum\nfrom ipaddress import IPv4Address\nimport uuid\n\nfrom NetworkQueries import query_address_asn\nfrom TrafficGenerator.traceroute_utils import number_of_hops_to_hostname\n\n\nclass Action(Enum):\n    filter = 1\n    rank = 2\n\n# Firewall consts\nINCOMING_INTERFACE = 'Lan3'\nDATE_COLUMN = 1\nHOUR_COLUMN = 2\nINTERFACE_COLUMN = 3   \nDSTPORT_COLUMN = 7\nSRCPORT_COLUMN = 8\nSOURCE_COLUMN = 9 \nDEST_COLUMN = 10\n\nTTL_COLUMN = 100\n\n# TCPDUMP consts\nDUMP_PARSER = r'..\\IPAlgorithm\\tcpdump.jar'\nDUMP_PARSER_ARGS = 'ttl sip sp dip dp time'\n\nclass FirewallPacket(object):\n    def __init__(self, row):\n        self.row = row\n        self.time = datetime.datetime.strptime(row[DATE_COLUMN] + ' ' + row[HOUR_COLUMN], \"%d%b%Y %H:%M:%S\")\n        self.src = row[SOURCE_COLUMN]\n        self.dst = row[DEST_COLUMN]\n        self.src_port = row[SRCPORT_COLUMN]\n        self.dst_port = row[DSTPORT_COLUMN]\n        if len(row) >= TTL_COLUMN - 1:\n            self.ttl = row[TTL_COLUMN]\n        else:\n            self.ttl = 0\n\nclass TCPDUMPPacket(object):\n    def __init__(self, ttl, sip, sp, dip, dp, time):\n        self.src = IPv4Address(int(sip))\n        self.dst = IPv4Address(int(dip))\n        self.src_port = sp\n        self.dst_port = dp\n        self.ttl = int(ttl) % 256\n        self.time = float(time)\n        self.number = uuid.uuid1().int\n\n    def __lt__(self, other):\n        return self.time < other.time\n\n    def __le__(self, other):\n        return self.time <= other.time\n\n    def __eq__(self, other):\n        return self.time == other.time\n\n    def __ne__(self, other):\n        return self.time != other.time\n\n    def __gt__(self, other):\n        return self.time > other.time\n\n    def __ge__(self, other):\n        return self.time >= other.time\n\nclass PcapPacket(TCPDUMPPacket):\n    def __init__(self, ttl, sip, sp, dip, dp, time, number=0):\n        self.src = IPv4Address(sip)\n        self.dst = IPv4Address(dip)\n        self.src_port = sp\n        self.dst_port = dp\n        self.ttl = int(ttl)\n        self.time = float(time)\n        self.number=number\n\nSOURCES_THRESHOLD = 20\n    \nDEFAULT_OUTPUT_FILE = 'output_log.txt'\n\n\ndef filter_incoming(file_type, input_log_file, output_log_file, **kwargs):\n    input_packets, header = get_incoming_packets(file_type, input_log_file)\n\n    with open(output_log_file, 'w') as output:\n            writer = csv.writer(output, delimiter = ' ')\n            writer.writerow(header)\n            writer.writerows(map(lambda packet: packet.row, input_packets))\n\n\ndef rank_origins(file_type, input_log_file, output_log_file, cut, tracert = False, **kwargs):\n    input_packets, header = get_incoming_packets(file_type, input_log_file)\n\n    sources_dict = {}\n    for packet in input_packets:\n        source = packet.src\n        if source in sources_dict:\n            sources_dict[source]+=1\n        else:\n            sources_dict[source]=1\n\n    sources_list = sorted(sources_dict.items(), key=lambda dict_item: dict_item[1], reverse=True)\n    reduced_sources_list = list(filter(lambda source: source[1] > SOURCES_THRESHOLD, sources_list))\n\n    asn_dict = {}\n    for source in reduced_sources_list:\n        try:\n            asn, name = query_address_asn(source[0])\n        except ipaddress.AddressValueError:\n            # Not an IPv4 (Host or IPv6)\n            continue\n        except IndexError:\n            # No known origin\n            continue\n        if asn in asn_dict:\n            asn_dict[asn][1]+=source[1]\n            asn_dict[asn][2].append(source[0])\n        else:\n            asn_dict[asn]=[name, source[1], [source[0]]]\n\n    asn_sources_list = sorted(asn_dict.items(), key=lambda dict_item: dict_item[1][1], reverse=True)\n\n    # Cut the list\n    asn_sources_list = asn_sources_list[:cut]\n\n    # Tracert if needed\n    if tracert:\n        temp_list = []\n        for asn_record in asn_sources_list:\n            # [1] is for the value index in the (key, value) tuple.\n            # [2] is for the IP list in the value tuple which looks like this (name, # of packets, IP list)\n            # [0] is for the FIRST IP in the IP list.\n            first_ip = asn_record[1][2][0]\n            number_of_hops = number_of_hops_to_hostname(first_ip)\n            temp_list.append((asn_record[0], [asn_record[1][0], asn_record[1][1], number_of_hops]))\n        asn_sources_list = temp_list\n\n    if output_log_file is not None:\n        with open(output_log_file, 'w', newline='') as output:\n            writer = csv.writer(output, delimiter = ' ')\n\n            if tracert:\n                data_index = 3\n            else:\n                data_index = 2\n\n            for asn, AS_data in asn_sources_list:\n                writer.writerow([asn]+AS_data[:data_index])\n\n    return asn_sources_list\n\n\ndef get_incoming_packets(file_type, input_log_file):\n    input_packets = []\n    header = ''\n    with open(input_log_file, 'r') as input:\n        if file_type == 'firewall':\n            reader = csv.reader(input, delimiter = ' ', quotechar='\"')\n            for row in reader:\n                if reader.line_num == 1:\n                    header = row\n                elif row[INTERFACE_COLUMN] == INCOMING_INTERFACE:\n                    input_packets.append(FirewallPacket(row))\n        elif file_type == 'tcpdump':\n            p = subprocess.Popen('java -jar {0} \"{1}\" {2}'.format(DUMP_PARSER, input_log_file, DUMP_PARSER_ARGS), stdout = subprocess.PIPE)\n            header = ''\n            for line in p.stdout.readlines():\n                packet_data = line.decode(\"utf-8\").split()\n                input_packets.append(TCPDUMPPacket(*packet_data))\n        else:\n            raise Exception('Unsupported file type')\n    \n    return input_packets, header\n\n\nif (__name__ == \"__main__\"):\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"file_type\", choices=['tcpdump', 'firewall'])\n    parser.add_argument(\"input_log_file\",)\n\n    subparsers = parser.add_subparsers()\n    # Filtered log file subparser\n    incoming_parser = subparsers.add_parser('filter_incoming', help='Only produce a log file of incoming traffic')\n    incoming_parser.set_defaults(func=filter_incoming)\n    incoming_parser.add_argument('output_log_file', nargs='?', default=DEFAULT_OUTPUT_FILE, help='Path of output file. Defaults to %(default)s')\n\n    # AS ranking file\n    ranking_parser = subparsers.add_parser('rank_ases', help='Produce a ranking of the origin ASes')\n    ranking_parser.set_defaults(func=rank_origins)\n    ranking_parser.add_argument('--tracert', action='store_true', help='Traceroutes the top ranked ASes to tell the TTL distance')\n    ranking_parser.add_argument('--cut', default=30, help='Show only this amount of ranked ASes')\n    ranking_parser.add_argument('output_log_file', nargs='?', default=DEFAULT_OUTPUT_FILE, help='Path of output file. Defaults to %(default)s')\n\n    args = parser.parse_args()\n    args.func(**vars(args))\n","sub_path":"process_log_file.py","file_name":"process_log_file.py","file_ext":"py","file_size_in_byte":6933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"541511381","text":"# -*- encoding: utf-8 -*-\n#################################################################################\n#                                                                               #\n# Copyright (C) 2009  Renato Lima - Akretion                                    #\n#                                                                               #\n#This program is free software: you can redistribute it and/or modify           #\n#it under the terms of the GNU Affero General Public License as published by    #\n#the Free Software Foundation, either version 3 of the License, or              #\n#(at your option) any later version.                                            #\n#                                                                               #\n#This program is distributed in the hope that it will be useful,                #\n#but WITHOUT ANY WARRANTY; without even the implied warranty of                 #\n#MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the                  #\n#GNU Affero General Public License for more details.                            #\n#                                                                               #\n#You should have received a copy of the GNU Affero General Public License       #\n#along with this program.  If not, see <http://www.gnu.org/licenses/>.          #\n#################################################################################\n\nimport time\nfrom datetime import datetime\nimport netsvc\nimport re\nimport string\nfrom unicodedata import normalize\n\nfrom osv import fields, osv\nfrom tools.translate import _\nimport pooler\n\ndef nfe_export(cr, uid, ids, nfe_environment='1', context=False):\n    StrFile = ''\n    StrNF = 'NOTA FISCAL|%s|\\n' % len(ids)\n    StrFile = StrNF\n    pool = pooler.get_pool(cr.dbname)\n\n    for inv in pool.get('account.invoice').browse(cr, uid, ids, context={'lang': 'pt_BR'}):\n    \t#Endereço do company\n    \tcompany_addr = pool.get('res.partner').address_get(cr, uid, [inv.company_id.partner_id.id], ['default'])\n    \tcompany_addr_default = pool.get('res.partner.address').browse(cr, uid, [company_addr['default']], context={'lang': 'pt_BR'})[0]\n    \tStrA = 'A|%s|%s|\\n' % ('2.00', '')\n    \tStrFile += StrA\n    \tStrRegB = {\n    \t\t       'cUF': company_addr_default.state_id.ibge_code,\n    \t\t       'cNF': '',\n    \t\t       'NatOp': normalize('NFKD',unicode(inv.cfop_ids[0].small_name or '')).encode('ASCII','ignore'),\n    \t\t       'intPag': '2', \n    \t\t       'mod': inv.fiscal_document_id.code,\n    \t\t       'serie': inv.document_serie_id.code,\n    \t\t       'nNF': inv.internal_number or '',\n    \t\t       'dEmi': inv.date_invoice or '',\n    \t\t       'dSaiEnt': inv.date_invoice or '',\n    \t\t       'hSaiEnt': '',\n    \t\t       'tpNF': '',\n    \t\t       'cMunFG': ('%s%s') % (company_addr_default.state_id.ibge_code, company_addr_default.l10n_br_city_id.ibge_code),\n    \t\t       'TpImp': '1',\n    \t\t       'TpEmis': '1',\n    \t\t       'cDV': '',\n    \t\t       'tpAmb': nfe_environment,\n    \t\t       'finNFe': '1',\n    \t\t       'procEmi': '0',\n    \t\t       'VerProc': '2.0.9',\n    \t\t       'dhCont': '',\n    \t\t       'xJust': '',\n    \t\t       }\n    \n    \tif inv.cfop_ids[0].type in (\"input\"):\n    \t\tStrRegB['tpNF'] = '0'\n    \telse:\n    \t\tStrRegB['tpNF'] = '1' \n    \n    \tStrB = 'B|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegB['cUF'], StrRegB['cNF'], StrRegB['NatOp'], StrRegB['intPag'], \n    \t\t                                                                 StrRegB['mod'], StrRegB['serie'], StrRegB['nNF'], StrRegB['dEmi'], StrRegB['dSaiEnt'],\n    \t\t                                                                 StrRegB['hSaiEnt'], StrRegB['tpNF'], StrRegB['cMunFG'], StrRegB['TpImp'], StrRegB['TpEmis'],\n    \t\t                                                                 StrRegB['cDV'], StrRegB['tpAmb'], StrRegB['finNFe'], StrRegB['procEmi'], StrRegB['VerProc'], \n    \t\t                                                                 StrRegB['dhCont'], StrRegB['xJust'])\n    \tStrFile += StrB\n    \n    \tStrRegC = {\n    \t\t       'XNome': normalize('NFKD',unicode(inv.company_id.partner_id.legal_name or '')).encode('ASCII','ignore'), \n    \t\t       'XFant': normalize('NFKD',unicode(inv.company_id.partner_id.name or '')).encode('ASCII','ignore'),\n    \t\t       'IE': re.sub('[%s]' % re.escape(string.punctuation), '', inv.company_id.partner_id.inscr_est or ''),\n    \t\t       'IEST': '',\n    \t\t       'IM': re.sub('[%s]' % re.escape(string.punctuation), '', inv.company_id.partner_id.inscr_mun or ''),\n    \t\t       'CNAE': re.sub('[%s]' % re.escape(string.punctuation), '', inv.company_id.cnae_main_id.code or ''),\n    \t\t       'CRT': inv.company_id.fiscal_type or '',\n    \t\t       }\n    \n    \t#TODO - Verificar, pois quando e informado do CNAE ele exige que a inscricao municipal, parece um bug do emissor da NFE\n    \tif not inv.company_id.partner_id.inscr_mun:\n    \t\tStrRegC['CNAE'] = ''\n    \n    \tStrC = 'C|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegC['XNome'], StrRegC['XFant'], StrRegC['IE'], StrRegC['IEST'], \n    \t\t                                StrRegC['IM'],StrRegC['CNAE'],StrRegC['CRT'])\n    \n    \tStrFile += StrC\n    \n    \tif inv.company_id.partner_id.tipo_pessoa == 'J':\n    \t\tStrC02 = 'C02|%s|\\n' % (re.sub('[%s]' % re.escape(string.punctuation), '', inv.company_id.partner_id.cnpj_cpf or ''))\n    \telse:\n    \t\tStrC02 = 'C02a|%s|\\n' % (re.sub('[%s]' % re.escape(string.punctuation), '', inv.company_id.partner_id.cnpj_cpf or ''))\n    \n    \tStrFile += StrC02\n    \n    \taddress_company_bc_code = ''\n    \tif company_addr_default.country_id.bc_code:\n    \t\taddress_company_bc_code = company_addr_default.country_id.bc_code[1:]\n    \n    \tStrRegC05 = {\n    \t\t       'XLgr': normalize('NFKD',unicode(company_addr_default.street or '')).encode('ASCII','ignore'), \n    \t\t       'Nro': company_addr_default.number or '',\n    \t\t       'Cpl': normalize('NFKD',unicode(company_addr_default.street2 or '')).encode('ASCII','ignore'),\n    \t\t       'Bairro': normalize('NFKD',unicode(company_addr_default.district or 'Sem Bairro')).encode('ASCII','ignore'),\n    \t\t       'CMun': '%s%s' % (company_addr_default.state_id.ibge_code, company_addr_default.l10n_br_city_id.ibge_code),\n    \t\t       'XMun':  normalize('NFKD',unicode(company_addr_default.l10n_br_city_id.name or '')).encode('ASCII','ignore'),\n    \t\t       'UF': company_addr_default.state_id.code or '',\n    \t\t       'CEP': re.sub('[%s]' %  re.escape(string.punctuation), '', str(company_addr_default.zip or '').replace(' ','')),\n    \t\t       'cPais': address_company_bc_code,\n    \t\t       'xPais': normalize('NFKD',unicode(company_addr_default.country_id.name or '')).encode('ASCII','ignore'),\n    \t\t       'fone': re.sub('[%s]' % re.escape(string.punctuation), '', str(company_addr_default.phone or '').replace(' ','')),\n    \t\t       }\n    \n    \tStrC05 = 'C05|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegC05['XLgr'], StrRegC05['Nro'], StrRegC05['Cpl'], StrRegC05['Bairro'],\n    \t\t                                                  StrRegC05['CMun'], StrRegC05['XMun'], StrRegC05['UF'], StrRegC05['CEP'],\n    \t\t                                                  StrRegC05['cPais'], StrRegC05['xPais'], StrRegC05['fone'])\n    \n    \tStrFile += StrC05\n    \n    \t#Se o ambiente for de teste deve ser escrito na razão do destinatário\n    \tif nfe_environment == '2': \n    \t\txNome = 'NF-E EMITIDA EM AMBIENTE DE HOMOLOGACAO - SEM VALOR FISCAL'\n    \telse:\n    \t\txNome = normalize('NFKD', unicode(inv.partner_id.legal_name or '')).encode('ASCII', 'ignore')\n    \n    \tStrRegE = {\n    \t\t       'xNome': xNome, \n    \t\t       'IE': re.sub('[%s]' % re.escape(string.punctuation), '', inv.partner_id.inscr_est or ''),\n    \t\t       'ISUF': '',\n    \t\t       'email': inv.partner_id.email or '',\n    \t\t       }\n    \n    \tStrE = 'E|%s|%s|%s|%s|\\n' % (StrRegE['xNome'], StrRegE['IE'], StrRegE['ISUF'], StrRegE['email'])\n    \n    \tStrFile += StrE\n    \n    \tif inv.partner_id.tipo_pessoa == 'J':\n    \t\tStrE0 = 'E02|%s|\\n' % (re.sub('[%s]' % re.escape(string.punctuation), '', inv.partner_id.cnpj_cpf or ''))\n    \telse:\n    \t\tStrE0 = 'E03|%s|\\n' % (re.sub('[%s]' % re.escape(string.punctuation), '', inv.partner_id.cnpj_cpf or ''))\n    \n    \tStrFile += StrE0\n    \n    \taddress_invoice_bc_code = ''\n    \tif inv.address_invoice_id.country_id.bc_code:\n    \t\taddress_invoice_bc_code = inv.address_invoice_id.country_id.bc_code[1:]\n    \n    \tStrRegE05 = {\n    \t\t       'xLgr': normalize('NFKD',unicode(inv.address_invoice_id.street or '')).encode('ASCII','ignore'),\n    \t\t       'nro': normalize('NFKD',unicode(inv.address_invoice_id.number or '')).encode('ASCII','ignore'),\n    \t\t       'xCpl': re.sub('[%s]' % re.escape(string.punctuation), '', normalize('NFKD',unicode(inv.address_invoice_id.street2 or '' )).encode('ASCII','ignore')),\n    \t\t       'xBairro': normalize('NFKD',unicode(inv.address_invoice_id.district or 'Sem Bairro')).encode('ASCII','ignore'),\n    \t\t       'cMun': ('%s%s') % (inv.address_invoice_id.state_id.ibge_code, inv.address_invoice_id.l10n_br_city_id.ibge_code),\n    \t\t       'xMun': normalize('NFKD',unicode(inv.address_invoice_id.l10n_br_city_id.name or '')).encode('ASCII','ignore'),\n    \t\t       'UF': inv.address_invoice_id.state_id.code,\n    \t\t       'CEP': re.sub('[%s]' % re.escape(string.punctuation), '', str(inv.address_invoice_id.zip or '').replace(' ','')),\n    \t\t       'cPais': address_invoice_bc_code,\n    \t\t       'xPais': normalize('NFKD',unicode(inv.address_invoice_id.country_id.name or '')).encode('ASCII','ignore'),\n    \t\t       'fone': re.sub('[%s]' % re.escape(string.punctuation), '', str(inv.address_invoice_id.phone or '').replace(' ','')),\n    \t\t       }\n    \n    \tStrE05 = 'E05|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegE05['xLgr'], StrRegE05['nro'], StrRegE05['xCpl'], StrRegE05['xBairro'],\n    \t\t                                           StrRegE05['cMun'], StrRegE05['xMun'], StrRegE05['UF'], StrRegE05['CEP'],\n    \t\t                                           StrRegE05['cPais'],StrRegE05['xPais'], StrRegE05['fone'],)\n    \n    \tStrFile += StrE05\n    \n    \tif inv.partner_shipping_id:\n    \t\t\n    \t\tif inv.address_invoice_id != inv.partner_shipping_id: \n    \n    \t\t    StrRegG = {\n    \t\t               'XLgr': normalize('NFKD',unicode(inv.partner_shipping_id.street or '',)).encode('ASCII','ignore'),\n    \t\t               'Nro': normalize('NFKD',unicode(inv.partner_shipping_id.number or '')).encode('ASCII','ignore'),\n    \t\t               'XCpl': re.sub('[%s]' % re.escape(string.punctuation), '', normalize('NFKD',unicode(inv.partner_shipping_id.street2 or '' )).encode('ASCII','ignore')),\n    \t\t               'XBairro': re.sub('[%s]' % re.escape(string.punctuation), '', normalize('NFKD',unicode(inv.partner_shipping_id.district or 'Sem Bairro' )).encode('ASCII','ignore')),\n    \t\t               'CMun': ('%s%s') % (inv.partner_shipping_id.state_id.ibge_code, inv.partner_shipping_id.l10n_br_city_id.ibge_code),\n    \t\t               'XMun': normalize('NFKD',unicode(inv.partner_shipping_id.l10n_br_city_id.name or '')).encode('ASCII','ignore'),\n    \t\t               'UF': inv.address_invoice_id.state_id.code,\n    \t\t             }\n      \n    \t\t    StrG = 'G|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegG['XLgr'],StrRegG['Nro'],StrRegG['XCpl'],StrRegG['XBairro'],StrRegG['CMun'],StrRegG['XMun'],StrRegG['UF'])\n    \t\t    StrFile += StrG\n    \t\t    \n    \t\t    if inv.partner_id.tipo_pessoa == 'J':\n    \t\t        StrG0 = 'G02|%s|\\n' % (re.sub('[%s]' % re.escape(string.punctuation), '', inv.partner_id.cnpj_cpf or ''))\n    \t\t    else:\n    \t\t        StrG0 = 'G02a|%s|\\n' % (re.sub('[%s]' % re.escape(string.punctuation), '', inv.partner_id.cnpj_cpf or ''))\n    \n    \t\t    StrFile += StrG0\n    \n    \ti = 0\n    \tfor inv_line in inv.invoice_line:\n    \t\ti += 1\n    \n    \t\tStrH = 'H|%s||\\n' % (i)\n    \n    \t\tStrFile += StrH\n    \n    \t\tStrRegI = {\n    \t\t       'CProd': normalize('NFKD',unicode(inv_line.product_id.code or '',)).encode('ASCII','ignore'),\n    \t\t       'CEAN': inv_line.product_id.ean13 or '',\n    \t\t       'XProd': normalize('NFKD',unicode(inv_line.product_id.name or '')).encode('ASCII','ignore'),\n    \t\t       'NCM': re.sub('[%s]' % re.escape(string.punctuation), '', inv_line.product_id.property_fiscal_classification.name or ''),\n    \t\t       'EXTIPI': '',\n    \t\t       'CFOP': inv_line.cfop_id.code,\n    \t\t       'UCom': normalize('NFKD',unicode(inv_line.uos_id.name or '',)).encode('ASCII','ignore'),\n    \t\t       'QCom': str(\"%.4f\" % inv_line.quantity),\n    \t\t       'VUnCom': str(\"%.2f\" % (inv_line.price_unit * (1-(inv_line.discount or 0.0)/100.0))),\n    \t\t       'VProd': str(\"%.2f\" % inv_line.price_total),\n    \t\t       'CEANTrib': inv_line.product_id.ean13 or '',\n    \t\t       'UTrib': inv_line.uos_id.name,\n    \t\t       'QTrib': str(\"%.4f\" % inv_line.quantity),\n    \t\t       'VUnTrib': str(\"%.2f\" % inv_line.price_unit),\n    \t\t       'VFrete': '',\n    \t\t       'VSeg': '',\n    \t\t       'VDesc': '',\n    \t\t       'vOutro': '',\n    \t\t       'indTot': '1',\n    \t\t       'xPed': '',\n    \t\t       'nItemPed': '',\n    \t\t       }\n    \n    \t\tif inv_line.product_id.code:\n    \t\t    StrRegI['CProd'] = inv_line.product_id.code\n    \t\telse:\n    \t\t    StrRegI['CProd'] = unicode(i).strip().rjust(4, u'0')\n    \n    \t\t#No OpenERP já traz o valor unitário como desconto\n    \t\t#if inv_line.discount > 0:\n    \t\t#    StrRegI['VDesc'] = str(\"%.2f\" % (inv_line.quantity * (inv_line.price_unit * (1-(inv_line.discount or 0.0)/100.0))))\n    \n    \t\tStrI = 'I|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegI['CProd'], StrRegI['CEAN'], StrRegI['XProd'], StrRegI['NCM'],\n    \t\t                                                                          StrRegI['EXTIPI'], StrRegI['CFOP'], StrRegI['UCom'], StrRegI['QCom'], \n    \t\t                                                                          StrRegI['VUnCom'], StrRegI['VProd'], StrRegI['CEANTrib'], StrRegI['UTrib'],\n    \t\t                                                                          StrRegI['QTrib'], StrRegI['VUnTrib'], StrRegI['VFrete'], StrRegI['VSeg'],\n    \t\t                                                                          StrRegI['VDesc'], StrRegI['vOutro'], StrRegI['indTot'], StrRegI['xPed'],\n    \t\t                                                                          StrRegI['nItemPed'])\n    \t\t\n    \t\tStrFile += StrI\n    \t\t\n    \t\tStrM = 'M|\\n'\n    \t\t\n    \t\tStrFile += StrM\n    \t\t\n    \t\tStrN = 'N|\\n'\n     \n    \t\tStrFile += StrN\n    \n    \t\t#TODO - Fazer alteração para cada tipo de cst\n    \t\tif inv_line.icms_cst in ('00'):\n    \t\t    \n    \t\t    StrRegN02 = {\n    \t\t       'Orig': inv_line.product_id.origin or '0',\n    \t\t       'CST': inv_line.icms_cst,\n    \t\t       'ModBC': '0',\n    \t\t       'VBC': str(\"%.2f\" % inv_line.icms_base),\n    \t\t       'PICMS': str(\"%.2f\" % inv_line.icms_percent),\n    \t\t       'VICMS': str(\"%.2f\" % inv_line.icms_value),\n    \t\t       }\n    \t\t\n    \t\t    StrN02 = 'N02|%s|%s|%s|%s|%s|%s|\\n' % (StrRegN02['Orig'], StrRegN02['CST'], StrRegN02['ModBC'], StrRegN02['VBC'], StrRegN02['PICMS'],\n    \t\t                                     StrRegN02['VICMS'])\n    \t\t    \n    \t\t    StrFile += StrN02\n    \t\t\n    \t\tif inv_line.icms_cst in ('20'):\n    \n    \t\t    StrRegN04 = {\n    \t\t       'Orig': inv_line.product_id.origin or '0',\n    \t\t       'CST': inv_line.icms_cst,\n    \t\t       'ModBC': '0',\n    \t\t       'PRedBC': str(\"%.2f\" % inv_line.icms_percent_reduction),\n    \t\t       'VBC': str(\"%.2f\" % inv_line.icms_base),\n    \t\t       'PICMS': str(\"%.2f\" % inv_line.icms_percent),\n    \t\t       'VICMS': str(\"%.2f\" % inv_line.icms_value),\n    \t\t       }\n    \t\t\n    \t\t    StrN04 = 'N04|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegN04['Orig'], StrRegN04['CST'], StrRegN04['ModBC'], StrRegN04['PRedBC'], StrRegN04['VBC'], StrRegN04['PICMS'],\n    \t\t                                              StrRegN04['VICMS'])\n    \t\t    StrFile += StrN04\n    \t\t\n    \t\tif inv_line.icms_cst in ('10'):\n    \t\t    StrRegN03 = {\n    \t\t       'Orig': inv_line.product_id.origin or '0',\n    \t\t       'CST': inv_line.icms_cst,\n    \t\t       'ModBC': '0',\n    \t\t       'VBC': str(\"%.2f\" % inv_line.icms_base),\n    \t\t       'PICMS': str(\"%.2f\" % inv_line.icms_percent),\n    \t\t       'VICMS': str(\"%.2f\" % inv_line.icms_value),\n    \t\t       'ModBCST': '4', #TODO\n    \t\t       'PMVAST': str(\"%.2f\" % inv_line.icms_st_mva) or '',\n    \t\t       'PRedBCST': '',\n    \t\t       'VBCST': str(\"%.2f\" % inv_line.icms_st_base),\n    \t\t       'PICMSST': str(\"%.2f\" % inv_line.icms_st_percent),\n    \t\t       'VICMSST': str(\"%.2f\" % inv_line.icms_st_value),\n    \t\t       }\n    \n    \t\t    StrN03 = 'N03|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegN03['Orig'], StrRegN03['CST'], StrRegN03['ModBC'], StrRegN03['VBC'], StrRegN03['PICMS'],\n    \t\t    StrRegN03['VICMS'], StrRegN03['ModBCST'], StrRegN03['PMVAST'], StrRegN03['PRedBCST'], StrRegN03['VBCST'],\n    \t\t    StrRegN03['PICMSST'], StrRegN03['VICMSST'])\n    \t\t    StrFile += StrN03\n    \t\t    \n    \t\tif inv_line.icms_cst in ('40', '41', '50', '51'):\n    \t\t    StrRegN06 = {\n    \t\t       'Orig': inv_line.product_id.origin or '0',\n    \t\t       'CST': inv_line.icms_cst,\n    \t\t       'vICMS': str(\"%.2f\" % inv_line.icms_value),\n    \t\t       'motDesICMS': '9', #FIXME\n    \t\t       }\n    \t\t\n    \t\t    StrN06 = 'N06|%s|%s|%s|%s|\\n' % (StrRegN06['Orig'], StrRegN06['CST'], StrRegN06['vICMS'], StrRegN06['motDesICMS'])\n    \t\t    \n    \t\t    StrFile += StrN06\n    \t\t\n    \t\tif inv_line.icms_cst in ('60'):                    \n    \t\t    StrRegN08 = {\n    \t\t       'Orig': inv_line.product_id.origin or '0',\n    \t\t       'CST': inv_line.icms_cst,\n    \t\t       'VBCST': str(\"%.2f\" % 0.00),\n    \t\t       'VICMSST': str(\"%.2f\" % 0.00),\n    \t\t       }\n    \n    \t\t    StrN08 = 'N08|%s|%s|%s|%s|\\n' % (StrRegN08['Orig'], StrRegN08['CST'], StrRegN08['VBCST'], StrRegN08['VICMSST'])\n    \t\t    \n    \t\t    StrFile += StrN08\n    \t\t    \n    \t\tif inv_line.icms_cst in ('70'):\n    \t\t    StrRegN09 = {\n    \t\t       'Orig': inv_line.product_id.origin or '0',\n    \t\t       'CST': inv_line.icms_cst,\n    \t\t       'ModBC': '0',\n    \t\t       'PRedBC': str(\"%.2f\" % inv_line.icms_percent_reduction),\n    \t\t       'VBC': str(\"%.2f\" % inv_line.icms_base),\n    \t\t       'PICMS': str(\"%.2f\" % inv_line.icms_percent),\n    \t\t       'VICMS': str(\"%.2f\" % inv_line.icms_value),\n    \t\t       'ModBCST': '4', #TODO\n    \t\t       'PMVAST': str(\"%.2f\" % inv_line.icms_st_mva) or '',\n    \t\t       'PRedBCST': '',\n    \t\t       'VBCST': str(\"%.2f\" % inv_line.icms_st_base),\n    \t\t       'PICMSST': str(\"%.2f\" % inv_line.icms_st_percent),\n    \t\t       'VICMSST': str(\"%.2f\" % inv_line.icms_st_value),\n    \t\t       }\n    \t\t\n    \t\t    StrN09 = 'N09|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegN09['Orig'], StrRegN09['CST'], StrRegN09['ModBC'], StrRegN09['PRedBC'], StrRegN09['VBC'], StrRegN09['PICMS'], StrRegN09['VICMS'], StrRegN09['ModBCST'],\n    StrRegN09['PMVAST'], StrRegN09['PRedBCST'], StrRegN09['VBCST'], StrRegN09['PICMSST'], StrRegN09['VICMSST'])\n    \n    \t\t    StrFile += StrN09\n    \t\t    \n    \t\tif inv_line.icms_cst in ('90', '900'):\n    \t\t    StrRegN10h = {\n    \t\t                  'Orig': inv_line.product_id.origin or '0',\n    \t\t                  'CSOSN': inv_line.icms_cst,\n    \t\t                  'modBC': '0',\n    \t\t                  'vBC': str(\"%.2f\" % 0.00),\n    \t\t                  'pRedBC': '',\n    \t\t                  'pICMS': str(\"%.2f\" % 0.00),\n    \t\t                  'vICMS': str(\"%.2f\" % 0.00),\n    \t\t                  'modBCST': '',\n    \t\t                  'pMVAST': '',\n    \t\t                  'pRedBCST': '',\n    \t\t                  'vBCST': '',\n    \t\t                  'pICMSST': '',\n    \t\t                  'vICMSST': '',\n    \t\t                  'pCredSN': str(\"%.2f\" % 0.00),\n    \t\t                  'vCredICMSSN': str(\"%.2f\" % 0.00),\n    \t\t                  }\n    \t\t                    \n    \t\t    StrN10h = 'N10h|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegN10h['Orig'], \n    \t\t                                                                        StrRegN10h['CSOSN'], \n    \t\t                                                                        StrRegN10h['modBC'],\n    \t\t                                                                        StrRegN10h['vBC'],\n    \t\t                                                                        StrRegN10h['pRedBC'],\n    \t\t                                                                        StrRegN10h['pICMS'],\n    \t\t                                                                        StrRegN10h['vICMS'],\n    \t\t                                                                        StrRegN10h['modBCST'],\n    \t\t                                                                        StrRegN10h['pMVAST'],\n    \t\t                                                                        StrRegN10h['pRedBCST'],\n    \t\t                                                                        StrRegN10h['vBCST'],\n    \t\t                                                                        StrRegN10h['pICMSST'],\n    \t\t                                                                        StrRegN10h['vICMSST'],\n    \t\t                                                                        StrRegN10h['pCredSN'],\n    \t\t                                                                        StrRegN10h['vCredICMSSN'])\n    \n    \t\t    StrFile += StrN10h\n    \n    \t\tStrRegO = {\n    \t\t       'ClEnq': '',\n    \t\t       'CNPJProd': '',\n    \t\t       'CSelo': '',\n    \t\t       'QSelo': '',\n    \t\t       'CEnq': '999',\n    \t\t}\n    \t\t\n    \t\tStrO = 'O|%s|%s|%s|%s|%s|\\n' % (StrRegO['ClEnq'], StrRegO['CNPJProd'], StrRegO['CSelo'], StrRegO['QSelo'], StrRegO['CEnq']) \n    \t\t\n    \t\tStrFile += StrO\n    \n    \t\tif inv_line.ipi_cst in ('50', '51', '52') and inv_line.ipi_percent > 0:\n    \t\t    StrRegO07 = {\n    \t\t       'CST': inv_line.ipi_cst,\n    \t\t       'VIPI': str(\"%.2f\" % inv_line.ipi_value),\n    \t\t    }\n    \t\t    \n    \t\t    StrO07 = 'O07|%s|%s|\\n' % (StrRegO07['CST'], StrRegO07['VIPI'])\n    \t\t    \n    \t\t    StrFile += StrO07 \n    \n    \t\t    if inv_line.ipi_type == 'percent' or '':\n    \t\t        StrRegO10 = {\n    \t\t           'VBC': str(\"%.2f\" % inv_line.ipi_base),\n    \t\t           'PIPI': str(\"%.2f\" % inv_line.ipi_percent),\n    \t\t        }\n    \t\t        StrO1 = 'O10|%s|%s|\\n' % (StrRegO10['VBC'], StrRegO10['PIPI'])\n    \t\t    \n    \t\t    if inv_line.ipi_type == 'quantity':\n    \t\t        pesol = 0\n    \t\t        if inv_line.product_id:\n    \t\t            pesol = inv_line.product_id.weight_net\n    \t\t        StrRegO11 = {\n    \t\t           'QUnid': str(\"%.4f\" % (inv_line.quantity * pesol)),\n    \t\t           'VUnid': str(\"%.4f\" % inv_line.ipi_percent),\n    \t\t        }\n    \t\t        StrO1 = 'O11|%s|%s|\\n' % (StrRegO11['QUnid'], StrRegO11['VUnid'])\n    \t\t    \n    \t\t    StrFile += StrO1\n    \t\t\n    \t\tif inv_line.ipi_cst in ('99'):\n    \t\t    StrRegO07 = {\n    \t\t                 'CST': inv_line.ipi_cst,\n    \t\t                 'VIPI': str(\"%.2f\" % inv_line.ipi_value),\n    \t\t                 }\n    \t\t    \n    \t\t    StrO07 = ('O07|%s|%s|\\n') % (StrRegO07['CST'], StrRegO07['VIPI'])\n    \t\t    StrFile += StrO07\n    \t\t    \n    \t\t    StrRegO10 = {\n    \t\t                 'VBC': str(\"%.2f\" % inv_line.ipi_base),\n    \t\t                 'PIPI': str(\"%.2f\" % inv_line.ipi_percent),\n    \t\t                 }\n    \t\t    \n    \t\t    StrO10 = ('O10|%s|%s|\\n') % (StrRegO10['VBC'], StrRegO10['PIPI'])\n    \t\t    StrFile += StrO10\n    \t\t    \n    \t\tif inv_line.ipi_percent == 0 and not inv_line.ipi_cst in ('99'):\n    \t\t    StrO1 = 'O08|%s|\\n' % inv_line.ipi_cst\n    \t\t    StrFile += StrO1\n    \t\t    \n    \t\tStrQ = 'Q|\\n'\n    \t\t\n    \t\tStrFile += StrQ\n    \t\t    \n    \t\tif inv_line.pis_cst in ('01') and inv_line.pis_percent > 0:\n    \t\t    StrRegQ02 = {\n    \t\t                 'CST': inv_line.pis_cst,\n    \t\t                 'VBC': str(\"%.2f\" % inv_line.pis_base),\n    \t\t                 'PPIS': str(\"%.2f\" % inv_line.pis_percent),\n    \t\t                 'VPIS': str(\"%.2f\" % inv_line.pis_value),\n    \t\t                 }\n    \t\t    \n    \t\t    StrQ02 = ('Q02|%s|%s|%s|%s|\\n') % (StrRegQ02['CST'], \n    \t\t                                       StrRegQ02['VBC'], \n    \t\t                                       StrRegQ02['PPIS'], \n    \t\t                                       StrRegQ02['VPIS'])\n    \t\t    \n    \t\t    StrFile += StrQ02\n    \t\t    \n    \t\tif inv_line.pis_cst in ('99'):\n    \t\t    StrRegQ05 = {\n    \t\t                 'CST': inv_line.pis_cst,\n    \t\t                 'VPIS': str(\"%.2f\" % inv_line.pis_value),\n    \t\t                 }\n    \t\t    \n    \t\t    StrQ05 = ('Q05|%s|%s|\\n') % (StrRegQ05['CST'], StrRegQ05['VPIS'])\n    \t\t    StrFile += StrQ05\n    \t\t    \n    \t\t    StrRegQ07 = {\n    \t\t                 'VBC': str(\"%.2f\" % inv_line.pis_base),\n    \t\t                 'PPIS': str(\"%.2f\" % inv_line.pis_percent),\n    \t\t                 }\n    \t\t    \n    \t\t    StrQ07 = ('Q07|%s|%s|\\n') % (StrRegQ07['VBC'], StrRegQ07['PPIS'])\n    \t\t    StrFile += StrQ07\n    \t\t    \n    \t\tif inv_line.pis_percent == 0 and not inv_line.pis_cst in ('99'):\n    \t\t    StrQ02 = 'Q04|%s|\\n' % inv_line.pis_cst\n    \t\t    StrFile += StrQ02\n    \t\t\n    \t\tStrQ = 'S|\\n'\n    \t\t\n    \t\tStrFile += StrQ\n    \n    \t\tif inv_line.cofins_cst in ('01') and inv_line.cofins_percent > 0:\n    \t\t    StrRegS02 = {\n    \t\t       'CST': inv_line.cofins_cst,\n    \t\t       'VBC': str(\"%.2f\" % inv_line.cofins_base),\n    \t\t       'PCOFINS': str(\"%.2f\" % inv_line.cofins_percent),\n    \t\t       'VCOFINS': str(\"%.2f\" % inv_line.cofins_value),\n    \t\t    }\n    \n    \t\t    StrS02 = ('S02|%s|%s|%s|%s|\\n') % (StrRegS02['CST'], StrRegS02['VBC'], StrRegS02['PCOFINS'], StrRegS02['VCOFINS'])\n    \t\t    StrFile += StrS02\n    \t\t    \n    \t\tif inv_line.cofins_cst in ('99'):\n    \t\t    StrRegS05 = {\n    \t\t                 'CST': inv_line.cofins_cst,\n    \t\t                 'VCOFINS': str(\"%.2f\" % inv_line.cofins_value),\n    \t\t                 }\n    \t\t    \n    \t\t    StrS05 = ('S05|%s|%s|\\n') % (StrRegS05['CST'], StrRegS05['VCOFINS'])\n    \t\t    StrFile += StrS05\n    \t\t    \n    \t\t    StrRegS07 = {\n    \t\t                 'VBC': str(\"%.2f\" % inv_line.cofins_base),\n    \t\t                 'PCOFINS': str(\"%.2f\" % inv_line.cofins_percent),\n    \t\t                 }\n    \t\t    \n    \t\t    StrS07 = ('S07|%s|%s|\\n') % (StrRegS07['VBC'], StrRegS07['PCOFINS'])\n    \t\t    StrFile += StrS07\n    \t\t        \n    \tif inv_line.cofins_percent == 0 and not inv_line.cofins_cst in ('99'):\n    \t\tStrS02 = 'S04|%s|\\n' % inv_line.cofins_cst\n    \t\tStrFile += StrS02\n    \t\t\n    \tStrW = 'W|\\n'\n    \n    \tStrFile += StrW\n    \n    \tStrRegW02 = {\n    \t\t         'vBC': str(\"%.2f\" % inv.icms_base),\n    \t\t         'vICMS': str(\"%.2f\" % inv.icms_value),\n    \t\t         'vBCST': str(\"%.2f\" % inv.icms_st_base),\n    \t\t         'vST': str(\"%.2f\" % inv.icms_st_value),\n    \t\t         'vProd': str(\"%.2f\" % inv.amount_untaxed),\n    \t\t         'vFrete': str(\"%.2f\" % inv.amount_freight),\n    \t\t         'vSeg': str(\"%.2f\" % inv.amount_insurance),\n    \t\t         'vDesc': '0.00',\n    \t\t         'vII': '0.00',\n    \t\t         'vIPI': str(\"%.2f\" % inv.ipi_value),\n    \t\t         'vPIS': str(\"%.2f\" % inv.pis_value),\n    \t\t         'vCOFINS': str(\"%.2f\" % inv.cofins_value),\n    \t\t         'vOutro': str(\"%.2f\" % inv.amount_costs),\n    \t\t         'vNF': str(\"%.2f\" % inv.amount_total),\n    \t\t         }\n    \n    \tStrW02 = 'W02|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|%s|\\n' % (StrRegW02['vBC'], StrRegW02['vICMS'], StrRegW02['vBCST'], StrRegW02['vST'], StrRegW02['vProd'],\n    \t\t                                                         StrRegW02['vFrete'], StrRegW02['vSeg'], StrRegW02['vDesc'], StrRegW02['vII'], StrRegW02['vIPI'],\n    \t\t                                                         StrRegW02['vPIS'], StrRegW02['vCOFINS'], StrRegW02['vOutro'], StrRegW02['vNF'])\n    \n    \tStrFile += StrW02\n    \n    \t# Modo do Frete: 0- Por conta do emitente; 1- Por conta do destinatário/remetente; 2- Por conta de terceiros; 9- Sem frete (v2.0)\n    \tif not inv.incoterm:\n    \t\tStrRegX0 = '9'\n    \telse:\n    \t\tStrRegX0 = inv.incoterm.freight_responsibility                      \n    \n    \tStrX = 'X|%s|\\n' % (StrRegX0)\n    \n    \tStrFile += StrX\n    \n    \tStrRegX03 = {\n    \t\t      'XNome': '',\n    \t\t      'IE': '',\n    \t\t      'XEnder': '',\n    \t\t      'UF': '',\n    \t\t      'XMun': '',\n    \t\t      }\n    \n    \tStrX0 = ''\n    \n    \tif inv.carrier_id:            \n    \n    \t\t#Endereço da transportadora\n    \t\tcarrier_addr = pool.get('res.partner').address_get(cr, uid, [inv.carrier_id.partner_id.id], ['default'])\n    \t\tcarrier_addr_default = pool.get('res.partner.address').browse(cr, uid, [carrier_addr['default']])[0]\n    \t\t\n    \t\tif inv.carrier_id.partner_id.legal_name:\n    \t\t    StrRegX03['XNome'] = normalize('NFKD', unicode(inv.carrier_id.partner_id.legal_name or '')).encode('ASCII', 'ignore')\n    \t\telse:\n    \t\t    StrRegX03['XNome'] = normalize('NFKD', unicode(inv.carrier_id.partner_id.name or '')).encode('ASCII', 'ignore')\n    \t\t\n    \t\tStrRegX03['IE'] = inv.carrier_id.partner_id.inscr_est or ''\n    \t\tStrRegX03['xEnder'] = normalize('NFKD', unicode(carrier_addr_default.street or '')).encode('ASCII', 'ignore')\n    \t\tStrRegX03['UF'] = carrier_addr_default.state_id.code or ''\n    \t\t\n    \t\tif carrier_addr_default.l10n_br_city_id:\n    \t\t    StrRegX03['xMun'] = normalize('NFKD', unicode(carrier_addr_default.l10n_br_city_id.name or '')).encode('ASCII', 'ignore')\n    \t\t\n    \t\tif inv.carrier_id.partner_id.tipo_pessoa == 'J':\n    \t\t    StrX0 = 'X04|%s|\\n' %  (re.sub('[%s]' % re.escape(string.punctuation), '', inv.carrier_id.partner_id.cnpj_cpf or ''))\n    \t\telse:\n    \t\t    StrX0 = 'X05|%s|\\n' %  (re.sub('[%s]' % re.escape(string.punctuation), '', inv.carrier_id.partner_id.cnpj_cpf or ''))\n    \n    \tStrX03 = 'X03|%s|%s|%s|%s|%s|\\n' % (StrRegX03['XNome'], StrRegX03['IE'], StrRegX03['XEnder'], StrRegX03['UF'], StrRegX03['XMun'])\n    \n    \tStrFile += StrX03\n    \tStrFile += StrX0\n    \n    \tStrRegX18 = {\n    \t\t         'Placa': '',\n    \t\t         'UF': '',\n    \t\t         'RNTC': '',\n    \t\t         }\n    \n    \tif inv.vehicle_id:\n    \t\tStrRegX18['Placa'] = inv.vehicle_id.plate or ''\n    \t\tStrRegX18['UF'] = inv.vehicle_id.plate.state_id.code or ''\n    \t\tStrRegX18['RNTC'] = inv.vehicle_id.rntc_code or ''\n    \t\t         \n    \n    \tStrX18 = 'X18|%s|%s|%s|\\n' % (StrRegX18['Placa'], StrRegX18['UF'], StrRegX18['RNTC'])\n    \n    \tStrFile += StrX18\n    \n    \tStrRegX26 = {\n    \t\t         'QVol': '',\n    \t\t         'Esp': '', \n    \t\t         'Marca': '',\n    \t\t         'NVol': '',\n    \t\t         'PesoL': '',\n    \t\t         'PesoB': '',\n    \t\t         }\n    \n    \tif inv.number_of_packages:\n    \t\tStrRegX26['QVol'] = inv.number_of_packages\n    \t\tStrRegX26['Esp'] = 'Volume' #TODO\n    \t\tStrRegX26['Marca']\n    \t\tStrRegX26['NVol']\n    \t\tStrRegX26['PesoL'] = str(\"%.3f\" % inv.weight_net)\n    \t\tStrRegX26['PesoB'] = str(\"%.3f\" % inv.weight)\n    \n    \tStrX26 = 'X26|%s|%s|%s|%s|%s|%s|\\n' % (StrRegX26['QVol'], StrRegX26['Esp'], StrRegX26['Marca'], StrRegX26['NVol'], StrRegX26['PesoL'], StrRegX26['PesoB'])\n    \n    \tStrFile += StrX26\n    \n    \tif inv.journal_id.revenue_expense:\n    \n    \t\tStrY = 'Y|\\n'\n    \t\t\n    \t\tStrFile += StrY\n    \t\t\n    \t\tfor line in inv.move_line_receivable_id:\n    \t\t    StrRegY07 = {\n    \t\t       'NDup': line.name,\n    \t\t       'DVenc': line.date_maturity or inv.date_due or inv.date_invoice,\n    \t\t       'VDup': str(\"%.2f\" % line.debit),\n    \t\t       }\n    \t\t\n    \t\t    StrY07 = 'Y07|%s|%s|%s|\\n' % (StrRegY07['NDup'], StrRegY07['DVenc'], StrRegY07['VDup'])\n    \t\t    \n    \t\t    StrFile += StrY07\n    \n    \tStrRegZ = {\n    \t\t       'InfAdFisco': '',\n    \t\t       'InfCpl': normalize('NFKD',unicode(inv.comment or '')).encode('ASCII','ignore'),\n    \t\t       }\n    \n    \tStrZ = 'Z|%s|%s|\\n' % (StrRegZ['InfAdFisco'], StrRegZ['InfCpl'])\n    \n    \tStrFile += StrZ              \n    \n    \tpool.get('account.invoice').write(cr, uid, [inv.id], {'nfe_export_date': datetime.now()})\n    \n    return unicode(StrFile.encode('utf-8'), errors='replace')\n\ndef nfe_import(cr, ids, nfe_environment='1', context=False):\n    return 'TESTE Import'\n","sub_path":"l10n_br_account/nfe/serializers/txt.py","file_name":"txt.py","file_ext":"py","file_size_in_byte":32076,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"366849307","text":"\"\"\"\r\n  Copyright (c) 2016- by Dietmar W Weiss\r\n\r\n  This is free software; you can redistribute it and/or modify it\r\n  under the terms of the GNU Lesser General Public License as\r\n  published by the Free Software Foundation; either version 3.0 of\r\n  the License, or (at your option) any later version.\r\n\r\n  This software is distributed in the hope that it will be useful,\r\n  but WITHOUT ANY WARRANTY; without even the implied warranty of\r\n  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\r\n  Lesser General Public License for more details.\r\n\r\n  You should have received a copy of the GNU Lesser General Public\r\n  License along with this software; if not, write to the Free\r\n  Software Foundation, Inc., 51 Franklin St, Fifth Floor, Boston, MA\r\n  02110-1301 USA, or see the FSF site: http://www.fsf.org.\r\n\r\n  Version:\r\n      2018-06-30 DWW\r\n\"\"\"\r\n\r\n\r\nfrom collections import OrderedDict\r\nfrom pandas import read_csv, read_excel\r\nimport numpy as np\r\nfrom tempfile import gettempdir\r\n\r\n\r\ndef split(df, split, sort=None, minSubFrameSize=1):\r\n    \"\"\"\r\n    Splits DataFrame into OrderedDictionary of sub-DataFrames. The value of the\r\n        'split' column is equal in every sub-DataFrame.\r\n    Sorts optionally the rows of every sub-DataFrame according to values in\r\n        'sort' column (ascending).\r\n\r\n    Args:\r\n        df (pandas.DataFrame):\r\n            single data frame\r\n\r\n        split (string):\r\n            identifier of column used for splitting data frame\r\n\r\n        sort (string):\r\n            identifier of column used for sorting every sub-frame,\r\n            no sorting if 'sort' is empty\r\n\r\n        minSubFrameSize (int):\r\n            minimum size of sub-DataFrame; smaller sizes are excluded\r\n\r\n    Returns:\r\n        collections.OrderedDictionary (data values in 'split' column are used\r\n        as keys)\r\n\r\n    Note:\r\n        The keys of the OrderedDictionary are of the same type as the values in\r\n        the 'split' column of 'data'\r\n    \"\"\"\r\n    splitValueVariations = df[split].unique().tolist()\r\n    subFrames = OrderedDict()\r\n    for val in splitValueVariations:\r\n        sub = df.loc[df[split] == val]\r\n        if len(sub.index) >= minSubFrameSize:\r\n            if sort:\r\n                sub.sort_values(by=[sort], ascending=[True], inplace=True)\r\n            sub.index = range(len(sub.index))\r\n            subFrames[val] = sub\r\n    return OrderedDict(sorted(subFrames.items()))\r\n\r\n\r\ndef select(df, par, op, val, sort=None):\r\n    \"\"\"\r\n    Extract single sub-frame of given pandas.DataFrame; selection can be\r\n    based on multiple conditions employing the\r\n    operators  ['==', '!=', '<>', '>', '>=', '<', '<=']\r\n\r\n    Args:\r\n        df (pandas.DataFrame):\r\n            single data frame\r\n\r\n        par (string, or list/tuple of string):\r\n            identifier of column used for filtering\r\n\r\n        op (string, or list/tuple of string):\r\n            operator for comparing elements of column 'df[par]' with 'val'\r\n\r\n        val (string, float, int, or list/tuple of string, float, int):\r\n            selection value in column used for extraction\r\n\r\n        sort (string):\r\n            key of column used for sorting; no sorting if 'sort' is None\r\n\r\n    Returns:\r\n        pandas.DataFrame containing rows fulfilling condition:\r\n        (df(i, par) 'op' val)\r\n\r\n    Note:\r\n        Length of 'par' must not be greater than length of 'val' if both are\r\n        list/tuple; 'op' will be corrected if too short\r\n    \"\"\"\r\n    if par is None:\r\n        return df\r\n    if isinstance(par, str):\r\n        par = [par]\r\n    if isinstance(par, tuple):\r\n        par = list(par)\r\n    if not op:\r\n        op = '=='\r\n    if isinstance(op, str):\r\n        op = [op]\r\n    if isinstance(op, tuple):\r\n        op = list(op)\r\n    op = [x if x else '==' for x in op]\r\n    if isinstance(val, (int, float, str, bool)):\r\n        val = [val]\r\n    if isinstance(val, tuple):\r\n        val = list(val)\r\n\r\n    for i in range(len(op), len(par)):\r\n        op.append(op[0])\r\n    assert len(par) == len(val), 'len(par) != len(val)='\r\n\r\n    for x in op:\r\n        assert any([x == o for o in\r\n                   ['=', '==', '!=', '>', '>=', '<', '<=']]), \\\r\n               'invalid operator'\r\n    assert len(par) == len(val), 'incompatible parameters'\r\n\r\n    subFrames = df\r\n\r\n    for o, p, v in zip(op, par, val):\r\n        if o == '==' or o == '=':\r\n            subFrames = subFrames.loc[subFrames[p] == v]\r\n        elif o == '!=':\r\n            subFrames = subFrames.loc[subFrames[p] != v]\r\n        elif o == '>':\r\n            subFrames = subFrames.loc[subFrames[p] > v]\r\n        elif o == '>=':\r\n            subFrames = subFrames.loc[subFrames[p] >= v]\r\n        elif o == '<':\r\n            subFrames = subFrames.loc[subFrames[p] < v]\r\n        elif o == '<=':\r\n            subFrames = subFrames.loc[subFrames[p] <= v]\r\n        else:\r\n            assert 0, 'unknown operator'\r\n\r\n    if sort:\r\n        subFrames = subFrames.sort_values(by=sort)\r\n    return subFrames\r\n\r\n\r\ndef xy2frame(X, Y, xKeys=None, yKeys=None):\r\n    \"\"\"\r\n    Converts two 2D arrays to pandas.DataFrame\r\n\r\n    Args:\r\n        X (2D array of float):\r\n            input array, first index is data point index\r\n\r\n        Y (2D array of float):\r\n            output array,first index is data point index\r\n\r\n        xKeys (list of string):\r\n            column indentifiers of inputs\r\n\r\n        yKeys (list of string):\r\n            column indentifiers of outputs\r\n\r\n    Returns:\r\n        pandas.DataFrame created from X and Y arrays\r\n    \"\"\"\r\n    assert xKeys is None or X is None or len(xKeys) == X.shape[1]\r\n    assert yKeys is None or Y is None or len(yKeys) == Y.shape[1]\r\n    assert X.shape[0] == Y.shape[0]\r\n\r\n    if xKeys is None:\r\n        if X is not None:\r\n            xKeys = ['x' + str(i) for i in range(X.shape[1])]\r\n        else:\r\n            xKeys = []\r\n    if yKeys is None:\r\n        if Y is not None:\r\n            yKeys = ['y' + str(i) for i in range(Y.shape[1])]\r\n        else:\r\n            yKeys = []\r\n    dic = OrderedDict()\r\n    for j in range(len(xKeys)):\r\n        dic[xKeys[j]] = X[:, j]\r\n    for j in range(len(yKeys)):\r\n        dic[yKeys[j]] = Y[:, j]\r\n    return DataFrame(dic)\r\n\r\n\r\ndef frame2xy(df, xKeys, yKeys):\r\n    \"\"\"\r\n    Converts pandas.DataFrame to two 2D arrays\r\n\r\n    Args:\r\n        df (pandas.DataFrame):\r\n            data frame\r\n\r\n        xKeys (list of string):\r\n            list of input keys\r\n\r\n        yKeys (list of string):\r\n            list of output keys\r\n\r\n    Returns:\r\n        X (2D numpy array of float):\r\n            independent variables\r\n\r\n        Y (2D numpy array of float):\r\n            dependent variables\r\n    \"\"\"\r\n    xKeys = list(xKeys) if xKeys is not None else []\r\n    yKeys = list(yKeys) if yKeys is not None else []\r\n    assert all(k in df for k in xKeys), \\\r\n        'unknown x-keys: ' + str(xKeys) + ', valid keys: ' + df.columns\r\n    assert all(k in df for k in yKeys), \\\r\n        'unknown y-keys: ' + str(yKeys) + ', valid keys: ' + df.columns\r\n    return np.asfarray(df.loc[:, xKeys]), np.asfarray(df.loc[:, yKeys])\r\n\r\n\r\ndef excel2xy(xKeys=None, yKeys=None, file=gettempdir(),\r\n             sheet_name='data', startcol=0, startrow=0):\r\n    \"\"\"\r\n    Reads DataFrame from excel file and convert it to two 2D arrays\r\n\r\n    Args:\r\n        file (string):\r\n            full path to Excel file\r\n\r\n        sheet_name (string):\r\n            name of Excel sheet\r\n\r\n        startcol (int):\r\n            start column in sheet\r\n\r\n        startrow (int):\r\n            start row in sheet\r\n\r\n        xKeys (list of string):\r\n            list of input keys\r\n\r\n        yKeys (list of string):\r\n            list of output keys\r\n\r\n    Returns:\r\n        X (2D numpy array of float):\r\n            independent variables\r\n\r\n        Y (2D numpy array of float):\r\n            dependent variables\r\n    \"\"\"\r\n    df = read_excel(open(file, 'rb'),\r\n                    sheetname=sheet_name, startcol=startcol, startrow=startrow)\r\n    return frame2xy(df, xKeys, yKeys)\r\n\r\n\r\ndef xy2excel(X, Y=None, xKeys=None, yKeys=None, file=gettempdir(),\r\n             sheet_name='data', startcol=0, startrow=0):\r\n    \"\"\"\r\n    Converts two 2D arrays to DataFrame and write it to excel file\r\n\r\n    Args:\r\n        xKeys (list of string):\r\n            list of input keys\r\n\r\n        yKeys (list of string):\r\n            list of output keys\r\n\r\n        file (string):\r\n            full path to Excel file\r\n\r\n        sheetname (string):\r\n            name of Excel sheet\r\n\r\n        startcol (int):\r\n            start column in sheet\r\n\r\n        startrow (int):\r\n            start row in sheet\r\n\r\n    Returns:\r\n        False if file cannot be opended for writing\r\n    \"\"\"\r\n    df = xy2frame(X, Y, xKeys, yKeys)\r\n    writer = ExcelWriter(file)\r\n    if not writer:\r\n        return False\r\n    df.to_excel(writer, sheet_name=sheet_name, startcol=startcol,\r\n                startrow=startrow)\r\n    writer.save()\r\n    return True\r\n\r\n\r\ndef loadAnsysCsv(file, sort='', clean=True, silent=False):\r\n    \"\"\"\r\n    Reads csv-file of results from Ansys, removes 'Pxx - '-prefix from column\r\n    names, and removes columns whose keys contain postfix '.FD'\r\n\r\n    Args:\r\n        file (string):\r\n            full path to file\r\n\r\n        sort (string):\r\n            identifier of column for sorting, no sorting if 'sort' is empty\r\n\r\n        clean (bool):\r\n            if True, remove 'NaN' and duplicated rows\r\n\r\n        silent (bool):\r\n            if True, suppress output to console\r\n\r\n    Returns:\r\n        pandas.DataFrame\r\n\r\n    Example:\r\n        #\r\n        # 01/01/2017 09:30:18\r\n        # The parameters defined in the project are:\r\n        # ,P2 - r [mm],P9 - w [mm],P11 - h [mm] ...\r\n        #\r\n        # The following header line defines the name of the columns by ref ...\r\n        Name,P2,P9,P11, ...\r\n        DP 0,112.5,60,40, ...\r\n        DP 1,112.5,60,40, ...\r\n        ...\r\n    \"\"\"\r\n    iLineHumanReadableKeys = 3  # index of line with human redable keys\r\n    nHeaderLines = 6            # number of header lines to be skipped\r\n\r\n    # read list of human readable keys from line 3 (zero-based)\r\n    keys = None\r\n    try:\r\n        with open(file) as fp:\r\n            for i, line in enumerate(fp):\r\n                if i == iLineHumanReadableKeys:\r\n                    keys = line[:-1].split(',')\r\n                    break\r\n        fp.close()\r\n    except IOError:\r\n        if not silent:\r\n            print(\"\\n???\\n??? file: '\" + file + \"' does not exist\\n???\\n\")\r\n        raise Exception('')\r\n    keys = keys[1:]  # skipping 'Name' in first non-commented line\r\n    humanReadableKeys = ['idDesignPoint']\r\n    for key in keys:\r\n        key = key.split(' - ')[1]  # second part after '-'\r\n        humanReadableKeys.append(' '.join(key.split()))  # del multiple space\r\n\r\n    # read data frame with non-human readable column keys\r\n    data = read_csv(file, comment='#', skiprows=nHeaderLines)\r\n\r\n    # replace non-human readable column keys with human redadable ones\r\n    data.columns = humanReadableKeys\r\n\r\n    # remove columns with design point index and keys containing '.FD'\r\n    data.drop('idDesignPoint', axis=1, inplace=True)\r\n    for col in data.columns:\r\n        if '.FD' in col:\r\n            data.drop(col, axis=1, inplace=True)\r\n\r\n    # sort data frame\r\n    if sort:\r\n        if not silent:\r\n            print(\"    Sort: '\" + str(sort) + \"'\")\r\n        data.sort_values(by=[sort], ascending=[True], inplace=True)\r\n\r\n    l = list(data.columns)\r\n    d = set([x for x in l if l.count(x) > 1])\r\n    assert len(d) == 0, '\\n\\n??? Duplicated columns:\\n\\n' + str(d)\r\n\r\n    # remove NaN and duplicates\r\n    if clean:\r\n        if not silent:\r\n            print('    Clean data')\r\n        data = data.dropna(how='any')\r\n        data.drop_duplicates(keep='last', inplace=True)\r\n        data = data.astype('float')\r\n\r\n    return data\r\n\r\n\r\n# Examples ####################################################################\r\n\r\nif __name__ == '__main__':\r\n    from pandas import DataFrame\r\n\r\n    df = DataFrame({'x': [10, 32, 20, 40, 10],\r\n                    'y': [31, 41, 41, 31, 10],\r\n                    'z': [32, 61, 44, 33, 7]})\r\n    print('*** \\ndf:\\n', df)\r\n\r\n    if 1:\r\n        print('\\n*** get value from pandas.DataFrame(rowIndex, columnKey)')\r\n\r\n        # reset_index() ensures that 'index' equals 'range(len(df.index))'\r\n        df.reset_index(inplace=True)\r\n        print(\"df.get_value(2, 'y'):\", df.get_value(2, 'y'))\r\n\r\n    if 1:\r\n        print('\\n*** Example: select()')\r\n\r\n        sub = select(df=df, par=['x', 'y', 'z'],\r\n                     op=['<', '>=', '>='], val=[40, 41, 33], sort='x')\r\n        print(sub)\r\n        sub = select(df, 'x', '!=', 32)\r\n        print('sub:\\n', sub)\r\n        sub = select(df, ('x', 'z'), '==', (10, 61))\r\n        print('sub:\\n', sub)\r\n        sub = select(df, ['x', 'y'], ['!=', '>='], [40, 41])\r\n        print('sub:\\n', sub)\r\n        sub = select(df, par='x', op='==', val=32)\r\n        print('sub:\\n', sub)\r\n\r\n    if 1:\r\n        print('\\n*** Example: split()')\r\n\r\n        print('*** \\ndf:\\n', df)\r\n        orderedDict = split(df, split='y', sort='x')\r\n        print('orderedDict:\\n', orderedDict)\r\n\r\n    if 1:\r\n        import matplotlib.pyplot as plt\r\n        print('\\n*** Example: df.plot()')\r\n\r\n        df.plot()\r\n        plt.show()\r\n        df.plot(x='x', y='z')\r\n        df['y'].plot()\r\n        plt.plot(df['x'], df['y'], label='y(x)')\r\n        plt.legend()\r\n        plt.show()\r\n\r\n    if 1:\r\n        import os\r\n        from pandas import ExcelWriter\r\n        print('*** Example: ExcelWriter(), read_excel() and to_csv()')\r\n\r\n        file = os.path.join(gettempdir(), 'PythonExport.xlsx')\r\n        writer = ExcelWriter(file)\r\n        df.to_excel(writer, sheet_name='data', startcol=4, startrow=3)\r\n        writer.save()\r\n        df.to_csv(file + '.csv', sep=',')\r\n\r\n        df2 = read_excel(open(file, 'rb'), sheetname='data',\r\n                         # startcol=4, startrow=3\r\n                         )\r\n        print('\\ndf2:\\n', df2)\r\n\r\n        if 0:\r\n            print('*** Example: Show file in Excel')\r\n            import os\r\n            os.system('start ' + file)\r\n\r\n        if 1:\r\n            print('*** Example: Show file in Excel')\r\n            import subprocess\r\n            p = subprocess.Popen(['start', file], shell=True)\r\n            p.wait()\r\n\r\n    if 1:\r\n        from pandas import Panel\r\n        print('\\n*** Example: Panel()')\r\n\r\n        p = Panel({'a': df, 'b': df})\r\n        p['b']['x'][1] = 11111\r\n        p['b']['x'].tun = False\r\n        p['b']['x'].com = True\r\n        del df\r\n        print('-' * 40)\r\n        for label, frame in p.iteritems():\r\n            print('label:', label, 'frame:\\n', frame)\r\n            for key, col in frame.items():\r\n                print('key:', key, 'col:', col.tolist())\r\n        print('-' * 40)\r\n        print('p.iloc1:', p.iloc[1])\r\n        print('p,a:', p['a'])\r\n        print('p,b:', p['b'])\r\n        print('p,b,x:', p.loc['b']['x'])\r\n        print('p,b,x,1:', p.loc['b']['x'][1])\r\n        print(\"p['b']['x'].tun:\", p['b']['x'].tun)\r\n        print(\"p['b']['x'].com:\", p['b']['x'].com)\r\n","sub_path":"coloredlids/data/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":15060,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"648029410","text":"from django.contrib.auth import login \nfrom django.shortcuts import redirect\nfrom core.forms import RANGARIAuthenticationForm\nfrom django.http import HttpResponseRedirect\n\nclass RANGARIAuthLoginMiddleware(object):\n    def process_request(self, request):\n        if request.method == \"POST\":\n            form = RANGARIAuthenticationForm(request,data=request.POST)\n            if form.is_valid():\n                login(request, form.get_user())\n                if request.POST.get('next') is not None and request.POST.get('next') != 'None':\n                    return HttpResponseRedirect(request.POST.get('next'))\n                # if request.GET.get('next') not None:\n                #     return HttpResponseRedirect(request.GET.get('next'))\n                if '/core/logout/' in request.get_full_path():\n                    return HttpResponseRedirect(request.META.get('HTTP_REFERER','/'))\n        else:\n            form = RANGARIAuthenticationForm(request)\n        request.login_form = form","sub_path":"rangari/core/templates/core/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"423037517","text":"''' AIPO Q5- Number of divisors\nSzymon Drapala\n'''\nimport math\n\n\ndef countDivisors(n):\n\n    count = 0\n\n    for d in range(1,n+1):\n        if n % d == 0:\n            \n            count = count + 1\n\n    return count\n\n#end def#\n\n\ndef toExecute():\n\n\n    #read inputs from file\n\n    fin = open('input.txt', 'rt')\n    fout = open('output.txt', 'wt')\n\n    n = int(fin.readline().strip())\n\n    nrs_1 = []\n    for i in range(n):\n\n        elem = int(fin.readline().strip())\n        nrs_1.append(elem)\n    #end for\n    \n\n    for i in range(n):\n        nr = countDivisors(nrs_1[i])\n\n        fout.write(str(nrs_1[i]) +' ' + str(nr)+'\\n')\n    #end for\n\n    fin.close()\n    fout.close()\n#end def\n","sub_path":"Modular Programming/Playground/mpt/Python Labs-20180209T111814Z-001/Python Labs/AIPO Q5/question 5.py","file_name":"question 5.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"442681846","text":"import pymel.core as pm\n\ndef bakeLocatorPos(locators, specifyFrames=False):\n\t'''This function bakes locators to the selected transforms\n\tArgs:\n\t\tlocators (pm.nt.Transform): this is a list of transforms to bake to buffer locators\n\t\tspecifyFrames (tuple): just a tuple of two integers that specify start/end frames e.g. (1004,1010)\n\tUsage:\n\t\tbakeLocatorPos(pm.ls(sl=True, type='transform')) #runs with timeline as you have it\n\t\tbakeLocatorPos(pm.ls(sl=True, type='transform'), specifyFrames=(1004,1010)) #runs with specific\n\t'''\n\t#make a decompose matrix for later\n\tdecompose = pm.createNode('decomposeMatrix')\n\t#Initialize the lists of objects we're going to manipulate\n\tbufferLocators, constraints = [], []\n\t\n\tfor locator in locators:\n\t\t#connect the original locator to the decomposeMatrix to get the world position\n\t\tlocator.worldMatrix[0].connect(decompose.inputMatrix, f=True)\n\t\t#create the buffer locator\n\t\tbufferloc = pm.spaceLocator( n=locator.name() + \"_BUFFERLOC\")\n\t\t\n\t\t#Set all the initial values for the buffer locator\n\t\tbufferloc.translate.set( decompose.outputTranslate.get() )\n\t\tbufferloc.rotate.set( decompose.outputRotate.get() )\n\t\t#bufferloc.scale.set(0,0,0)\n\t\t\n\t\t#add the bufferLocator and the constraints \n\t\tconstraints.append( pm.parentConstraint(locator, bufferloc, mo=True))\n\t\tbufferLocators.append(bufferloc)\n\t#we don't need the decompose matrix anymore\n\tpm.delete(decompose)\n\t#get the start and end frame\n\tstartFrame = pm.playbackOptions(q=True,ast=True)\n\tendFrame = pm.playbackOptions(q=True,aet=True)\n\tframeRange=(startFrame,endFrame)\n\tif specifyFrames:\n\t\tframeRange=specifyFrames\n\t#bake the animation on all the buffer locators for start and end time\n\tpm.bakeResults( bufferLocators, simulation=True, t=frameRange, sampleBy=1, disableImplicitControl=True, preserveOutsideKeys=True,sparseAnimCurveBake=False, removeBakedAttributeFromLayer=False, bakeOnOverrideLayer=False, controlPoints=False, shape=True);\n\tpm.delete(constraints)","sub_path":"maya/_tests_/aw_bakeLocators.py","file_name":"aw_bakeLocators.py","file_ext":"py","file_size_in_byte":1955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"45190793","text":"import json\nfrom decimal import Decimal\nfrom typing import List\n\nfrom django.db.models import QuerySet, F, Case, When, Value, IntegerField\n\nfrom usaspending_api.common.recipient_lookups import combine_recipient_hash_and_level\nfrom usaspending_api.recipient.models import RecipientProfile, RecipientLookup\nfrom usaspending_api.recipient.v2.lookups import SPECIAL_CASES\nfrom usaspending_api.search.v2.views.spending_by_category_views.spending_by_category import (\n    Category,\n    AbstractSpendingByCategoryViewSet,\n)\n\n\nclass RecipientDunsViewSet(AbstractSpendingByCategoryViewSet):\n    \"\"\"\n    This route takes award filters and returns spending by Recipient DUNS.\n    \"\"\"\n\n    endpoint_doc = \"usaspending_api/api_contracts/contracts/v2/search/spending_by_category/recipient_duns.md\"\n\n    category = Category(name=\"recipient_duns\", agg_key=\"recipient_agg_key\")\n\n    @staticmethod\n    def _get_recipient_id(row: list) -> str:\n        \"\"\"\n        In the recipient_profile table there is a 1 to 1 relationship between hashes and DUNS\n        (recipient_unique_id) and the hashes+duns match exactly between recipient_profile and\n        recipient_lookup where there are matches.  Grab the level from recipient_profile by\n        hash if we have one or by DUNS if we have one of those.\n        \"\"\"\n        if \"recipient_hash\" in row:\n            profile_filter = {\"recipient_hash\": row[\"recipient_hash\"]}\n        elif \"recipient_unique_id\" in row:\n            profile_filter = {\"recipient_unique_id\": row[\"recipient_unique_id\"]}\n        else:\n            raise RuntimeError(\n                \"Attempted to lookup recipient profile using a queryset that contains neither \"\n                \"'recipient_hash' nor 'recipient_unique_id'\"\n            )\n\n        profile = (\n            RecipientProfile.objects.filter(**profile_filter)\n            .exclude(recipient_name__in=SPECIAL_CASES)\n            .annotate(\n                sort_order=Case(\n                    When(recipient_level=\"C\", then=Value(0)),\n                    When(recipient_level=\"R\", then=Value(1)),\n                    default=Value(2),\n                    output_field=IntegerField(),\n                )\n            )\n            .values(\"recipient_hash\", \"recipient_level\")\n            .order_by(\"sort_order\")\n            .first()\n        )\n\n        return (\n            combine_recipient_hash_and_level(profile[\"recipient_hash\"], profile[\"recipient_level\"]) if profile else None\n        )\n\n    def build_elasticsearch_result(self, response: dict) -> List[dict]:\n        results = []\n        location_info_buckets = response.get(\"group_by_agg_key\", {}).get(\"buckets\", [])\n        for bucket in location_info_buckets:\n            recipient_info = json.loads(bucket.get(\"key\"))\n\n            results.append(\n                {\n                    \"amount\": int(bucket.get(\"sum_field\", {\"value\": 0})[\"value\"]) / Decimal(\"100\"),\n                    \"recipient_id\": recipient_info[\"hash_with_level\"] or None,\n                    \"name\": recipient_info[\"name\"] or None,\n                    \"code\": recipient_info[\"unique_id\"] or \"DUNS Number not provided\",\n                }\n            )\n\n        return results\n\n    def query_django(self, base_queryset: QuerySet) -> List[dict]:\n        django_filters = {}\n        if self.subawards:\n            django_values = [\"recipient_name\", \"recipient_unique_id\"]\n            annotations = {\"name\": F(\"recipient_name\"), \"code\": F(\"recipient_unique_id\")}\n        else:\n            django_values = [\"recipient_hash\"]\n            annotations = {}\n\n        queryset = self.common_db_query(base_queryset, django_filters, django_values).annotate(**annotations)\n        lower_limit = self.pagination.lower_limit\n        upper_limit = self.pagination.upper_limit\n        query_results = list(queryset[lower_limit:upper_limit])\n\n        for row in query_results:\n            row[\"recipient_id\"] = self._get_recipient_id(row)\n\n            if self.subawards:\n                for key in django_values:\n                    del row[key]\n            if not self.subawards:\n                lookup = (\n                    RecipientLookup.objects.filter(recipient_hash=row[\"recipient_hash\"])\n                    .values(\"legal_business_name\", \"duns\")\n                    .first()\n                )\n\n                # The Recipient Name + DUNS should always be retrievable in RecipientLookup\n                # For odd edge cases or data sync issues, handle gracefully:\n                if lookup is None:\n                    lookup = {}\n\n                row[\"name\"] = lookup.get(\"legal_business_name\")\n                row[\"code\"] = lookup.get(\"duns\") or \"DUNS Number not provided\"\n                del row[\"recipient_hash\"]\n\n        return query_results\n","sub_path":"usaspending_api/search/v2/views/spending_by_category_views/spending_by_recipient_duns.py","file_name":"spending_by_recipient_duns.py","file_ext":"py","file_size_in_byte":4728,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"118403160","text":"class RotatedArraySearch(object):\n    def __init__(self) -> None:\n        \"\"\"Constructor.\n        \"\"\"\n        pass\n\n    def main(self: object, input_list: list, target: int) -> int:\n        \"\"\"Find the index by searching in a rotated sorted array in O(log n) time.\n\n            Parameters\n            ----------\n            input_list : list, required\n                List to search for the number.\n            target : int, required\n                Number to get the index for.\n            \n            Returns\n            ----------\n            int\n                Returns the index of the number if exists otherwise -1.\n        \"\"\"\n\n        if not input_list or len(input_list) == 0:\n            return -1\n        elif len(input_list) == 1 and  input_list[0] == target:\n            return 0\n        else:\n            return self.search(input_list, target, 0, len(input_list)-1)\n\n    def search(\n        self: object,\n        input_list: list,\n        target: int,\n        start_index: int,\n        end_index: int) -> int:\n\n        \"\"\"Search helper method.\n\n            Parameters\n            ----------\n            input_list : list, required\n                List to search for the number.\n            target : int, required\n                Number to get the index for.\n            start_index : int, required\n                Start index within the list.\n            end_index : int, required\n                End index within the list.\n            \n            Returns\n            ----------\n            int\n                Returns the index of the number if exists otherwise -1.\n        \"\"\"\n\n        if start_index > end_index:\n            return -1\n\n        middle_index: int = (start_index + end_index) // 2\n\n        if input_list[middle_index] == target: # middle is target\n            return middle_index\n        elif input_list[start_index] <= input_list[middle_index]: # left array is sorted\n            if target >= input_list[start_index] and target <= input_list[middle_index]:\n                return self.search(input_list, target, start_index, middle_index-1)\n            else:\n                return self.search(input_list, target, middle_index+1, end_index)\n        else: # right array is sorted\n            if target >= input_list[middle_index] and target <= input_list[end_index]:\n                return self.search(input_list, target, middle_index+1, end_index)\n            else:\n                return self.search(input_list, target, start_index, middle_index-1)","sub_path":"solution/rotated_array_search.py","file_name":"rotated_array_search.py","file_ext":"py","file_size_in_byte":2483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"233863329","text":"#!/usr/bin/env python\n\nimport sys\n\nimport setuptools\n\nwith open('README.rst') as ld_file:\n    long_description = ld_file.read()\n\nneeds_sphinx = set(['build_sphinx', 'upload_docs', 'release']).intersection(sys.argv)\nsphinx_req = ['sphinx', 'rst.linker'] if needs_sphinx else []\nneeds_pytest = set(['pytest', 'test']).intersection(sys.argv)\npytest_runner = ['pytest-runner>=2.6'] if needs_pytest else []\n\nsetup_params = dict(\n    name=\"path.py\",\n    use_scm_version=True,\n    description=\"A module wrapper for os.path\",\n    long_description=long_description,\n    author=\"Jason Orendorff\",\n    author_email=\"jason.orendorff@gmail.com\",\n    maintainer=\"Jason R. Coombs\",\n    maintainer_email=\"jaraco@jaraco.com\",\n    url=\"http://github.com/jaraco/path.py\",\n    license=\"MIT License\",\n    py_modules=['path', 'test_path'],\n    classifiers=[\n        'Development Status :: 5 - Production/Stable',\n        'License :: OSI Approved :: MIT License',\n        'Intended Audience :: Developers',\n        'Operating System :: OS Independent',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 2.6',\n        'Programming Language :: Python :: 2.7',\n        'Programming Language :: Python :: 3',\n        'Topic :: Software Development :: Libraries :: Python Modules'\n    ],\n    setup_requires=[\n        'setuptools_scm',\n    ] + sphinx_req + pytest_runner,\n    tests_require=['pytest', 'appdirs'],\n    extras_require={\n        ':python_version==\"2.6\"': ['importlib'],\n    },\n)\n\n\nif __name__ == '__main__':\n    setuptools.setup(**setup_params)\n","sub_path":"python 好用库/lib/path.py-master/path.py-master/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"33970769","text":"import vh\nimport argparse\nimport tarfile\nimport tempfile\nimport os\nimport json\nfrom copy import deepcopy\n\n#\n# def parameter_generator():\n#     for workers in [10, 13]:\n#         for servers in [10, 13]:\n#             cur_model_name = \"torch_speed_linear_workers_{}_servers_{}\".format(workers, servers).replace('.', '_')\n#             yield {\n#                 \"system\": {\n#                     \"num_workers\": workers,\n#                     \"num_embedding_servers\": servers,\n#                     \"num_deep_servers\": 1,\n#                     \"num_downloaders\": 5,\n#                     \"num_parsers\": 3,\n#                     \"max_gradient_queue_size\": 20\n#                 },\n#                 \"model_name\": cur_model_name,\n#                 \"save_model_dir\": \"//home/bs/users/alxmopo3ov/{}\".format(cur_model_name),\n#             }\n#             return\n\n\ndef parameter_generator():\n    yield {}\n\n\ndef create_parser():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--model_module\", type=str, required=True, help=\"Path to the python model module. See examples at deep_mode.py and model.py\")\n    parser.add_argument(\"--model_config\", type=str, required=True, help=\"Path to some file which is passes to python module functions to configure models\")\n    parser.add_argument(\"--additional_files\", type=str, required=False, default=[], nargs='*')\n    return parser\n\n\nCPP_FILES = [\n    \"libtorch_embedding\"\n]\n\n\ndef create_archive(arcname, model, model_config, additional_files):\n    with tarfile.TarFile(arcname, 'w') as arc:\n        for f in CPP_FILES:\n            arc.add(f, recursive=True)\n        for f in os.listdir('torch_ps'):\n            if '__pycache__' not in f:\n                arc.add(os.path.join('torch_ps', f))\n        arc.add('./pytorch_train_process.py')\n        arc.add(model, arcname='model.py')\n        arc.add(model_config, arcname='model_config')\n        for file in additional_files:\n            arc.add(file, recursive=True)\n\n\n# All options for number of workers/servers will be removed\nJOB_COMMAND = \"\"\"bash -c 'export CXX=/usr/bin/g++-7 && export PATH=/anaconda3/bin/:$PATH && tar -xvf ${input[\"input_scripts\"]} && YT_TOKEN=${param[\"token\"]} YT_PROXY=hahn python pytorch_train_process.py --model_module model.py --model_config model_config'\"\"\"\n\n\nif __name__ == '__main__':\n    parser = create_parser()\n    args = parser.parse_args()\n\n    with open(args.model_config, \"rt\") as f:\n        basic_json = json.load(f)\n    for param in parameter_generator():\n        cur_json = deepcopy(basic_json)\n        cur_json.update(param)\n        with tempfile.NamedTemporaryFile(dir='.') as tmp, tempfile.NamedTemporaryFile(dir='.') as tmp_json:\n            with open(tmp_json.name, \"wt\") as f:\n                json.dump(cur_json, f)\n            create_archive(tmp.name, args.model_module, tmp_json.name, args.additional_files)\n            arc = vh.File(tmp.name)\n\n            with vh.Graph() as graph:\n                train_torch = vh.op(id=\"eb5a6711-487a-428f-9e4b-d2b89a5824f2\")\n                train_torch(input_scripts=arc,\n                            job_command=JOB_COMMAND,\n                            max_ram=70000,\n                            max_disk=20000,\n                            cpu_guarantee=3100,\n                            ttl=3600,\n                            token=\"alxmopo3ov_yt_token\")\n\n            vh.run(graph, always_make=True, quota='ads', project='pytorch_experiments', label=cur_json[\"model_name\"])\n","sub_path":"run_nirvana.py","file_name":"run_nirvana.py","file_ext":"py","file_size_in_byte":3453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"76756476","text":"#Python program converted from block2_StimCorrected.mat Ver.1\r\n#2016/04/11 Ito (Fujitsu Ltd.)\r\n\r\nimport sys\r\n# sys.path.append('/Library/Python/2.7/site-packages/nwb')\r\nsys.path.append('/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/nwb')\r\nsys.path.append('/System/Library/Frameworks/Python.framework/Versions/2.7/Extras/lib/python')\r\nimport numpy\r\n# import h5py\r\nimport scipy.io\r\nimport nwb_file\r\n# import utils\r\nimport nwb_utils\r\n\r\n########################################################################\r\n# Parameters\r\n\r\n# open and save files\r\nopen_mat_data  = '/Users/takayuki/VirtualBox_Share/R375/20151218/block2/block2_mat/block2_Spikes.mat'\r\nopen_lfp_mat   = '/Users/takayuki/VirtualBox_Share/R375/20151218/block2/block2_mat/block2_LFPs.mat'\r\nopen_raw_file_body  = '/Users/takayuki/VirtualBox_Share/R375/20151218/block2/block2_mat/block2_Raw_ch'\r\nsave_file_name = '/Users/takayuki/VirtualBox_Share/R375/20151218/block2/block2_mat_2ch/block2.h5'\r\n\r\n\r\n# General notes\r\n# each file should have a descriptive globally unique identifier that specifies the lab and this experiment session\r\nIdentifier   = 'ins20151218b02'\r\n# provide one or two sentences that describe the experiment and what data is in the file\r\nGeneral_description = 'TE recording with Go890'\r\n\r\nExperimenter = 'AS'\r\nInstitution  = 'RIKEN Brain Science Institute'\r\nLab          = 'Lab. for Integrative Neural Systems'\r\n\r\nSpecies      = 'Macaca fuscata'\r\nSex          = 'm'  # m or f\r\nSubject_id   = '375M'\r\nWeight       = '9.2kg'\r\nGenotype     = 'WT' # WT: wild type\r\nAge          = 'unknown'    # not age but Date of birth (Format: 2009-01-13)\r\nDescription  = 'NBR'   # breeder or origin\r\n\r\n\r\n# Electrodes\r\nbroken_ch    = [8, 10, 41, 48, 50, 55, 63]\r\nnum_shank   = 8\r\nImpedance    = ['xMOhm']*64\r\nFiltering_spike = 'xHz - yHz'\r\nFiltering_LFP   = 'xxHz - yyHz'\r\nRecording_device = 'TDT'\r\nElectrode_name = 'NeuroNexus 8x8 #12345'\r\nLocation     = 'Right area TE anterior dorsal'\r\nElectrode_map = [[0,0,0]]*64    # unit: meter\r\n# Electrode_map = [[0, 0, 1.0e-4*(150*0)], [0, 0, 1.0e-4*(150*1)], [0, 0, 1.0e-4*(150*2)]]\r\nElectrode_group = ['shank1']*8 + ['shank2']*8 + ['shank3']*8 + ['shank4']*8 + ['shank5']*8 + ['shank6']*8 + ['shank7']*8 + ['shank8']*8\r\nSpike_detection_method = 'detection threshold was set at -3.5 times of the standard deviation among 10 seconds just befor recording'\r\ncomment_LFP = 'local field potential'\r\ncomment_Raw = 'raw wave form for spike analysis'\r\n\r\n\r\n# Stimulus\r\nStim_set_name   = 'Go890'\r\nNum_stim        = 890\r\nNum_block       = 12\r\nrefresh_rate    = 85    # [Hz]\r\nframe_pre       = 9\r\nframe_on        = 17\r\nscreen_size     = [800, 600] # [pixel]\r\nscreen_distance = 57    # [cm]\r\nstim_move       = '0.1 degree 1cycle/sec'   # text\r\n# link_img_h5    = ['/Users/takayuki/Dropbox/DataBaseFolder/old_files/convert/Go890.h5', '/images']\r\ncomment_stim    = 'comment stim'\r\n\r\n########################################################################\r\n# Start main program\r\n\r\n# Load mat data using scipy.io\r\nprint(\"Load spike mat file '%s'\" % (open_mat_data))\r\ndt = scipy.io.loadmat(open_mat_data)\r\n\r\nTS_all = dt['TS']\r\nstim_on_time = dt['stim_on_time']\r\nstim_id = dt['stim_id']\r\nrec_prm = dt[\"rec_prm\"]\r\nstart_time = str(rec_prm[0,0]['rec_start'][0])\r\n# frames = rec_prm[0,0]['frames']\r\nrec_ch = rec_prm[0,0]['analyzed_ch']\r\nnum_ch = rec_ch.size\r\nsampling_rate_Spike = dt['sampling_rate_Spike']\r\nsampling_rate_Spike = sampling_rate_Spike.tolist()[0]\r\nsampling_rate_Spike = sampling_rate_Spike[0]\r\n\r\n\r\n########################################################################\r\n# create a new NWB file\r\n# several settings are specified when doing so. these can be supplied within the NWB constructor or defined in a dict, as in in this example\r\n\r\nsettings = {}\r\nsettings[\"file_name\"] = save_file_name\r\n# the function nwb_utils.create_identifier() is recommended to use as it takes the string and appends the present date and time\r\nsettings[\"identifier\"] = nwb_utils.create_identifier(Identifier)\r\nsettings[\"description\"] = General_description\r\n# indicate that it's OK to overwrite exting file\r\nsettings[\"mode\"] = \"w\"\r\n# specify the start time of the experiment. all times in the NWB file are relative to experiment start time\r\n# if the start time is not specified the present time will be used\r\nsettings[\"start_time\"] = start_time\r\n# create the NWB file object. this manages the file\r\nprint(\"Creating \" + settings[\"file_name\"])\r\nf = nwb_file.open(**settings)\r\n\r\n\r\n########################################################################\r\n# set parameters in general\r\n\r\nf.set_dataset('session_id', Identifier)\r\nf.set_dataset('experimenter', Experimenter)\r\nf.set_dataset('institution', Institution)\r\nf.set_dataset('lab', Lab)\r\nf.set_dataset('experiment_description', General_description)\r\n# f.set_dataset('devices', Recording_device)\r\n\r\n# set parameters in subject in general\r\ns = f.make_group('subject')\r\ns.set_dataset('species', Species)\r\ns.set_dataset('sex', Sex)\r\ns.set_dataset('subject_id', Subject_id)\r\ns.set_dataset('weight', Weight)\r\ns.set_dataset('genotype', Genotype)\r\ns.set_dataset('age', Age)\r\ns.set_dataset('description', Description)\r\n\r\n########################################################################\r\n# set parameters in extracellular_ephys\r\n\r\nex_g = f.make_group('extracellular_ephys')\r\nfor i in range(num_shank):\r\n    shank_g = ex_g.make_group('<electrode_group_X>', 'shank%d' % (i+1))\r\n    shank_g.set_dataset('description', Electrode_name)\r\n    shank_g.set_dataset('device', Recording_device)\r\n    shank_g.set_dataset('location', 'TE')\r\nex_g.set_dataset('electrode_map', Electrode_map)\r\nex_g.set_dataset('electrode_group', Electrode_group)\r\nex_g.set_dataset('impedance', Impedance)\r\nex_g.set_dataset('filtering', Filtering_spike)\r\nex_g.set_custom_dataset('broken_ch', broken_ch)\r\n\r\n\r\n########################################################################\r\n# set parameters in presentation in stimulus\r\n\r\ng3 = f.make_group('<TimeSeries>', 'VisualStimulus', path='/stimulus/presentation')\r\ng3.set_attr('source', 'stimulus images')\r\ng3.set_attr('comments', comment_stim)\r\ng3.set_attr('description', stim_move)\r\n\r\ng3.set_dataset('timestamps', stim_on_time[0])\r\ng3.set_dataset('num_samples',stim_on_time.shape[1])\r\ng3.set_dataset('data', stim_id[0], attrs={'unit': 'StimID'})\r\n\r\ng3.set_custom_dataset('Refresh_rate', refresh_rate, attrs={'unit': 'Hz'})\r\ng3.set_custom_dataset('Screen_size', screen_size, attrs={'unit': 'pixel'})\r\ng3.set_custom_dataset('Screen_distance', screen_distance, attrs={'unit': 'cm'})\r\n# g3.set_custom_dataset('Stim_move', stim_move)\r\ng3.set_custom_dataset('Num_block', Num_block)\r\ng3.set_custom_dataset('Frame_pre', frame_pre)\r\ng3.set_custom_dataset('Frame_on',  frame_on)\r\n# Stimulus external link\r\n# print(\"Link external image h5 file '%s' '%s'\" % (link_img_h5[0],link_img_h5[1]))\r\n# h = f.make_group('<TimeSeries>','stimuli', path='/stimulus/templates', link='extlink:%s,%s'%(link_img_h5[0],link_img_h5[1]))\r\n\r\ng3.set_custom_dataset('Stim_set_name',Stim_set_name, attrs={'Num_stim': Num_stim})\r\n\r\n########################################################################\r\n# LFP\r\n\r\nprint(\"Load LFP mat file '%s'\" % open_lfp_mat)\r\nlfps = scipy.io.loadmat(open_lfp_mat)\r\nsampling_rate_LFP = lfps['sampling_rate_LFP']\r\nsampling_rate_LFP = sampling_rate_LFP.tolist()[0]\r\nsampling_rate_LFP = sampling_rate_LFP[0]\r\nlfps = lfps['LFPs']\r\n\r\nlfps_g  = f.make_group(\"<ElectricalSeries>\", 'LFPs', path=\"/acquisition/timeseries\")\r\nlfps_g.set_attr('source', 'LFP')\r\nlfps_g.set_attr('comments', comment_LFP)\r\nlfps_g.set_attr('description', Filtering_LFP)\r\n\r\nlfps_g.set_dataset('data', lfps, compress=True, attrs={'conversion': 1.0e-6})\r\nlfps_g.set_dataset('num_samples', lfps.shape[0])\r\nlfps_g.set_dataset('starting_time', 0.0, attrs={\"rate\": sampling_rate_LFP})\r\nlfps_g.set_dataset('electrode_idx', range(num_ch))\r\n\r\n\r\n########################################################################\r\n# Raw wave form\r\n\r\nfor kk in range(num_ch):\r\n    group_name = 'Raw_ch%02d' % (kk+1)\r\n    raw_g = f.make_group(\"<ElectricalSeries>\", group_name, path=\"/acquisition/timeseries\")\r\n    raw_g.set_attr('source', 'filtered wave form data')\r\n    raw_g.set_attr('comments', comment_Raw)\r\n    raw_g.set_attr('description', Filtering_spike)\r\n\r\n    open_raw_mat = open_raw_file_body + '%02d.mat' % (kk+1)\r\n    print(\"Load RawWave mat file '%s'\" % open_raw_mat)\r\n    raw_wave = scipy.io.loadmat(open_raw_mat)\r\n    sampling_rate_Raw = raw_wave['sampling_rate_Raw']\r\n    sampling_rate_Raw = sampling_rate_Raw.tolist()[0]\r\n    sampling_rate_Raw = sampling_rate_Raw[0]\r\n    raw_wave = raw_wave['Wave'].squeeze()\r\n\r\n    raw_g.set_dataset('data', raw_wave, compress=True, attrs={'conversion': 1.0e-6})\r\n    raw_g.set_dataset('num_samples', raw_wave.shape[0])\r\n    raw_g.set_dataset('starting_time', 0.0, attrs={\"rate\": sampling_rate_Raw})\r\n    raw_g.set_dataset('electrode_idx', range(num_ch))\r\n\r\n\r\n########################################################################\r\n# Timestamp MUA data in processing\r\n\r\nfor k in range(num_ch):\r\n    TS = TS_all[0][k]\r\n    TS = TS[0]\r\n\r\n    source_idx = TS * sampling_rate_Spike\r\n    source_idx = source_idx.round()\r\n    source_idx = source_idx.astype(numpy.int32)\r\n    target_path = '/acquisition/timeseries/Raw_ch%02d' % (k+1)\r\n\r\n    m = f.make_group('<Module>', 'MUA_ch%02d' % (k+1))\r\n    ch_g = m.make_group('EventDetection', attrs={'source': target_path})\r\n\r\n    ch_g.set_dataset('times', TS, compress=True)\r\n    ch_g.set_dataset('detection_method', Spike_detection_method)\r\n    ch_g.set_dataset('source_idx', source_idx)\r\n    ch_g.set_dataset('source_electricalseries/', 'link:'+target_path)\r\n\r\n\r\nf.close()\r\n","sub_path":"Convert.py","file_name":"Convert.py","file_ext":"py","file_size_in_byte":9686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"534816415","text":"import argparse\nimport json\nimport os\nimport torch\nimport torch.nn as nn\nimport numpy as np\nfrom PIL import Image\n\nfrom torch.utils.data import DataLoader\nfrom unet_model import UNet\nfrom decouple_res_model import DecoupleResnet \nfrom decouple_style_model import DecoupleStyleResnet \nfrom data_utils import autoTestSetRaw2jpgD \nfrom shutil import copyfile\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"6\"\n\ndef load_checkpoint(checkpoint_path, model):\n    assert os.path.isfile(checkpoint_path)\n    checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')\n    iteration = checkpoint_dict['iteration']\n    model_for_loading = checkpoint_dict['model']\n    model.load_state_dict(model_for_loading.state_dict())\n    print(\"Loaded checkpoint '{}' (iteration {})\" .format(\n          checkpoint_path, iteration))\n    return model, iteration\n\ndef save_training_images(input_img, output_img, output_gt, image_path):\n    print(\"Saving output images\")\n    b,c,h,w = input_img.shape\n    output_img[output_img > 1] = 1 \n    output_img[output_img < 0] = 0 \n    tmp_batch1 = input_img[0,:,:,:]\n    tmp_batch2 = output_img[0,:,:,:]\n    tmp_batch3 = output_gt[0,:,:,:]\n    tmp_batch4 = tmp_batch3 - tmp_batch2 + 0.5\n    #Save also the image diff\n    for i in range(b-1):\n        tmp_batch1 = np.concatenate((tmp_batch1, input_img[i+1,:,:,:]), axis=1)\n        tmp_batch2 = np.concatenate((tmp_batch2, output_img[i+1,:,:,:]), axis=1)\n        tmp_batch3 = np.concatenate((tmp_batch3, output_gt[i+1,:,:,:]), axis=1)\n        tmp_batch4 = np.concatenate((tmp_batch4, output_gt[i+1,:,:,:]-output_img[i+1,:,:,:]+0.5), axis=1)\n    new_img_array = np.concatenate((tmp_batch1,tmp_batch2,tmp_batch3,tmp_batch4), axis=2) * 255\n    new_img = Image.fromarray(np.transpose(new_img_array, [1,2,0]).astype('uint8'), 'RGB')\n    new_img.save(image_path)\n\ndef test(output_directory, epochs, batch_size, data_type, net_type, seed, checkpoint_path):\n    # set manual seed\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed(seed)\n\n    # build model\n    if net_type == \"u_net\":\n        model = UNet(**network_config).cuda()\n    elif net_type == \"decouple_net\":\n        model = DecoupleResnet(**network_config).cuda()\n    elif net_type == \"decouple_style_net\":\n        model = DecoupleStyleResnet(**network_config).cuda()\n    else:\n        print(\"unsupported network type\")\n        return 0\n\n    # Load checkpoint if one exists\n    iteration = 0\n    if checkpoint_path != \"\":\n        model, iteration = load_checkpoint(checkpoint_path, model)\n        iteration += 1\n\n    # build dataset\n    if data_type == \"raw2jpgD\":\n        trainset = autoTestSetRaw2jpgD(**data_config)\n    else:\n        print(\"wrong data converting type\")\n        return 0\n    epoch_size = len(trainset)\n    train_loader = DataLoader(trainset, num_workers=1, shuffle=False,\n                              sampler=None,\n                              batch_size=batch_size,\n                              pin_memory=False,\n                              drop_last=True)\n\n    # Get shared output_directory ready\n    if not os.path.isdir(output_directory):\n        os.makedirs(output_directory)\n        # copy a config file to the output directory for checking\n        copyfile(config_path, output_directory+\"/\"+config_path)\n        os.chmod(output_directory, 0o775)\n    print(\"output directory\", output_directory)\n\n    model.eval()\n    epoch_offset = 0\n\n    # ================ MAIN TRAINNIG LOOP! ===================\n    for epoch in range(epoch_offset, epochs):\n        print(\"Epoch: {}\".format(epoch))\n        for i, batch in enumerate(train_loader):\n            model.zero_grad()\n\n            if data_type == \"jpg2jpg\" or data_type == \"raw2jpgS\":\n                input_img, output_gt = batch\n            elif data_type == \"raw2jpg\":\n                input_img, output_gt, input_gt = batch\n            elif data_type == \"raw2jpgD\":\n                params, input_img, output_gt, input_gt = batch\n                params = torch.autograd.Variable(params.cuda())\n            input_img  = torch.autograd.Variable(input_img.cuda())\n            output_gt  = torch.autograd.Variable(output_gt.cuda())\n            if net_type == \"u_net\":\n                outputs = model(input_img)\n            elif net_type == \"decouple_net\" or net_type == \"decouple_style_net\":\n                outputs = model([params, input_img])\n            # save testing images\n            image_path = \"{}/img_{}_{}.jpg\".format(output_directory, epoch, i)\n            if data_type == \"jpg2jpg\":\n                save_training_images(input_img.cpu().data.numpy()[:,:3,:,:], outputs.cpu().data.numpy()\n                                    ,output_gt.cpu().data.numpy(), image_path)\n            elif data_type == \"raw2jpg\" or data_type == \"raw2jpgD\":\n                save_training_images(input_gt.cpu().data.numpy(), outputs.cpu().data.numpy()\n                                    ,output_gt.cpu().data.numpy(), image_path)\n            if data_type == \"raw2jpgS\":\n                save_training_images(output_gt.cpu().data.numpy(), outputs.cpu().data.numpy()\n                                    ,output_gt.cpu().data.numpy(), image_path)\n            \n\n            iteration += 1\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-c', '--config', type=str,\n                        help='JSON file for configuration')\n    args = parser.parse_args()\n    global config_path \n    config_path = args.config\n\n    with open(config_path) as f:\n        data = f.read()\n    config = json.loads(data)\n    test_config = config[\"test_config\"]\n    global data_config\n    data_config = config[\"data_config\"]\n    global network_config\n    network_config = config[\"network_config\"]\n\n    torch.backends.cudnn.enabled = True\n    torch.backends.cudnn.benchmark = False\n    test(**test_config)\n","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":5796,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"354479147","text":"from core.etl_utils import ETL_Base, Commandliner\n\n\nclass Job(ETL_Base):\n    \"\"\"To run/deploy sql jobs, using --sql_file arg.\"\"\"\n\n    def set_job_file(self):\n        job_file=self.args['sql_file']\n        # logger.info(\"job_file: '{}'\".format(job_file))\n        return job_file\n\n    def transform(self, **ignored):\n        sql = self.read_sql_file(self.args['sql_file'])\n        df = self.query(sql)\n        return df\n\n    @staticmethod\n    def read_sql_file(fname):\n        fh = open(fname, 'r')\n        sql = fh.read()\n        fh.close()\n        return sql\n\n\nclass SQLCommandliner(Commandliner):\n    @staticmethod\n    def define_commandline_args():\n        parser = Commandliner.define_commandline_args()\n        parser.add_argument(\"-q\", \"--sql_file\", help=\"path of sql file to run\")\n        return parser\n\n\nif __name__ == \"__main__\":\n    SQLCommandliner(Job)\n","sub_path":"core/sql_job.py","file_name":"sql_job.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"107591350","text":"########## oadm policy add-cluster-role-to-user view david --config=/etc/origin/master/admin.kubeconfig\n\nimport json\nimport sys\nimport requests\n\n#PODS_URL = \"https://test01.easycloud.cz:8443/api/v1/pods\"\nURL = \"https://test01.easycloud.cz:8443/api/v1/namespaces\"\n#TOKEN = \"M4i2JJBR2jGj1iDYucjK5Ne7xPIHls4yHjuj3Kk581c\"\nTOKEN = \"tLWotYauQHcDccmiKYTQ4Vs9-3E49JYNW1jCOKI0Tik\"\n\ndef filter_event(event):\n    if event['status'] in ['Terminating', 'DELETE']:\n        return pod\n\n\ndef get_resource_version(obj):\n    if 'object' in obj:\n        obj = obj['object']\n    return obj['metadata']['resourceVersion']\n\ntry:\n    while True:\n        try:\n            url = URL\n            url += '?watch=true'\n            url += '&access_token=' + TOKEN\n            #print('url>>', url)\n            response = requests.get(url, verify=False, stream=True)\n\n            for line in response.iter_lines():\n                data = line.decode()\n                obj = json.loads(data).get('object')\n\n                if obj.get('kind') == 'Namespace' and obj['status']['phase'] == 'Active':\n                    print('--- Namespace %s: %s' % (obj['metadata']['name'],\n                                                obj['status']['phase']))\n\n                try:\n                    if obj['spec']['finalizers'][0] == 'openshift.io/origin' and obj['status']['phase'] == 'Terminating':\n                        print('--- Whatever %s: %s' % (obj['metadata']['name'],\n                                                    obj['status']['phase']))\n                except:\n                    continue\n\n                #if obj['spec']['finalizers'][0] == '':\n                #    print('--- Namespace %s: %s' % (obj['metadata']['name'],\n                #                                obj['status']['phase']))\n\n                #event = filter_event(obj)\n\n                #if pod_event:\n                #    print('----> Terminating')\n\n        except KeyError as ex:\n            print('ex>>', ex)\n            print(json.dumps(obj, indent=3))\n            import traceback\n            traceback.print_exc()\n            sys.exit(1)\n\nexcept KeyboardInterrupt:\n    print('...exit')\n    sys.exit(0)\n","sub_path":"roles/finalize/files/etcd_watchdog.py","file_name":"etcd_watchdog.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"197509028","text":"from game.Game import Game\nfrom game.scene.Manager import Manager\n\nclass Player:\n    def __init__(self, name):\n\n        # References\n        game = Game()\n\n        # Player\n        self.name = name\n        self.xp = 0\n\n        # Inventory\n        self.gold = 1000\n        self.inventory = []\n\n        # Stats\n        self.life = 100\n        self.strength = 0\n        self.mana = 0\n\n        # Multipliers\n        self.movement = 0\n        self.turns = 0\n\n        # Location\n        self.x = 0\n        self.y = 0\n\n        # Scene Manager\n        self.scene = Manager()\n\n    def move(self, turn):\n\n        # Suggested new coordinates\n        cx = self.x\n        cy = self.y\n\n        if turn == \"w\":\n            cy += 1\n        elif turn == \"s\":\n            cy -= 1\n        elif turn == \"a\":\n            cx -= 1\n        elif turn == \"d\":\n            cx += 1\n        else:\n            self.scene.print(\"Unknown action '\" + turn + \"', please try again.\")\n\n        # Check if it's already occupied\n\n        # Move player to coordinate\n        #self.x = cx\n        #self.y = cy\n\n    def increaseXp(self, amount):\n        self.xp += amount\n        self.scene.print(\"\\n⭐ XP Level up!\\n Great job! You increased your Experience Points (XP) with +\" + str(amount) + \". Total: \" + str(self.xp) + \"\\n\")\n\n    def decreaseXp(self, amount):\n        self.xp -= amount\n\n    def getStatsInText(self):\n        return \"\" \\\n        \"⭐ XP: \\t\\t\" + str(self.xp) + \"\\n\" + \\\n        \"💰 Gold: \\t\" + str(self.gold) + \"\\n\" + \\\n        \"💓 Life: \\t\" + str(self.life) + \" hp\" + \"\\n\" + \\\n        \"💪 Strength: \\t\" + str(self.strength) + \"\\n\" + \\\n        \"💙 Mana: \\t\" + str(self.mana)\n\n    def getDebugInText(self):\n        return \"\"\n","sub_path":"player/Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":1713,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"511059916","text":"import os, sys, copy\nimport numpy as np\nimport yaml\nfrom warnings import warn\nfrom collections import OrderedDict, namedtuple, Counter\n\n# Set default residue charges for amino acids (AMBER force field)\namberAminoAcidUnits = ['ALA', 'ARG', 'ASN', 'ASP', 'ASH', # ASH: ASP protonated\n                  'CYS', 'CYM', 'CYX' , # CYM: deprotonated, CYX: S-S crosslinking\n                  'GLU', 'GLH', 'GLN', 'GLY',\n                  'HID', 'HIE', 'HIP', # HIP: protonated\n                  'ILE', 'LEU', 'LYS', 'LYN', # LYN: neutral\n                  'MET', 'PHE', 'PRO', 'SER', 'THR', 'TRP', 'TYR', 'VAL']\nresidueCharge = []\nfor residue in amberAminoAcidUnits:\n    if residue == 'ARG' or residue == 'LYS' or residue == 'HIP':\n        residueCharge.append(1)\n    elif residue == 'ASP' or residue == 'GLU' or residue == 'CYM':\n        residueCharge.append(-1)\n    else:\n        residueCharge.append(0)\namberResidueChargeDict = OrderedDict()\nfor idx, i in enumerate(amberAminoAcidUnits):\n    amberResidueChargeDict[i] = residueCharge[idx]\n\nclass Input:\n    def __init__(self, inputFile=None):\n        if inputFile is None:\n            self.cheminformatics = None\n            self.set_charge = {}\n            self.resChargeDict = {}\n            self.charge_equal = {}\n            self.nmols = []\n            self.restraintinfo = {}\n            self.gridinfo = None\n            self.gridspace = 0.7\n            self.normalization = False\n            self.symmetry  = True\n        else:\n            self.readinp(inputFile)\n\n    def readinp(self, inputFile):\n        inp = yaml.load(open(inputFile))\n        # Read cheminformatics\n        if 'cheminformatics' in inp:\n            if inp['cheminformatics'] == 'openeye' or inp['cheminformatics'] == 'rdkit' or inp['cheminformatics'] == 'None':\n                cheminformatics = inp['cheminformatics']\n            else:\n                raise NotImplementedError(\"%s is not implemented. Please choose openeye, rdkit or None:) \" % inp['cheminformatics'])\n        else:\n            cheminformatics = None\n\n        # Read set_charge for charge freezing\n        if 'set_charge' in inp:\n            set_charge= inp['set_charge']\n        else:\n            set_charge = {}\n\n        # From net_set, define resChargeDictself.\n        # for small molecule, resname is mol1, mol2, ... and atomname = elem\n        resChargeDict = copy.deepcopy(amberResidueChargeDict)\n        if 'net_set' in inp:\n            net_set = inp['net_set']\n            resChargeDict.update(net_set)\n        if 'charges' in inp:\n            resChargeDict.update(inp['charges'])\n\n        # atomnames and resnames whose charges are set to be equal\n        if 'charge_equal' in inp:\n            charge_equal_inp = inp['charge_equal']\n        else:\n            charge_equal_inp = {}\n\n        newcharge_equal = []\n        for i in charge_equal_inp:\n            atomname = charge_equal_inp[i]['atomname']\n            resname = charge_equal_inp[i]['resname']\n            newcharge_equal.append([atomname, resname])\n        charge_equal = newcharge_equal\n\n        nmols = []\n        for mol in inp['molecules']:\n            nmols.append(int(inp['molecules'][mol]))\n\n        restraintinfo = inp['restraint']\n\n        gridinfo = {}\n        if 'boundary_select' in inp:\n            gridinfo['boundary_select'] = inp['boundary_select']\n\n        if 'grid_space' in inp:\n            space = inp['grid_space']\n        else:\n            space = 0.7\n\n        if 'normalization' in inp:\n            normalization = inp['normalization']\n        else:\n            normalization = False\n\n        if 'symmetry' in inp:\n            symmetry = inp['symmetry']\n        else:\n            symmetry = True\n\n        self.cheminformatics = cheminformatics\n        self.set_charge      = set_charge\n        self.resChargeDict   = resChargeDict\n        self.charge_equal    = charge_equal\n        self.nmols           = nmols\n        self.restraintinfo   = restraintinfo\n        self.gridinfo        = gridinfo\n        self.gridspace       = space\n        self.normalization   = normalization\n        self.symmetry        = symmetry\ndef main():\n    inp = Input()\n    inp.readinp(sys.argv[1])\n    print(inp.normalization)\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"respyte/readinp_resp.py","file_name":"readinp_resp.py","file_ext":"py","file_size_in_byte":4229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"158490366","text":"##############################################################################\n#\n# Copyright (c) 2001, 2002 Zope Corporation and Contributors.\n# All Rights Reserved.\n#\n# This software is subject to the provisions of the Zope Public License,\n# Version 2.1 (ZPL).  A copy of the ZPL should accompany this distribution.\n# THIS SOFTWARE IS PROVIDED \"AS IS\" AND ANY AND ALL EXPRESS OR IMPLIED\n# WARRANTIES ARE DISCLAIMED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED\n# WARRANTIES OF TITLE, MERCHANTABILITY, AGAINST INFRINGEMENT, AND FITNESS\n# FOR A PARTICULAR PURPOSE.\n#\n##############################################################################\n\"\"\"Management view component for principal-role management (Zope2's\n'local roles').\n\n$Id$\n\"\"\"\nfrom datetime import datetime\n\nfrom zope.app import zapi\nfrom zope.app.i18n import ZopeMessageIDFactory as _\nfrom zope.app.security.settings import Unset, Deny, Allow\nfrom zope.app.servicenames import Authentication\n\nfrom zope.app.securitypolicy.interfaces import IRole\nfrom zope.app.securitypolicy.interfaces import IPrincipalRoleManager\nfrom zope.app.securitypolicy.interfaces import IPrincipalRoleMap\n\nclass PrincipalRoleView(object):\n\n    def getAllPrincipals(self):\n        principals = getattr(self, '_principals', None)\n        if principals is None:\n            auth = zapi.getService(Authentication)\n            principals = self._principals = auth.getPrincipals('')\n        return principals\n\n    def getAllRoles(self):\n        roles = getattr(self, '_roles', None)\n        if roles is None:\n            roles = self._roles = \\\n              [role\n               for name, role in zapi.getUtilitiesFor(IRole)]\n        return roles\n\n    def createGrid(self, principals=None, roles=None):\n        if principals is None:\n            principals = self.request.get('principals')\n            if principals is None:\n                principals = self.getAllPrincipals()\n            else:\n                # Ugh, we have ids, but we want objects\n                auth_service = zapi.getService(Authentication)\n                principals = [auth_service.getPrincipal(principal)\n                              for principal in principals]\n\n\n        if roles is None:\n            roles = self.request.get('roles')\n            if roles is None:\n                roles = self.getAllRoles()\n            else:\n                # XXX This code path needs a test\n                utils = zapi.getUtilitiesFor(IRole)\n                roles = [role for name, role in utils if name in roles]\n\n        return PrincipalRoleGrid(principals, roles, self.context)\n\n    def update(self, testing=None):\n        status = ''\n\n        if 'APPLY' in self.request:\n            principals = self.request.get('principals')\n            roles = self.request.get('roles')\n            prm = IPrincipalRoleManager(self.context)\n            for role in roles:\n                for principal in principals:\n                    name = 'grid.%s.%s' % (role, principal)\n                    setting = self.request.get(name, 'Unset')\n                    if setting == 'Unset':\n                        prm.unsetRoleForPrincipal(role, principal)\n                    elif setting == 'Allow':\n                        prm.assignRoleToPrincipal(role, principal)\n                    elif setting == 'Deny':\n                        prm.removeRoleFromPrincipal(role, principal)\n                    else:\n                        raise ValueError(\"Incorrect setting %s\" % setting)\n\n            formatter = self.request.locale.dates.getFormatter(\n                'dateTime', 'medium')\n            status = _(\"Settings changed at ${date_time}\")\n            status.mapping = {'date_time': formatter.format(datetime.utcnow())}\n\n        return status\n\nclass PrincipalRoleGrid(object):\n\n    def __init__(self, principals, roles, context):\n        self._principals = principals\n        self._roles = roles\n        self._grid = {}\n\n        map = IPrincipalRoleMap(context)\n\n        for role in roles:\n            rid = role.id\n            for principal in principals:\n                pid = principal.id\n                setting = map.getSetting(rid, pid)\n                self._grid[(pid, rid)] = setting.getName()\n\n    def principals(self):\n        return self._principals\n\n    def principalIds(self):\n        return [p.id for p in self._principals]\n\n    def roles(self):\n        return self._roles\n\n    def roleIds(self):\n        return [r.id for r in self._roles]\n\n    def getValue(self, principal_id, role_id):\n        return self._grid[(principal_id, role_id)]\n\n    def listAvailableValues(self):\n        return (Unset.getName(), Allow.getName(), Deny.getName())\n","sub_path":"Zope3/tags/ZopeX3-3.0.0-Zope-2.8-a2/src/zope/app/securitypolicy/browser/principalroleview.py","file_name":"principalroleview.py","file_ext":"py","file_size_in_byte":4639,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"542910500","text":"import unittest\n\nfrom nmigen.build import Clock\n\nfrom cores.hdmi.hdmi import TimingGenerator, Hdmi, HdmiClocking\nfrom soc.platforms.zynq import ZynqSocPlatform\nfrom cores.axi.sim_util import axil_read, axil_write\nfrom util.bundle import Bundle, Signal\nfrom util.sim import SimPlatform\nfrom cores.hdmi.parse_modeline import parse_modeline\nfrom cores.hdmi.cvt_python import generate_modeline\nfrom util.nmigen_misc import get_signals\n\n\nclass TestHdmi(unittest.TestCase):\n    def test_timing_generator(self):\n        platform = SimPlatform()\n        dut = TimingGenerator(parse_modeline(generate_modeline(640, 480, 60)))\n\n        def testbench():\n            last_x = 0\n            for i in range(800 - 1):\n                yield\n                this_x = (yield dut.x)\n                assert this_x == last_x + 1, \"x increment failed\"\n                last_x = this_x\n            yield\n            assert 1 == (yield dut.y), \"y increment failed\"\n\n        platform.add_sim_clock(\"sync\", 100e6)\n        platform.sim(dut, testbench, traces=get_signals(dut))\n\n    def test_hdmi_registers(self, testdata=0x1):\n        platform = ZynqSocPlatform(SimPlatform())\n\n        class Pins(Bundle):\n            data_r = Signal()\n            data_g = Signal()\n            data_b = Signal()\n            clock = Signal()\n\n        dut = Hdmi(Pins(), generate_clocks=False, modeline=generate_modeline(640, 480, 60))\n\n        platform.add_sim_clock(\"pix\", 117.5e6)\n\n        def testbench():\n            axi = platform.axi_lite_master\n            memorymap = platform.memorymap\n            for name, addr in memorymap.flatten().items():\n                print(name, addr)\n                yield from axil_read(axi, addr.address)\n                yield from axil_write(axi, addr.address, testdata)\n                # self.assertEqual(testdata, (yield from axil_read(axi, addr.address)))\n\n        platform.sim(dut, (testbench, \"axi_lite\"))\n\n    def test_mmcm_calculation(self):\n        clocking = HdmiClocking(Clock(79.75e6))\n        clocking.find_valid_config()\n","sub_path":"src/cores/hdmi/hdmi_test.py","file_name":"hdmi_test.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"354801777","text":"import os\n\nimport numpy as np\nimport pytest\n\nfrom pennylane import qchem\n\nfrom openfermion import QubitOperator\n\nterms_h20_jw_full = {\n    (): (7 + 0j),\n    ((0, \"Z\"),): (-0.5 + 0j),\n    ((1, \"Z\"),): (-0.5 + 0j),\n    ((2, \"Z\"),): (-0.5 + 0j),\n    ((3, \"Z\"),): (-0.5 + 0j),\n    ((4, \"Z\"),): (-0.5 + 0j),\n    ((5, \"Z\"),): (-0.5 + 0j),\n    ((6, \"Z\"),): (-0.5 + 0j),\n    ((7, \"Z\"),): (-0.5 + 0j),\n    ((8, \"Z\"),): (-0.5 + 0j),\n    ((9, \"Z\"),): (-0.5 + 0j),\n    ((10, \"Z\"),): (-0.5 + 0j),\n    ((11, \"Z\"),): (-0.5 + 0j),\n    ((12, \"Z\"),): (-0.5 + 0j),\n    ((13, \"Z\"),): (-0.5 + 0j),\n}\n\nterms_h20_jw_23 = {\n    (): (3 + 0j),\n    ((0, \"Z\"),): (-0.5 + 0j),\n    ((1, \"Z\"),): (-0.5 + 0j),\n    ((2, \"Z\"),): (-0.5 + 0j),\n    ((3, \"Z\"),): (-0.5 + 0j),\n    ((4, \"Z\"),): (-0.5 + 0j),\n    ((5, \"Z\"),): (-0.5 + 0j),\n}\n\nterms_h20_bk_44 = {\n    (): (4 + 0j),\n    ((0, \"Z\"),): (-0.5 + 0j),\n    ((0, \"Z\"), (1, \"Z\")): (-0.5 + 0j),\n    ((2, \"Z\"),): (-0.5 + 0j),\n    ((1, \"Z\"), (2, \"Z\"), (3, \"Z\")): (-0.5 + 0j),\n    ((4, \"Z\"),): (-0.5 + 0j),\n    ((4, \"Z\"), (5, \"Z\")): (-0.5 + 0j),\n    ((6, \"Z\"),): (-0.5 + 0j),\n    ((3, \"Z\"), (5, \"Z\"), (6, \"Z\"), (7, \"Z\")): (-0.5 + 0j),\n}\n\nterms_lih_anion_bk = {\n    (): (5 + 0j),\n    ((0, \"Z\"),): (-0.5 + 0j),\n    ((0, \"Z\"), (1, \"Z\")): (-0.5 + 0j),\n    ((2, \"Z\"),): (-0.5 + 0j),\n    ((1, \"Z\"), (2, \"Z\"), (3, \"Z\")): (-0.5 + 0j),\n    ((4, \"Z\"),): (-0.5 + 0j),\n    ((4, \"Z\"), (5, \"Z\")): (-0.5 + 0j),\n    ((6, \"Z\"),): (-0.5 + 0j),\n    ((3, \"Z\"), (5, \"Z\"), (6, \"Z\"), (7, \"Z\")): (-0.5 + 0j),\n    ((8, \"Z\"),): (-0.5 + 0j),\n    ((8, \"Z\"), (9, \"Z\")): (-0.5 + 0j),\n}\n\n\n@pytest.mark.parametrize(\n    (\"orbitals\", \"mapping\", \"terms_exp\"),\n    [\n        (14, \"JORDAN_wigner\", terms_h20_jw_full),\n        (6, \"JORDAN_wigner\", terms_h20_jw_23),\n        (8, \"bravyi_KITAEV\", terms_h20_bk_44),\n        (10, \"bravyi_KITAEV\", terms_lih_anion_bk),\n    ],\n)\ndef test_particle_number_observable(orbitals, mapping, terms_exp, custom_wires, monkeypatch):\n    r\"\"\"Tests the correctness of the particle number observable :math:`\\hat{N}` generated\n    by the ``'particle_number'`` function.\n\n    The parametrized inputs are `.terms` attribute of the particle number `QubitOperator`.\n    The equality checking is implemented in the `qchem` module itself as it could be\n    something useful to the users as well.\n    \"\"\"\n\n    N = qchem.particle_number(orbitals, mapping=mapping, wires=custom_wires)\n\n    particle_number_qubit_op = QubitOperator()\n    monkeypatch.setattr(particle_number_qubit_op, \"terms\", terms_exp)\n\n    assert qchem._qubit_operators_equivalent(particle_number_qubit_op, N, wires=custom_wires)\n\n\n@pytest.mark.parametrize(\n    (\"orbitals\", \"msg_match\"),\n    [\n        (-3, \"'orbitals' must be greater than 0\"),\n        (0, \"'orbitals' must be greater than 0\"),\n    ],\n)\ndef test_exception_particle_number(orbitals, msg_match):\n    \"\"\"Test that the function `'particle_number'` throws an exception if the\n    number of orbitals is less than zero.\"\"\"\n\n    with pytest.raises(ValueError, match=msg_match):\n        qchem.particle_number(orbitals)\n","sub_path":"qchem/tests/test_particle_number_obs.py","file_name":"test_particle_number_obs.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"108645661","text":"\nimport os\n\nimport numpy as np\nimport scipy.misc as misc\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.utils import shuffle\n\n\ndef load_rgb_onehot(base_dir):\n    base_dir = r'F:\\worldview2\\Basic, Dallas, ,USA, 40cm_053951940010\\ml_datasets\\training\\grayscale'\n    base_dir = r'D:\\data\\machine_learning\\worldview\\dallas\\grayscale'\n    # base_dir = r'C:\\dev\\data\\cnn_deconv'\n    # base_dir = r'D:\\data\\machine_learning\\worldview\\dallas\\rgb_onehot'\n    base_dir = '/home/ec2-user/src/data/wv2/rgb_onehot'\n\n    in_dir = base_dir\n    true_files = []\n    true_targets = []\n    false_files = []\n    false_targets = []\n\n    # Grab the true\n    for dirpath, dirnames, filenames in os.walk(in_dir):\n        for filename in [f for f in filenames if f.startswith(\"true\")]:\n            true_files.append(os.path.join(dirpath, filename))\n            true_targets.append(1)\n\n    # Grab the false data\n    for dirpath, dirnames, filenames in os.walk(in_dir):\n        for filename in [f for f in filenames\n                         if (f.startswith(\"false\"))]:\n            false_files.append(os.path.join(dirpath, filename))\n            false_targets.append(0)\n\n    # Ensure they're both in the same order\n    true_files.sort()\n    false_files.sort()\n\n    print(len(true_files))\n    print(len(false_files))\n\n    all_files = true_files + false_files\n    all_targets = true_targets + false_targets\n    shuffle_files, shuffle_targets = shuffle(all_files, all_targets)\n\n    train_files, test_files, train_targets, test_targets = train_test_split(\n        shuffle_files, shuffle_targets, train_size=0.90,\n        stratify=shuffle_targets)\n\n    img_size = 96\n\n    # Load in the train images\n    images_train = np.zeros((len(train_files), img_size, img_size, 3),\n                            dtype='uint8')\n    for ix, f in enumerate(train_files):\n        images_train[ix, :, :, :] = misc.imread(f)\n\n    y = np.array(train_targets).astype('uint8')\n    labels_train = np.zeros((len(y), 2), dtype='uint8')\n    for ix, y_val in enumerate(y):\n        labels_train[ix, y[ix]] = 1\n\n    cls_train = labels_train.argmax(1)\n\n    # Load in the test images\n    images_test = np.zeros((len(test_files), img_size, img_size, 3),\n                           dtype='uint8')\n    for ix, f in enumerate(test_files):\n        images_test[ix, :, :, :] = misc.imread(f)\n\n    y = np.array(test_targets).astype('uint8')\n    labels_test = np.zeros((len(y), 2), dtype='uint8')\n    for ix, y_val in enumerate(y):\n        labels_test[ix, y[ix]] = 1\n\n    return images_train, labels_train, images_test, labels_test\n","sub_path":"roads.py","file_name":"roads.py","file_ext":"py","file_size_in_byte":2579,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"492162472","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\n\nimport sys\nsys.path.append('/zywa/aoam')\nimport re\n\nfrom utils.logger import logger\nfrom utils import config_util, exeCmd\nfrom utils.environment_util import environment_util\n\nenv = environment_util()\nconf = config_util.getDict('zookeeper')\nlog = logger(loggername='zookeeper')\n\n\ndef getQuorumPeerMain(hostAndPorts):\n    dicts = {}\n    for hostAndPort in hostAndPorts:\n        host = hostAndPort.split(':')[0]\n        dicts[host] = 'QuorumPeerMain'\n    return dicts\n\n\ndef startZk(host, server):\n    ZOOKEEPER_HOME = env.ZOOKEEPER_HOME\n    log.warn('开始启动 {host} 节点 {server} 服务'.format(host=host, server=server))\n    _shell = 'ansible client -l {host} -a \"{ZOOKEEPER_HOME}'.format(host=host, ZOOKEEPER_HOME=ZOOKEEPER_HOME)\n    _shell = _shell + '/bin/zkServer.sh start\"'\n    exeCmd.run(_shell)\n\n\ndef checkServerProcess():\n    hostAndPorts = conf.get('zookeeper.hosts')\n    serverlist = getQuorumPeerMain(hostAndPorts.split(','))\n    for host in serverlist:\n        content = exeCmd.execJps(host)\n        if len(re.findall(serverlist.get(host), content)) < 1:\n            log.warn('{host} 节点 QuorumPeerMain 服务未运行'.format(host=host))\n            startZk(host, 'QuorumPeerMain')\n        else:\n            log.info('{host} 节点 QuorumPeerMain 服务正在运行'.format(host=host))\n\n\nif __name__ == '__main__':\n    if conf is None:\n        sys.exit()\n    checkServerProcess()\n","sub_path":"bin/zookeeper.py","file_name":"zookeeper.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"577630788","text":"bl_info = {\n    \"name\": \"Jocafa Shortcuts\",\n    \"description\": \"Jocafa's Shortcuts\",\n    \"author\": \"Josh Faul\",\n    \"version\": (0, 0, 1),\n    \"blender\": (2, 90, 0),\n    \"location\": \"3D View > Sidebar\",\n    \"warning\": \"\",\n    \"category\": \"3D View\"\n}\n\nimport bpy\n\n#import panels\nfrom .panels import (\n    JCF_ShortcutsPanel,\n    JCF_RenderShortcutsPanel\n)\n\n#import operators\nfrom .operators import (\n    JCF_OT_set_render_size,\n    JCF_OT_set_render_scale,\n    JCF_OT_set_render_samples,\n    JCF_OT_add_tetrasphere,\n    JCF_OT_debug\n)\n\n#shared properties\nclass JCF_Properties(bpy.types.PropertyGroup):\n    \"\"\"JCF Shared Properties\"\"\"\n    overlays_expanded: bpy.props.BoolProperty(default=True)\n    foo: bpy.props.FloatProperty(name=\"foo\", default=0.0, min=0.0, max=1.0)\n\nclasses = (\n    JCF_Properties,\n    JCF_ShortcutsPanel,\n    JCF_RenderShortcutsPanel,\n    JCF_OT_set_render_size,\n    JCF_OT_set_render_scale,\n    JCF_OT_set_render_samples,\n    JCF_OT_add_tetrasphere,\n    JCF_OT_debug\n)\n\ndef register():\n    for cls in classes:\n        bpy.utils.register_class(cls)\n\n    bpy.types.Scene.jcf_props = bpy.props.PointerProperty(type=JCF_Properties)\n\ndef unregister():\n    for cls in classes:\n        bpy.utils.unregister_class(cls)\n\n    del bpy.types.Scene.jcf_props\n\n\nif __name__ == \"__main__\":\n    register()\n","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"465742377","text":"# -*- coding: utf-8 -*-\nimport numpy as np\n\nimport pymses\nimport sys\nimport glob as glob\nimport os\n\nfrom pymses.filters import CellsToPoints\nfrom pymses.utils import constants as cst\nimport matplotlib.pyplot as plt\nplt.ion() #Mis 2 lignes plus bas\n\nimport h5py\n\n\n#Remise a zero des parametres de matplotlib car ils sont modifies par pipeline_taille_disque_par_pdf.py ! Inutile si on quitte ipython entre les 2...\n#plt.rcParams.update(plt.rcParamsDefault)\n#plt.ion()\n\nif __name__ == '__main__':\n#-----------------------------------------------------------\n#Noms des simulations et caracteristiques du calcul du rayon\n#-----------------------------------------------------------\n    simu = 'B335_noturb_norot_hydro_pert_asym_aleatoire_bigbox_20pourc_sink_seuil_haut_rot0.25'\n    tag = '50_shr_bigbox'\n\n    output_min = 'None'\n    output_max = 40\n\n    seuil_rho = 1e-10\n\n\n\n\ndef trace_taille_disque(simu,tag,tag2,output_min,output_max,color,indice,tagfull=[1],t1=0.,legend='',marker='.',seuil_rho=1e-10,output_frag='None'):\n#------------------\n#Differents chemins\n#------------------\n    path_analyses='/home/averliat/these/analyses/'+simu+'/'\n\n    if output_min=='None':\n        ref=np.loadtxt(path_analyses+'t0_seuil_rho_'+str(seuil_rho)+'.txt')\n        output_0 = int(ref[0])\n        time_0 = ref[1]\n        output_min = output_0\n        file_save = 'Rayon_disque_par_pdf_modal_bin4_'+str(output_min)+'_'+str(output_max)+'.hdf5'\n\n\n\n#------------------------------------\n#Chargement des differentes quantites\n#------------------------------------\n    h5f = h5py.File(path_analyses+file_save, 'r')\n\n    mean_rad_loc_tab=h5f['mean_rad_loc_tab'][:]\n    time_tab=h5f['time_tab'][:]\n    time_cor_tab=h5f['time_cor_tab'][:]\n    min_rad_loc_tab=h5f['min_rad_loc_tab'][:]\n    max_rad_loc_tab=h5f['max_rad_loc_tab'][:]\n    numero_output=h5f['numero_output'][:]\n    mass_disk_tab=h5f['mass_disk_tab'][:]\n    log_rho_disk_tab=h5f['log_rho_disk_tab'][:]\n    mag_mean_broad_tab=h5f['mag_mean_broad_tab'][:]\n    median_rad_loc_tab=h5f['median_rad_loc_tab'][:]\n    rad_estim_tab=h5f['rad_estim_tab'][:]\n\n    h5f.close()\n\n\n\n#------------------------------------------------------------------\n#Redimensionnement tableaux pour la simu 50_mhr (disque trop grand)\n#------------------------------------------------------------------\n    if tag=='50_mhr':\n        time_tab=time_tab[0:len(mean_rad_loc_tab)-7]\n        numero_output=numero_output[0:len(mean_rad_loc_tab)-7]\n        time_cor_tab=time_cor_tab[0:len(mean_rad_loc_tab)-7]\n        mean_rad_loc_tab=mean_rad_loc_tab[0:len(mean_rad_loc_tab)-7]\n\n    if tag=='50_hr':\n        time_tab=time_tab[0:84]\n        numero_output=numero_output[0:84]\n        time_cor_tab=time_cor_tab[0:84]\n        mean_rad_loc_tab=mean_rad_loc_tab[0:84]\n\n\n\n#--------------------------------------------------\n#Filtre mediane glissante pour avoir moins de bruit\n#--------------------------------------------------\n    half_window=20\n    if 'rot' in tag2:#tag2=='20_rot1':\n        half_window=2\n        if 'MHD' in tag2:#tag2=='20_rot1':\n            half_window=5\n    def filtre_median(tab):\n        nbr_pnts=len(tab)-2*half_window\n        tab_filtre=np.zeros(nbr_pnts)\n        for i in range(nbr_pnts):\n            ind=i+half_window+1\n            tab_filtre[i]=np.median(tab[ind-half_window-1:ind+half_window])\n        return tab_filtre\n\n    def filtre_mean(tab):\n        nbr_pnts=len(tab)-2*half_window\n        tab_filtre=np.zeros(nbr_pnts)\n        for i in range(nbr_pnts):\n            ind=i+half_window+1\n            tab_filtre[i]=np.mean(tab[ind-half_window-1:ind+half_window])\n        return tab_filtre\n\n    def filtre_time(tab):\n        tab_filtre=tab[half_window:-half_window]\n        return tab_filtre\n\n\n\n#------------------\n#Debut de la figure\n#------------------\n\n    if output_frag != 'None':\n        frag=output_frag-output_min+1\n    else:\n        frag=output_max-output_min+1\n\n    mean_rad_loc_tab=mean_rad_loc_tab[0:frag]\n    time_tab=time_tab[0:frag]\n    time_cor_tab=time_cor_tab[0:frag]\n    min_rad_loc_tab=min_rad_loc_tab[0:frag]\n    max_rad_loc_tab=max_rad_loc_tab[0:frag]\n    numero_output=numero_output[0:frag]\n    mass_disk_tab=mass_disk_tab[0:frag]\n    log_rho_disk_tab=log_rho_disk_tab[0:frag]\n    mag_mean_broad_tab=mag_mean_broad_tab[0:frag]\n    median_rad_loc_tab=median_rad_loc_tab[0:frag]\n    rad_estim_tab=rad_estim_tab[0:frag]\n\n\n\n    fig=plt.figure(5)\n    ax=plt.gca()\n    ax.plot(filtre_time(time_cor_tab)*1e3,filtre_median(rad_estim_tab), color=color, label=legend)\n    plt.xlabel(r'Time (kyr)')\n    plt.ylabel(r'Disk radius (AU)')\n    #plt.legend(loc='best')\n\n    \n    if indice==len(tagfull)-1:\n        #Get artists and labels for legend and chose which ones to display\n        handles, labels = ax.get_legend_handles_labels()\n        display_rot = [5,6,7,8]\n        display_MHD = [3,4]\n        display_norot = [0,1,2]\n\n\n        #Create custom artists\n        #simArtist = plt.Line2D((0,1),(0,0), color='dimgrey', linestyle='solid')\n        #anyArtist = plt.Line2D((0,1),(0,0), color='dimgrey',linestyle='dashed')\n\n        #Create custom artists for color\n        #handles=[]\n        #for j in range(len(tag)):\n        #    handles.append(plt.Line2D((0,1),(0,0), color=colors, linestyle='solid'))#, label='The red data')\n\n\n\n        #Create legend from custom artist/label lists\n        #ax.legend([simArtist,anyArtist]+[handle for i,handle in enumerate(handles) if i in display],\n        #        [ur'$\\theta_{\\text{rad}} - \\theta$', ur'$\\theta_{\\text{orthorad}} - \\theta$']+[label for i,label in enumerate(labels) if i in display])\n        norot_legend=ax.legend([handle for i,handle in enumerate(handles) if i in display_norot],\n                [label for i,label in enumerate(labels) if i in display_norot],loc='upper right',ncol=1,fancybox=True)#,framealpha=0.1)\n        ax.add_artist(norot_legend)\n        #ax.add_artist(norot_legend)\n        #norot_legend.get_frame().set_alpha(0.1)\n        #norot_legend.set_frame_on(True)\n        #norot_legend.get_frame().set_edgecolor('black')\n\n        rot_legend=ax.legend([handle for i,handle in enumerate(handles) if i in display_rot],\n                [label for i,label in enumerate(labels) if i in display_rot],loc='upper left',ncol=1,fancybox=True)#,framealpha=0.1)\n        ax.add_artist(rot_legend)\n        ax.add_artist(rot_legend)\n        rot_legend.get_frame().set_alpha(0.1)\n        rot_legend.set_frame_on(True)\n        rot_legend.get_frame().set_edgecolor('black')\n\n        MHD_legend=ax.legend([handle for i,handle in enumerate(handles) if i in display_MHD],\n                [label for i,label in enumerate(labels) if i in display_MHD],loc='lower right',ncol=1)#,fancybox=True)#,framealpha=0.1)\n        #ax.add_artist(MHD_legend)\n        #MHD_legend.get_frame().set_alpha(0.1)\n        #MHD_legend.set_frame_on(True)\n        #MHD_legend.get_frame().set_edgecolor('black')\n\n        #ax.legend([simArtist,anyArtist],\n        #        [ur'$\\Delta \\theta_{\\text{rad}}$', ur'$\\Delta \\theta_{\\text{orthorad}}$'],loc='upper right')\n    \n\n    plt.xlim((-2,40))\n    #plt.xlim((0,8))\n    #plt.ylim((0,100))\n\n\n    plt.show()\n\n\n\nif __name__ == '__main__':\n    trace_taille_disque(simu,tag,tag,output_min,output_max,'b')\n\n\n\n\n\n\n\n","sub_path":"Article_1/trace_taille_disque_par_pdf_modal.py","file_name":"trace_taille_disque_par_pdf_modal.py","file_ext":"py","file_size_in_byte":7213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"396204188","text":"import matplotlib.pyplot as plt\n\nplt.style.use('ggplot')\n\nslices = [59219, 55466, 47544, 36443, 35917]\nlabels = ['JavaScript', 'HTML/CSS', 'SQL', 'Python', 'Java']\nexplode = [0, 0, 0, 0.1, 0]\t# % of ratio\n\nplt.pie(slices, explode=explode, shadow=True, labels=labels,\n\twedgeprops={'edgecolor': 'black'}, autopct='%1.1f%%')\n\nplt.title(\"My Awesome Pie Chart\")\nplt.tight_layout()\n\nplt.savefig('img/3.png', dpi=300)\n\nplt.show()\n","sub_path":"Matplotlib/3_pie.py","file_name":"3_pie.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"643788864","text":"from urllib import request\nfrom bs4 import BeautifulSoup\n\nheaders = {'user-agent':'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/79.0.3945.88 Safari/537.36'}\n\npage = 8660\nfor i in range(0, 1):\n    url = 'https://www.ptt.cc/bbs/movie/index%s.html'%(page)\n\n    req = request.Request(url=url, headers=headers)\n    res = request.urlopen(req).read().decode('utf8')\n\n    soup = BeautifulSoup(res, 'html.parser')\n\n\n    title =soup.select('div[class=\"title\"] a')\n    print(title)\n    # for each_title in title:\n    #     print(each_title.text)\n    #     try:\n    #         print('https://www.ptt.cc'+ each_title['href'])\n    #     except KeyError as e:\n    #         print(e.args)\n    #     print()\n    #\n    # page -= 1","sub_path":"pyetl/all_title.py","file_name":"all_title.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"129847527","text":"import read, copy\nfrom util import *\nfrom logical_classes import *\n\nverbose = 0\n\nclass KnowledgeBase(object):\n    def __init__(self, facts=[], rules=[]):\n        self.facts = facts\n        self.rules = rules\n        self.ie = InferenceEngine()\n\n    def __repr__(self):\n        return 'KnowledgeBase({!r}, {!r})'.format(self.facts, self.rules)\n\n    def __str__(self):\n        string = \"Knowledge Base: \\n\"\n        string += \"\\n\".join((str(fact) for fact in self.facts)) + \"\\n\"\n        string += \"\\n\".join((str(rule) for rule in self.rules))\n        return string\n\n    def _get_fact(self, fact):\n        \"\"\"INTERNAL USE ONLY\n        Get the fact in the KB that is the same as the fact argument\n\n        Args:\n            fact (Fact): Fact we're searching for\n\n        Returns:\n            Fact: matching fact\n        \"\"\"\n        for kbfact in self.facts:\n            if fact == kbfact:\n                return kbfact\n\n    def _get_rule(self, rule):\n        \"\"\"INTERNAL USE ONLY\n        Get the rule in the KB that is the same as the rule argument\n\n        Args:\n            rule (Rule): Rule we're searching for\n\n        Returns:\n            Rule: matching rule\n        \"\"\"\n        for kbrule in self.rules:\n            if rule == kbrule:\n                return kbrule\n\n    def kb_add(self, fact_rule):\n        \"\"\"Add a fact or rule to the KB\n        Args:\n            fact_rule (Fact|Rule) - the fact or rule to be added\n        Returns:\n            None\n        \"\"\"\n        printv(\"Adding {!r}\", 1, verbose, [fact_rule])\n        if isinstance(fact_rule, Fact):\n            if fact_rule not in self.facts:\n                self.facts.append(fact_rule)\n                for rule in self.rules:\n                    self.ie.fc_infer(fact_rule, rule, self)\n            else:\n                if fact_rule.supported_by:\n                    ind = self.facts.index(fact_rule)\n                    for f in fact_rule.supported_by:\n                        self.facts[ind].supported_by.append(f)\n                else:\n                    ind = self.facts.index(fact_rule)\n                    self.facts[ind].asserted = True\n        elif isinstance(fact_rule, Rule):\n            if fact_rule not in self.rules:\n                self.rules.append(fact_rule)\n                for fact in self.facts:\n                    self.ie.fc_infer(fact, fact_rule, self)\n            else:\n                if fact_rule.supported_by:\n                    ind = self.rules.index(fact_rule)\n                    for f in fact_rule.supported_by:\n                        self.rules[ind].supported_by.append(f)\n                else:\n                    ind = self.rules.index(fact_rule)\n                    self.rules[ind].asserted = True\n\n    def kb_assert(self, fact_rule):\n        \"\"\"Assert a fact or rule into the KB\n\n        Args:\n            fact_rule (Fact or Rule): Fact or Rule we're asserting\n        \"\"\"\n        printv(\"Asserting {!r}\", 0, verbose, [fact_rule])\n        self.kb_add(fact_rule)\n\n    def kb_ask(self, fact):\n        \"\"\"Ask if a fact is in the KB\n\n        Args:\n            fact (Fact) - Statement to be asked (will be converted into a Fact)\n\n        Returns:\n            listof Bindings|False - list of Bindings if result found, False otherwise\n        \"\"\"\n        print(\"Asking {!r}\".format(fact))\n        if factq(fact):\n            f = Fact(fact.statement)\n            bindings_lst = ListOfBindings()\n            # ask matched facts\n            for fact in self.facts:\n                binding = match(f.statement, fact.statement)\n                if binding:\n                    bindings_lst.add_bindings(binding, [fact])\n\n            return bindings_lst if bindings_lst.list_of_bindings else []\n\n        else:\n            print(\"Invalid ask:\", fact.statement)\n            return []\n\n    def _kb_remove_facts(self, fact_or_rule):\n        for f in fact_or_rule.supports_facts:\n            f_idx = self.facts.index(f)\n            kb_f = self.facts[f_idx]\n            for pair in kb_f.supported_by:\n                if fact_or_rule in pair:\n                    kb_f.supported_by.remove(pair)\n            if not kb_f.supported_by and not kb_f.asserted:\n                self.kb_retract(kb_f)\n\n    def _kb_remove_rules(self, fact_or_rule):\n        for r in fact_or_rule.supports_rules:\n            r_idx = self.rules.index(r)\n            kb_r = self.rules[r_idx]\n            for pair in kb_r.supported_by:\n                if fact_or_rule in pair:\n                    kb_r.supported_by.remove(pair)\n            if not kb_r.supported_by and not kb_r.asserted:\n                self.kb_retract(kb_r)\n\n    def kb_retract(self, fact_or_rule):\n        \"\"\"Retract a fact from the KB\n\n        Args:\n            fact (Fact) - Fact to be retracted\n\n        Returns:\n            None\n        \"\"\"\n        printv(\"Retracting {!r}\", 0, verbose, [fact_or_rule])\n        ####################################################\n        # Implementation goes here\n        # Not required for the extra credit assignment\n        if isinstance(fact_or_rule, Rule) and fact_or_rule in self.rules:\n            rule_idx = self.rules.index(fact_or_rule)\n            kb_rule = self.rules[rule_idx]\n            if kb_rule.asserted or kb_rule.supported_by:\n                return\n            self._kb_remove_facts(kb_rule)\n            self._kb_remove_rules(kb_rule)\n            self.rules.pop(rule_idx)\n        elif isinstance(fact_or_rule, Fact) and fact_or_rule in self.facts:\n            fact_idx = self.facts.index(fact_or_rule)\n            kb_fact = self.facts[fact_idx]\n            if kb_fact.supported_by:\n                kb_fact.asserted = False\n                return\n            self._kb_remove_facts(kb_fact)\n            self._kb_remove_rules(kb_fact)\n            self.facts.pop(fact_idx)\n\n    def kb_explain(self, fact_or_rule, indent = ''):\n        \"\"\"\n        Explain where the fact or rule comes from\n\n        Args:\n            fact_or_rule (Fact or Rule) - Fact or rule to be explained\n\n        Returns:\n            string explaining hierarchical support from other Facts and rules\n        \"\"\"\n        ####################################################\n        if isinstance(fact_or_rule, Rule):\n            if fact_or_rule in self.rules:\n                rule_idx = self.rules.index(fact_or_rule)\n                kb_rule = self.rules[rule_idx]\n                res_str = indent + 'rule: ('\n                for sIdx in range(len(kb_rule.lhs) - 1):\n                    res_str += str(kb_rule.lhs[sIdx]) + ', '\n                res_str += str(kb_rule.lhs[-1]) + ') -> ' + str(kb_rule.rhs)\n                if kb_rule.asserted:\n                    res_str += ' ASSERTED'\n                res_str += '\\n'\n                indent += '  '\n                for pair in kb_rule.supported_by:\n                    res_str += indent + 'SUPPORTED BY\\n'\n                    res_str += self.kb_explain(pair[0], indent + '  ')\n                    res_str += self.kb_explain(pair[1], indent + '  ')\n            else:\n                return indent + \"Rule is not in the KB\\n\"\n        elif isinstance(fact_or_rule, Fact):\n            if fact_or_rule in self.facts:\n                fact_idx = self.facts.index(fact_or_rule)\n                kb_fact = self.facts[fact_idx]\n                res_str = indent + 'fact: ' + str(kb_fact.statement)\n                if kb_fact.asserted:\n                    res_str += ' ASSERTED'\n                res_str += '\\n'\n                indent += '  '\n                for pair in kb_fact.supported_by:\n                    res_str += indent + 'SUPPORTED BY\\n'\n                    res_str += self.kb_explain(pair[0], indent + '  ')\n                    res_str += self.kb_explain(pair[1], indent + '  ')\n            else:\n                return indent + \"Fact is not in the KB\\n\"\n        else:\n            return False\n        return res_str\n\n\nclass InferenceEngine(object):\n    def fc_infer(self, fact, rule, kb):\n        \"\"\"Forward-chaining to infer new facts and rules\n\n        Args:\n            fact (Fact) - A fact from the KnowledgeBase\n            rule (Rule) - A rule from the KnowledgeBase\n            kb (KnowledgeBase) - A KnowledgeBase\n\n        Returns:\n            Nothing            \n        \"\"\"\n        printv('Attempting to infer from {!r} and {!r} => {!r}', 1, verbose,\n            [fact.statement, rule.lhs, rule.rhs])\n        ####################################################\n        # Implementation goes here\n        # Not required for the extra credit assignment\n        b = match(rule.lhs[0], fact.statement)\n        if b:\n            s_by = [(fact, rule)]\n            st = instantiate(rule.lhs[0], b)\n            new_rhs = instantiate(rule.rhs, b)\n            if len(rule.lhs) == 1 and st == fact.statement:  # create new fact with rule.rhs\n                new_f = Fact(new_rhs, s_by)\n                kb.kb_assert(new_f)\n                fact.supports_facts.append(new_f)\n                rule.supports_facts.append(new_f)\n            else:\n                new_lhs = []\n                if st != fact.statement:  # create new rule with new rule.lhs[0]\n                    new_lhs.append(st)\n                for old_lhs in rule.lhs[1:]:\n                    new_lhs.append(instantiate(old_lhs, b))\n                new_rule = Rule([new_lhs, new_rhs], s_by)\n                kb.kb_assert(new_rule)\n                fact.supports_rules.append(new_rule)\n                rule.supports_rules.append(new_rule)\n","sub_path":"student_code.py","file_name":"student_code.py","file_ext":"py","file_size_in_byte":9390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"535776962","text":"# -*- encoding: utf-8 -*-  \r\n\r\nimport datetime\r\nfrom rklib.model import BaseModel\r\nfrom apps.models.cache_tool import CacheTool\r\nfrom apps.config import game_config\r\nfrom apps.models.virtual.card import Card as CardMod\r\nfrom apps.models.user_base import UserBase\r\nfrom apps.common import utils\r\n\r\n\r\nclass UserTips(BaseModel):\r\n    #\"\"\"三国的推送信息\r\n    #\"\"\"\r\n    def __init__(self, uid = None):\r\n        BaseModel.__init__(self)\r\n        self.uid = uid\r\n        self.usertips_info = {'mytips':[],\r\n                              'max_tips_time':None,\r\n                              'max_other_tips_time':None,\r\n                              'interval_time':None,\r\n                              \r\n                           }\r\n        \r\n    @classmethod\r\n    def get_instance(cls,uid):\r\n        obj = cls.get(uid)\r\n        if obj is None:\r\n            obj = cls._install(uid)\r\n        return obj\r\n        \r\n    @classmethod\r\n    def get(cls,uid):\r\n        obj = super(UserTips,cls).get(uid)\r\n        return obj\r\n    \r\n    @classmethod\r\n    def _install(cls, uid):\r\n        obj = cls(uid)\r\n        obj.put()\r\n        return obj \r\n    \r\n    def add_cards_tips(self, cid):\r\n        #\"\"\"type=2,获得武将\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 2:\r\n                continue\r\n            cids = sango_tips[k]['cids']\r\n            if cid not in cids:\r\n                continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            card_mod_obj = CardMod.get(cid)\r\n            star = card_mod_obj.star\r\n            name = card_mod_obj.name\r\n            user_base_obj = UserBase.get(self.uid)\r\n            temp['content'] = sango_tips[k]['content']%(user_base_obj.username,str(star), name)\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            \r\n            self.add_cache_tool(temp)\r\n            break\r\n        \r\n    def get_double_charge_tips(self):\r\n        #\"\"\"type:3,若玩家的首充双倍还在，\r\n        #每次通信中之后，都显示一次\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        data = {}\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 3:\r\n                continue\r\n            from apps.models.user_property import UserProperty\r\n            user_property_obj = UserProperty.get(self.uid)\r\n            if user_property_obj.first_charge is False:\r\n                continue\r\n            data['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            data['content'] = sango_tips[k]['content']\r\n            \r\n        return data \r\n    \r\n    def add_four_property_cards(self, ucid):\r\n        #\"\"\"type:4,用户的指定武将成为四满武将\r\n        #\"\"\"\r\n        from apps.models.user_cards import UserCards\r\n        user_cards_obj = UserCards.get(self.uid)\r\n        cid = user_cards_obj.cards[ucid]['cid']\r\n        \r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 4:\r\n                continue\r\n            cids = sango_tips[k]['cids']\r\n            if cid not in cids:\r\n                continue\r\n            \r\n            #判断是否是四满\r\n            card_mod_obj = CardMod.get(cid)\r\n            #武将等级是否满\r\n            if user_cards_obj.cards[ucid].get('lv', 1) < card_mod_obj.max_lv:\r\n                continue\r\n            #武将技能是否满\r\n            if user_cards_obj.cards[ucid].get('sk_lv', 1) < card_mod_obj.max_sk_lv:\r\n                continue\r\n            #技能突破是否满\r\n            if user_cards_obj.cards[ucid].get('awake_index', 0) < len(card_mod_obj.awake_skid):\r\n                continue\r\n            #+1蛋是否满了\r\n            if card_mod_obj.category not in ['7','8']:\r\n                max_pl = user_cards_obj.cards[ucid].get('pl_hp',0) + \\\r\n                         user_cards_obj.cards[ucid].get('pl_recover',0) + \\\r\n                         user_cards_obj.cards[ucid].get('pl_attack',0)\r\n                if max_pl < 297:\r\n                    continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            star = card_mod_obj.star\r\n            name = card_mod_obj.name\r\n            user_base_obj = UserBase.get(self.uid)\r\n            temp['content'] = sango_tips[k]['content']%(user_base_obj.username,str(star), name)\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            \r\n            self.add_cache_tool(temp)\r\n            break\r\n        \r\n    def add_title_tips(self, title_id):\r\n        #\"\"\"type:5,用户获得了指定的称号\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 5:\r\n                continue\r\n            title_ids = sango_tips[k]['title_ids']\r\n            if title_id not in title_ids:\r\n                continue\r\n            \r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            title_config = self.base_info.title_info.get_title_config()\r\n            title_name = title_config['title_info'][title_id]['name']\r\n            user_base_obj = UserBase.get(self.uid)\r\n            temp['content'] = sango_tips[k]['content']%(user_base_obj.username,title_name)\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            \r\n            self.add_cache_tool(temp)\r\n            break\r\n            \r\n    def add_jflw_tips(self, guild_names):\r\n        #\"\"\"type:6-疾风乱舞结束后，对前3名的同盟进行宣传\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 6:\r\n                continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            temp['content'] = sango_tips[k]['content']%(guild_names['jf_name_1'], guild_names['jf_name_2'], \r\n                                                        guild_names['jf_name_3'], guild_names['lw_name_1'], \r\n                                                        guild_names['lw_name_2'], guild_names['lw_name_3'],)\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            self.add_cache_tool(temp)\r\n            break\r\n    \r\n    def add_new_guild_tips(self, guild_name):\r\n        #\"\"\"type:7-新同盟的建立\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 7:\r\n                continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            user_base_obj = UserBase.get(self.uid)\r\n            temp['content'] = sango_tips[k]['content']%(user_base_obj.username, guild_name)\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            self.add_cache_tool(temp)\r\n            break\r\n        \r\n    def add_lv_up_tips(self, lv):\r\n        #\"\"\"type:8-玩家升级至指定等级\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 8:\r\n                continue\r\n            lv_config = sango_tips[k]['lv']\r\n            if lv not in lv_config:\r\n                continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            user_base_obj = UserBase.get(self.uid)\r\n            temp['content'] = sango_tips[k]['content']%(user_base_obj.username, str(lv))\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            self.add_cache_tool(temp)\r\n            break\r\n        \r\n    def add_special_dungeon_tips(self, dungeon_type, floor_id, room_id):\r\n        #\"\"\"type:9-炼狱战场首次通关\r\n        #\"\"\"\r\n        if dungeon_type != 'special':\r\n            return \r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 9:\r\n                continue\r\n            pass_dungeon_ids = sango_tips[k]['special']\r\n            param_dungeon_id = str(floor_id) + '-' + str(room_id)\r\n            if param_dungeon_id not in pass_dungeon_ids:\r\n                continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            user_base_obj = UserBase.get(self.uid)\r\n            dungeon_name = game_config.special_dungeon_config['special'][floor_id]['rooms'][room_id]['name']\r\n            temp['content'] = sango_tips[k]['content']%(user_base_obj.username, dungeon_name)\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            self.add_cache_tool(temp)\r\n            break\r\n            \r\n    def add_guild_lv_up(self, gid, lv):\r\n        #\"\"\"type:10-同盟每次升级\r\n        #\"\"\"\r\n        sango_tips = game_config.sango_tips_config.get('sango_tips', {})\r\n        for k in sango_tips:\r\n            if sango_tips[k]['type'] != 10:\r\n                continue\r\n            temp = {}\r\n            if len(self.usertips_info['mytips']) > 50:\r\n                self.usertips_info['mytips'].pop(0)\r\n            temp['createtime'] = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\r\n            from apps.models.guild_base import GuildBase\r\n            guild_base_obj = GuildBase.get(gid)\r\n            if guild_base_obj is None:\r\n                return  \r\n            temp['content'] = sango_tips[k]['content']%(guild_base_obj.gname, str(lv))\r\n            temp['uid'] = self.uid\r\n            self.usertips_info['mytips'].append(temp)\r\n            self.put()\r\n            self.add_cache_tool(temp)\r\n            break\r\n    \r\n    def add_cache_tool(self, info):\r\n        #\"\"\"增加到全局变量里，给其他玩家看\r\n        #\"\"\"\r\n        sango_tips_record = CacheTool.get('sango_tips_record', [])\r\n        if len(sango_tips_record) > 100:\r\n            sango_tips_record.pop(0)\r\n        sango_tips_record.append(info)\r\n        CacheTool.set('sango_tips_record', sango_tips_record)\r\n        \r\n    def get_all_tips(self):\r\n        #\"\"\"获得所有的tips信息\r\n        #\"\"\"\r\n        #先看是否有双倍\r\n        data = []\r\n        tips_3 = self.get_double_charge_tips()\r\n        if tips_3:\r\n            data.append(tips_3)\r\n        \r\n        #看看自己是否还有其他的提醒\r\n        if self.usertips_info['max_tips_time'] is  None:\r\n            if len(self.usertips_info['mytips']) > 0:\r\n                data.extend(self.usertips_info['mytips'])\r\n                self.usertips_info['max_tips_time'] = self.usertips_info['mytips'][-1]['createtime']\r\n                self.put()\r\n        else:\r\n            for temp in self.usertips_info['mytips']:\r\n                if temp['createtime'] <= self.usertips_info['max_tips_time']:\r\n                    continue\r\n                self.usertips_info['max_tips_time'] = temp['createtime']\r\n                self.put()\r\n                data.append(temp)\r\n        #只传10个\r\n        if len(data) >= 10:\r\n            return data[:10] \r\n        \r\n        #考虑到服务器的性能，不能每个请求都去给tips，中间间隔30分钟\r\n        now = datetime.datetime.now()\r\n        if 'interval_time' in self.usertips_info and self.usertips_info['interval_time'] is not None:\r\n            sango_tips_interval = game_config.sango_tips_config.get('sango_tips_interval', 0.5)\r\n            interval_time = self.usertips_info['interval_time']\r\n            if interval_time + datetime.timedelta(hours = sango_tips_interval) > now:\r\n                return data \r\n        self.usertips_info['interval_time'] = now\r\n\r\n        other_data = []\r\n        #再看看有没有其他人的tips\r\n        sango_tips_record = CacheTool.get('sango_tips_record', [])\r\n        for record in sango_tips_record:\r\n            if self.uid == record['uid']:\r\n                continue\r\n            if self.usertips_info['max_other_tips_time'] is None:\r\n                other_data.append(record)\r\n                self.usertips_info['max_other_tips_time'] = record['createtime']\r\n                self.put()\r\n            else:\r\n                if record['createtime'] > self.usertips_info['max_other_tips_time']:\r\n                    self.usertips_info['max_other_tips_time'] = record['createtime']\r\n                    other_data.append(record)\r\n                    self.put()\r\n                    \r\n        n = len(data)\r\n        m = len(other_data)\r\n        if m +n > 10:\r\n            other_data = other_data[n-10:]\r\n        data.extend(other_data)\r\n        return data  \r\n\r\n    @property\r\n    def base_info(self):\r\n        if not hasattr(self, '_base_info'):\r\n            from apps.models.user_base import UserBase\r\n            self._base_info = UserBase.get(self.uid)\r\n        return self._base_info\r\n","sub_path":"python/project/card/my_card/apps/models/user_tips.py","file_name":"user_tips.py","file_ext":"py","file_size_in_byte":14230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"46129952","text":"import json\n\njson_file = open('friends_json.txt', 'r')\nfile_contents = json.load(json_file)\njson_file.close()\n\nprint(file_contents['friends'][0])\n\ncars = [\n    {'make': 'Ford', 'model': 'Fiesta'},\n    {'make': 'Ford', 'model': 'Focus'}\n]\n\nfile_ptr = open('cars_json.txt', 'w')\njson.dump(cars, file_ptr)\nfile_ptr.close()\n","sub_path":"Files/json_imports.py","file_name":"json_imports.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"430338365","text":"import logging\nfrom abc import ABC, abstractmethod\nfrom typing import Any, Dict\n\nfrom pydantic import BaseModel, ValidationError\n\nlogger = logging.getLogger(__name__)\n\n\nclass BaseEnvelope(ABC):\n    @staticmethod\n    def _parse_user_dict_schema(user_event: Dict[str, Any], schema: BaseModel) -> Any:\n        if user_event is None:\n            return None\n        logger.debug(\"parsing user dictionary schema\")\n        try:\n            return schema(**user_event)\n        except (ValidationError, TypeError):\n            logger.exception(\"Validation exception while extracting user custom schema\")\n            raise\n\n    @staticmethod\n    def _parse_user_json_string_schema(user_event: str, schema: BaseModel) -> Any:\n        if user_event is None:\n            return None\n        # this is used in cases where the underlying schema is not a Dict that can be parsed as baseModel\n        # but a plain string i.e SQS has plain string payload\n        if schema == str:\n            logger.debug(\"input is string, returning\")\n            return user_event\n        logger.debug(\"trying to parse as json encoded string\")\n        try:\n            return schema.parse_raw(user_event)\n        except (ValidationError, TypeError):\n            logger.exception(\"Validation exception while extracting user custom schema\")\n            raise\n\n    @abstractmethod\n    def parse(self, event: Dict[str, Any], schema: BaseModel):\n        return NotImplemented\n","sub_path":"aws_lambda_powertools/utilities/advanced_parser/envelopes/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"625401969","text":"# !/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n\r\nimport os\r\nimport re\r\nimport urllib.request\r\nfrom lxml import etree\r\n\r\n\r\nsite = 'http://fllwedding.com'\r\ntags_html = urllib.request.urlopen(site).read()\r\ntags_parser = etree.HTML(tags_html)\r\ntags_title = tags_parser.xpath('//*[@id=\"divTags\"]/dd/ul/li//a[@href]/text()')\r\ntags_url = tags_parser.xpath('//*[@id=\"divTags\"]/dd/ul/li//a/@href')\r\nall_tags = dict(zip(tags_title, tags_url))\r\nprint (\"Tags:\", tags_title)\r\ntag_input = input('please input tag: ')\r\nif tag_input not in all_tags:\r\n    print ('No Such Tag')\r\n    exit()\r\nnew_tag_html = urllib.request.urlopen(all_tags[tag_input]).read()\r\nnew_tag_parser = etree.HTML(new_tag_html)\r\nmax_page_num = new_tag_parser.xpath('/html/body/div[5]/div[1]/div[3]/a[13]//@href')[0].strip(site)[-3:]\r\nprint ('Max_Page_Num is:', max_page_num)\r\nnext_page_num = int(input('please input page_num: '))\r\nnext_page_url = []\r\nfor i in range(next_page_num):\r\n    next_page_url.append(site + '//' + all_tags[tag_input].split('/')[3].split('.')[0] + '_' + str(i+1) + '.html')\r\n","sub_path":"meimeidabenyin/purl_get/NewUrl.py","file_name":"NewUrl.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"40584151","text":"# This file is part of e-genie\n#\n# e-genie is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# e-genie is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with e-genie.  If not, see <http://www.gnu.org/licenses/>.\n\nfrom django import forms\n\nfrom deployments.models import Deployment\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import *\nfrom crispy_forms.bootstrap import StrictButton, FormActions\nfrom django.core.urlresolvers import reverse\n\n\nclass DeploymentAddHubForm(forms.ModelForm):\n\n    class Meta:\n        fields = ['hub']\n        model = Deployment\n        widgets = {\n            'hub': forms.HiddenInput()\n        }\n\n    def __init__(self, *args, **kwargs):\n        super(DeploymentAddHubForm, self).__init__(*args, **kwargs)\n\n        self.fields['hub'].label = \"Hub Identifier\"\n\n    def clean_hub(self):\n        hub = self.cleaned_data['hub']\n\n        if self.instance.hub:\n            raise forms.ValidationError(\n                \"There is already a hub assigned to this deployment.\")\n\n        try:\n            old_deployment = hub.deployment\n            old_deployment.hub = None\n            old_deployment.save()\n        except (AttributeError, Deployment.DoesNotExist):\n            pass\n\n        return hub\n\n\nclass DeploymentCreateForm(forms.ModelForm):\n    gas_pence_per_kwh = forms.IntegerField(label='Gas Cost (pence per KWh)')\n    elec_pence_per_kwh = forms.IntegerField(\n        label='Electricity Cost (pence per KWh)')\n\n    def __init__(self, *args, **kwargs):\n        super(DeploymentCreateForm, self).__init__(*args, **kwargs)\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.label_class = 'col-lg-2'\n        self.helper.field_class = 'col-lg-8'\n        self.helper.form_method = 'post'\n        self.helper.form_action = 'deployments:create'\n        self.helper.layout = Layout(\n            Fieldset('', 'client_name', 'address_line_one', 'post_code',\n                     'notes', 'photo', 'elec_pence_per_kwh', 'gas_pence_per_kwh'),\n            FormActions(Submit('save', 'Save Changes'))\n        )\n\n    class Meta:\n        fields = [\n            'client_name', 'address_line_one', 'post_code', 'notes', 'photo', 'elec_pence_per_kwh', 'gas_pence_per_kwh']\n        model = Deployment\n\n\nclass DeploymentEndForm(forms.ModelForm):\n\n    class Meta:\n        fields = ['id']\n        model = Deployment\n\n    def save(self):\n        self.instance.end()\n\n\nclass DeploymentRemoveHubForm(forms.ModelForm):\n\n    class Meta:\n        fields = ['id']\n        model = Deployment\n\n    def save(self):\n        deployment = self.instance\n\n        deployment.hub = None\n        deployment.save()\n\n\nclass DeploymentStartForm(forms.ModelForm):\n\n    class Meta:\n        fields = ['id']\n        model = Deployment\n\n    def save(self):\n        self.instance.start()\n\n\nclass DeploymentUpdatePhotoForm(forms.ModelForm):\n\n    class Meta:\n        fields = ['photo', ]\n        model = Deployment\n\n\nclass DeploymentUpdateForm(forms.ModelForm):\n    gas_pence_per_kwh = forms.IntegerField(label='Gas Cost (pence per KWh)')\n    elec_pence_per_kwh = forms.IntegerField(\n        label='Electricity Cost (pence per KWh)')\n\n    def __init__(self, *args, **kwargs):\n        super(DeploymentUpdateForm, self).__init__(*args, **kwargs)\n        self.helper = FormHelper()\n        self.helper.form_class = 'form-horizontal'\n        self.helper.label_class = 'col-lg-2'\n        self.helper.field_class = 'col-lg-8'\n        self.helper.form_method = 'post'\n        self.helper.layout = Layout(\n            Fieldset('', 'client_name', 'address_line_one', 'post_code',\n                     'notes', 'elec_pence_per_kwh', 'gas_pence_per_kwh'),\n            FormActions(Submit('save', 'Save Changes'))\n        )\n\n    class Meta:\n        fields = [\n            'client_name', 'address_line_one', 'post_code', 'notes', 'elec_pence_per_kwh', 'gas_pence_per_kwh']\n        model = Deployment\n","sub_path":"src/egenie/deployments/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":4403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"592215735","text":"import os.path as osp\nimport sys\nBASE_DIR = osp.abspath(osp.join(osp.dirname(__file__), osp.pardir))\nsys.path.insert(0, BASE_DIR)\nfrom utils.config import TRAIN_SAVE_DIR, PAIR_PATH, \\\n    TOTAL_PAIR, INTERVAL, CONCAT, AUGMENT_PROBABILITY, NUM_WORKERS, save_args, MEAN\nfrom model.regressor.create_regressor import create_model, create_metric, ModelEMA\nfrom utils.regressor.retail_eval import evaluation_num_fold\nfrom utils.regressor.retail_dataset import RetailTrain, RetailTest, parseList\nfrom utils.regressor.plots import plot_recognition_results\nfrom utils.regressor.distance_calculation_arcface import test_inference\nfrom torch.nn import DataParallel\nfrom datetime import datetime\nfrom tqdm.autonotebook import tqdm\nfrom torch.optim import lr_scheduler\nimport torch.optim as optim\nimport time\nimport numpy as np\nimport os\nimport os.path as osp\nimport logging\nimport torch.utils.data\nimport argparse\nimport yaml\nfrom timm.scheduler.cosine_lr import CosineLRScheduler\nimport math\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--epochs', type=int, default=5, help='Total epochs')\nparser.add_argument('--input_size', type=int, default=112, help='Input Size')\nparser.add_argument('--batch_size', type=int, default=32, help='Batch size')\nparser.add_argument('--resume', type=str, default='', help='Resume weights path')\nparser.add_argument('--gpu', type=str, default='0, 1', help='GPUs')\nparser.add_argument('--save_dir', type=str, default='./second_match', help='The path to save log and weights')\nparser.add_argument('--name', type=str, default='Retail_v2_', help='save to save_dir/name')\nparser.add_argument('--save_interval', type=int, default=1, help='The interval of saving model')\nparser.add_argument('--test_interval', type=int, default=1, help='The interval of testing model')\nparser.add_argument('--use_cgd', action='store_true', default=False, help='Whether to use CGD')\nparser.add_argument('--backbone', default='efficientnet_b4', help='Backbone')\nparser.add_argument('--swin_type', default='large', help='swin type')\nparser.add_argument('--loss_head', default='Circleloss', help='Loss_head')\nparser.add_argument('--pretrain', default=False, help='start from pretrain model')\nparser.add_argument('--ema', action='store_true', default=True, help='Exponential Moving Average')\nopt = parser.parse_args()\n\n\ndef init_log(output_dir):\n    logging.basicConfig(level=logging.DEBUG,\n                        format='%(asctime)s %(message)s',\n                        datefmt='%Y%m%d-%H:%M:%S',\n                        filename=os.path.join(output_dir, 'log.log'),\n                        filemode='w')\n    console = logging.StreamHandler()\n    console.setLevel(logging.INFO)\n    logging.getLogger('').addHandler(console)\n    return logging\n\n\n# gpu init\nmulti_gpus = False\nif torch.cuda.device_count() > 1 and len(opt.gpu) > 1:\n    multi_gpus = True\nos.environ['CUDA_VISIBLE_DEVICES'] = opt.gpu\n\n# other init\nstart_epoch = 1\nsave_dir = os.path.join(opt.save_dir, opt.backbone + '_' + opt.loss_head + '_' + str(opt.epochs) + '_' + str(opt.batch_size)\n                        + '_' + str(opt.input_size) + '_' + str(opt.pretrain) + '_' + datetime.now().strftime('%Y%m%d_%H%M%S'))\n\nos.makedirs(save_dir, exist_ok=True)\n\nweights_dir = osp.join(save_dir, 'weights')\nos.makedirs(weights_dir, exist_ok=True)\n\nresults_file = osp.join(save_dir, 'results.txt')\n\nwith open(osp.join(save_dir, 'opt.yaml'), 'w') as f:\n    yaml.safe_dump(vars(opt), f, sort_keys=False)\n\nwith open(osp.join(save_dir, 'config.yaml'), 'w') as f:\n    yaml.safe_dump(save_args, f, sort_keys=False)\n\n\nlogging = init_log(save_dir)\n_print = logging.info\n_print(opt)\n\nimg_size = opt.input_size\n\nnet = create_model(name=opt.backbone, pretrained=opt.pretrain, \n                   input_size=img_size, cgd=opt.use_cgd, swin_type=opt.swin_type).cuda()\nif opt.ema:\n    ema = ModelEMA(net)\n\nloss = create_metric(opt.loss_head).cuda()\n\n# define trainloader and testloader\n# img_size = net.get_image_size(model_name)\n\ntrainset = RetailTrain(root=TRAIN_SAVE_DIR, img_size=img_size, **AUGMENT_PROBABILITY)\ntrainloader = torch.utils.data.DataLoader(trainset, batch_size=opt.batch_size,\n                                          shuffle=True, num_workers=NUM_WORKERS, drop_last=True)\n\n# nl: left_image_path\n# nr: right_image_path\nnl, nr, flags, folds = parseList(pair_path=PAIR_PATH)\ntestdataset = RetailTest(nl, nr, img_size=img_size)\ntestloader = torch.utils.data.DataLoader(testdataset, batch_size=opt.batch_size,\n                                         shuffle=False, num_workers=NUM_WORKERS, drop_last=False)\n\nif opt.resume:\n    ckpt = torch.load(opt.resume)\n    net.load_state_dict(ckpt['net_state_dict'])\n    start_epoch = ckpt['epoch'] + 1\n\n# define optimizers\noptimizer_ft = optim.SGD(params=net.parameters(), lr=0.1, momentum=0.9, nesterov=True, weight_decay=4e-4)\nexp_lr_scheduler = lr_scheduler.MultiStepLR(optimizer_ft, milestones=[5, 52, 90], gamma=0.1)\n# optimizer_ft = optim.SGD(params=net.parameters(), lr=5e-4, momentum=0.9, nesterov=True, weight_decay=4e-4)\n# optimizer_ft = optim.AdamW(params=net.parameters(), lr=5e-4, eps=1e-8,betas=(0.9, 0.999), weight_decay=0.05)\n# exp_lr_scheduler = CosineLRScheduler(optimizer=optimizer_ft,\n#                                      t_initial=opt.epochs * len(trainloader),\n#                                      t_mul=1.,\n#                                      lr_min=5e-6,\n#                                      warmup_lr_init=5e-7,\n#                                      warmup_t=math.ceil(0.05 * opt.epochs * len(trainloader)),\n#                                      cycle_limit=1,\n#                                      t_in_epochs=False\n#                                      )\n\nif multi_gpus:\n    net = DataParallel(net)\n    loss = DataParallel(loss)\n\nbest_acc = 0.0\nbest_epoch = 0\n# result = ('%15s' * 4) % (\n#         'epochs', 'train_loss', 'accs', 'thresholds')\n#\n# with open(results_file, 'a') as f:\n#     f.write(result + '\\n')  # append metrics, val_loss\n\n# init metric and loss\ntrain_total_loss, accs, thresholds = 0, 0, 0\n\nfor epoch in range(start_epoch, opt.epochs + 1):\n    exp_lr_scheduler.step(epoch)\n    # train model\n    _print('Train Epoch: {}/{} ...'.format(epoch, opt.epochs))\n\n    net.train()\n\n    train_total_loss = 0.0\n    total = 0\n    since = time.time()\n    trainloader = tqdm(trainloader)\n    for i, data in enumerate(trainloader):\n\n        img, label = data[0].cuda(), data[1].cuda()\n        batch_size = img.size(0)\n        optimizer_ft.zero_grad()\n\n        raw_logits = net(img)\n        total_loss = loss(raw_logits, label)\n        if multi_gpus:\n            total_loss = torch.mean(total_loss)\n        total_loss.backward()\n        optimizer_ft.step()\n        if opt.ema:\n            ema.update(net)\n\n        train_total_loss += total_loss.item() * batch_size\n        total += batch_size\n\n        trainloader.set_description('Training Progress')\n    train_total_loss = train_total_loss / total\n    time_elapsed = time.time() - since\n    loss_msg = '    total_loss: {:.4f} time: {:.0f}m {:.0f}s' \\\n        .format(train_total_loss, time_elapsed // 60, time_elapsed % 60)\n    _print(loss_msg)\n\n    # test model on lfw\n    if epoch % opt.test_interval == 0:\n        net.eval()\n        featureLs = []\n        featureRs = []\n        _print('Test Epoch: {} ...'.format(epoch))\n        for data in tqdm(testloader):\n            for i in range(len(data)):\n                data[i] = data[i].cuda()\n            if opt.ema:\n                features = [test_inference(d, ema.ema, concat=CONCAT, mean=MEAN).numpy() for d in data]\n            else:\n                features = [test_inference(d, net, concat=CONCAT, mean=MEAN).numpy() for d in data]\n            featureLs.append(features[0])\n            featureRs.append(features[1])\n        featureLs = np.concatenate(featureLs, axis=0)\n        featureRs = np.concatenate(featureRs, axis=0)\n\n        result = {'fl': featureLs, 'fr': featureRs, 'fold': folds, 'flag': flags}\n        # save tmp_result\n        # scipy.io.savemat('./result/tmp_result.mat', result)\n        accs, thresholds = evaluation_num_fold(result, num=TOTAL_PAIR / INTERVAL)\n        accs = np.mean(accs)\n        thresholds = np.mean(thresholds) \n        _print('    ave: {:.4f}'.format(accs * 100))\n        _print('    best_threshold: {:.4f}'.format(thresholds))\n\n    result = ('%10s' * 1 + '%10.4g' * 3) % (\n        f'{epoch}/{opt.epochs}', train_total_loss, accs, thresholds)\n\n    with open(results_file, 'a') as f:\n        f.write(result + '\\n')  # append metrics, val_loss\n    # save model\n    if epoch % opt.save_interval == 0:\n        msg = 'Saving checkpoint: {}'.format(epoch)\n        _print(msg)\n        if multi_gpus:\n            net_state_dict = net.module.state_dict()\n        else:\n            net_state_dict = net.state_dict()\n        torch.save({\n            'epoch': epoch,\n            # 'net_state_dict': net_state_dict,\n            'ema': ema.ema.state_dict(), },\n            os.path.join(weights_dir, '%03d.ckpt' % epoch))\n\nplot_recognition_results(save_dir=save_dir)\nprint('finishing training')\n","sub_path":"tools/train_regressor.py","file_name":"train_regressor.py","file_ext":"py","file_size_in_byte":9070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"597350700","text":"import random\nimport re\n\nimport numpy as np\nimport torch\n\nMAX_CHAR_LENGTH = 45\nNUM_CHAR_PAD = 2\n\n# Regular expressions used to normalize digits.\nDIGIT_RE = re.compile(r\"\\d\")\n\n\ndef normalize_digit_word(word):\n    return DIGIT_RE.sub(\"0\", word)\n\n\ndef load_glove_embedding(embedding_path, normalize_digits=True):\n    \"\"\"\n    Load glove embeddings from file.\n\n    Args:\n        embedding_path:  the file to load embedding from.\n        normalize_digits: whether to normalize the digits characters in token.\n\n    Returns: embedding dict, embedding dimension, caseless\n\n    \"\"\"\n\n    embed_dim = -1\n    embedd_dict = dict()\n    with open(embedding_path, \"r\") as file:\n        for line in file:\n            line = line.strip()\n            if len(line) == 0:\n                continue\n\n            tokens = line.split()\n            if embed_dim < 0:\n                embed_dim = len(tokens) - 1\n            else:\n                assert embed_dim + 1 == len(\n                    tokens\n                ), f\"glove_dim{embed_dim} cur_dim{len(tokens)}\"\n            embedd = np.empty(embed_dim, dtype=np.float32)\n            embedd[:] = tokens[1:]\n            word = (\n                DIGIT_RE.sub(\"0\", tokens[0]) if normalize_digits else tokens[0]\n            )\n            embedd_dict[word] = embedd\n\n    return embedd_dict\n\n\ndef set_random_seed(seed):\n    if seed is not None:\n        random.seed(seed)\n        np.random.seed(seed)\n        torch.manual_seed(seed)\n        if torch.cuda.is_available():\n            torch.cuda.manual_seed(seed)\n","sub_path":"forte/models/ner/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"3324208","text":"\nimport os\nimport sys\nimport cv2\nimport torch\nimport argparse\nimport numpy as np\nimport imageio as io\nimport matplotlib.pyplot as plt\n\nfrom tqdm import tqdm\nfrom tools.customdatasets import transform\nfrom tools.post_processing import rectify_image\nfrom net_models import ResNetUNet\n\nclass DocumentCleanup():\n    def __init__(self, debug=False, net_weights='unet_best.pth'):\n        self.debug = debug\n\n        if torch.cuda.is_available():\n            self.device = torch.device(\"cuda:\" + str(torch.cuda.current_device()))\n        else:\n            self.device = torch.device(\"cpu\")\n\n        self.model = self.load_network(net_weights)\n\n    def load_network(self, net_weights):\n        # Instantiate network model\n        net = ResNetUNet(pretrained=True, num_classes=1)\n\n        # Load a pretrained network\n        try:\n            weights_folder = 'net_weights'\n            weights_path = os.path.join(weights_folder, net_weights)\n\n            if not os.path.exists(weights_folder):\n                os.makedirs(weights_folder)\n\n            if not os.path.exists(weights_path):\n                raise Exception(\"You need a network weights file.\")\n\n            net.load_state_dict(torch.load(weights_path))\n\n        except Exception as err:\n            print(\"A problem has occurred loading model file.\", err)\n        \n        return net.to(self.device).eval()\n\n    def post_processing(self, image):\n        # Rectify and binarize network output image.\n        image = rectify_image(image)\n        return image\n\n    def load_image(self, file_path):\n        \n        image = io.imread(file_path)\n        image = transform(image)\n        return image\n\n    def process(self, input_path, output_path):\n        with torch.no_grad():\n            image = self.load_image(input_path)\n\n            if len(image.shape) < 4:\n                image = image.unsqueeze_(0)\n\n            image = image.to(self.device)\n\n            output = self.model(image)[0]\n            output = output.cpu().squeeze()\n            output = self.post_processing(output.numpy())\n\n            if self.debug:\n                f, axarr = plt.subplots(1, 2)\n                axarr[0].imshow(image.cpu()[0][0], cmap='gray', vmin=0, vmax=1)\n                axarr[1].imshow(output, cmap='gray', vmin=0, vmax=255)\n                plt.show()\n            else:\n                cv2.imwrite(output_path, output)\n\n\ndef create_parser_arguments():\n    parser = argparse.ArgumentParser(description=\"Document Cleanup\")\n    parser.add_argument(\n        \"--input_folder\",\n        dest=\"input_folder\",\n        help=\"Folder containing images to be cleaned.\",\n        default=\"dataset/test/noisy_data\"\n    )\n    parser.add_argument(\n        \"--output_folder\",\n        dest=\"output_folder\",\n        help=\"Folder where the processed imagens will be saved.\",\n        default=\"dataset/test/clean_data\"\n    )\n    parser.add_argument(\n        \"--debug\",\n        dest=\"debug\",\n        help=\"Run in debug mode. [y/n]\",\n        default=\"n\"\n    )\n    try:\n        args = parser.parse_args()\n    except:\n        parser.print_help()\n        sys.exit(1)\n    return args\n\n\nif __name__==\"__main__\":\n    args = create_parser_arguments()\n    debug = args.debug.lower() == 'y'\n\n    doc = DocumentCleanup(debug)\n    for file in tqdm(os.listdir(args.input_folder)):\n        input_path = os.path.join(args.input_folder, file)\n        output_path = os.path.join(args.output_folder, file)\n        doc.process(input_path, output_path)","sub_path":"01-DocumentCleanup/cleanup.py","file_name":"cleanup.py","file_ext":"py","file_size_in_byte":3452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"547369231","text":"#! /usr/bin/python\n\n#-*-coding:utf-8-*-\n\n\nfrom PyQt5 import QtGui\nfrom PyQt5.QtPrintSupport import (\n\tQPrintDialog,\n\t QPrinter,\n\t QPrintPreviewDialog,\n\t )\nfrom PyQt5.QtWidgets import ( \n\t\tQApplication,\n\t\tQMainWindow,\n\t\tQPushButton,\n\t\tQTextEdit,\n\n\t\t)\n\n\n\nclass Window(QMainWindow):\n\t\"\"\"\n\tCette classe nous montre qu'on peut soi-même faire les\n\tdimensions de la fenêtre à la main\n\t\"\"\"\n\tdef __init__(self):\n\t\tsuper().__init__() # Appel du constructeur de la classe QMainWindow\n\n\t\tself.title = \"PyQt5 Window \"\n\t\tself.top = 100\n\t\tself.left = 100\n\t\tself.width = 680\n\t\tself.height = 500\n\n\n\t\tself.setWindowIcon(QtGui.QIcon(\"icons/line.png\")) #ça na pas marché\n\n\n\t\tself.init_window()\n\n\n\n\tdef init_window(self):\n\n\t\tself.button = QPushButton(\"Print\", self)\n\n\t\tself.button.setGeometry(100,100,100, 50)\n\n\t\tself.button.clicked.connect(self.createPrintDialog)\n\n\t\tself.button1 = QPushButton(\"Preview\", self)\n\t\tself.button1.setGeometry(203, 100,100, 50)\n\n\t\tself.button1.clicked.connect(self.printPreviewDialog)\n\n\n\n\t\tself.textEdit = QTextEdit(self)\n\t\tself.textEdit.setGeometry(100, 150, 200, 200 )\n\n\n\t\tself.setWindowTitle(self.title)\n\t\tself.setGeometry(self.top, self.left, self.width, self.height)\n\t\tself.show()\n\n\n\n\tdef createPrintDialog(self):\n\t\tprinter = QPrinter(QPrinter.HighResolution)\n\t\tdialog = QPrintDialog(printer, self)\n\n\t\tif dialog.exec_() == QPrintDialog.Accepted:\n\t\t\tself.textEdit.print_(printer)\n\n\n\n\tdef printPreviewDialog(self):\n\t\tprinter = QPrinter(QPrinter.HighResolution)\n\t\tpreviewDialog = QPrintPreviewDialog(printer, self)\n\t\tpreviewDialog.paintRequested.connect(self.printPreview)\n\t\tpreviewDialog.exec_()\n\n\n\n\tdef printPreview(self, printer):\n\t\tself.textEdit.print_(printer)\n\n\n\n\nif __name__ == '__main__':\n\timport sys\n\tapp = QApplication(sys.argv)\n\twindow = Window()\n\n\tsys.exit(app.exec_())\n\n\n\n","sub_path":"project/qt3/printerDialog.py","file_name":"printerDialog.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"232523031","text":"import math\ndef Rot2Eul(A):\n    if A[2][2] == 1:\n        Y = 0\n        X = math.atan2(A[0][1],A[0][0])\n        Z = X\n    else:\n        Y = math.acos(A[2][2])\n        X = math.atan2((A[2][0]/math.sin(Y)),-(A[2][1]/math.sin(Y)))\n        Z = math.atan2((A[0][2]/math.sin(Y)),(A[1][2]/math.sin(Y)))\n    x = math.degrees(X)\n    y = math.degrees(Y)\n    z = math.degrees(Z)\n    return x,y,z\nA = [[ 1, 0, 0],\n [0 , 0,  1],\n [ 0 , -1 , 0]]\nprint(Rot2Eul(A))\n","sub_path":"Rot2Eul.py","file_name":"Rot2Eul.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"378716131","text":"from flask import Flask, request\nimport time\nimport os\nimport tinys3\n\napp = Flask(__name__)\n\ndef dump_files(clinic_name, files, notes):\n    ts = time.time()\n    stamp = '%s_%s'  % (clinic_name, str(int(round(ts))))\n    folder = \"files/%s/\" % stamp\n    os.makedirs(folder)\n    f = open('%snotes' %folder, 'w')\n    f.write(notes)\n\n    # ##### For uploading to S3\n    # conn = tinys3.Connection('S3_ACCESS_KEY', 'S3_SECRET_KEY', tls=True)\n    # conn.upload(''.join([folder, f.filename]), f, 'my_bucket')\n    # for f in files:\n    #     conn.upload(''.join([folder, f.filename]), f, 'my_bucket')  # my_bucket is a bucket set up on aws\n    #     #######\n\n    f.close()\n    for f in files:\n        f.save(''.join([folder, f.filename]))\n\n\n@app.route(\"/generalUpload\", methods=[\"POST\", \"GET\"])\ndef generalUpload():\n    return app.send_static_file('uploadPage.html')\n\n@app.route(\"/process\", methods=[\"POST\"])\ndef process():\n    if request.method == \"POST\":\n        if request.files['data'].filename == '':\n            return app.send_static_file('Missing_File_Error_Page.html')\n        if request.form['clinic'] == '':\n            return app.send_static_file('Missing_File_Error_Page.html')\n        clinic_name = request.form['clinic']\n        uploaded_files = request.files.getlist(\"data\")\n        notes = request.form['notes']\n        dump_files(clinic_name, uploaded_files, notes)\n    return \"Data has been recieved. Thank you for participating.\"\n\napp.run()","sub_path":"generalUpload.py","file_name":"generalUpload.py","file_ext":"py","file_size_in_byte":1451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"309006775","text":"#Write a method quick_trick_block_stack( n ) that loops through numbers N through 2. If N is prime, create a queue and put it on the stack.  If N is not prime, enqueue N on the top queue.  Return the stack of queues.\n#Then loop through the stack of queues printing all the numbers, with each queue on a single line.\n\n\nfrom Stack import Stack\nfrom Queue import Queue\nimport math\n\ndef is_prime(n):\n    for factor in range(2, int(math.sqrt(n)) + 1 ):\n        if n % factor == 0:\n            return False\n    return True\n\ndef quick_trick_block_stack(n):\n    stack = Stack()\n    queue = Queue()\n\n    stack.push( queue )\n\n    for number in range(n+1, 1, -1):\n        if is_prime(number):\n            stack.push( Queue() )\n        else:\n            stack.peek().enqueue( number )\n\n    while not stack.is_empty():\n        queue = stack.pop()\n        while not queue.is_empty():\n            print( queue.dequeue(), end=\" \")\n        print()\n\n\nquick_trick_block_stack(100)","sub_path":"StacksAndQueues/StacksAndQueues/quick_trick_block_stack.py","file_name":"quick_trick_block_stack.py","file_ext":"py","file_size_in_byte":963,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"73597517","text":"\n# coding: utf-8\n\n# In[1]:\n\n# Function/Method\n\n\n# In[ ]:\n\n#planforweekends\n#- go for shopping # 1\n# - go for camping # 2\n# - go for fishing # 3\n\n\n# In[8]:\n\n# 1\n\ndef planforweekends():\n    print ('go for Shopping')\n    print ('go for camping')\n    print ('go for fishing')\n\n\n# In[5]:\n\nplanforweekends()\n\n\n# In[9]:\n\n#2\ndef planforweekends(act1,act2,act3):\n    print ('go for ' + act1)\n    print ('go for ' + act2)\n    print ('go for ' + act3)\n\n\n# In[7]:\n\nplanforweekends('Shopping','Camping','Fishing')\n\n\n# In[10]:\n\nplanforweekends('Fishing','Camping','Shopping')\n\n\n# In[14]:\n\n\n\n\n# In[18]:\n\n#3\ndef planforweekends(act1,act2,act3='Shopping'):\n    print ('go for ' + act1) # statement1\n    print ('go for ' + act2) # Statement2\n    print ('go for ' + act3) # Statement3\n\n\n# In[17]:\nplanforweekends('Fishing','Camping')\n\nplanforweekends('Fishing','Camping','Treeking')\n\n\n# In[19]:\n\n# Want to do a sum of two numbers\n\n\n# In[30]:\n\n#1\ndef sum(a,b):\n    \"\"\" This function performs sum of two numbers\"\"\"\n    c = a + b\n    print ('Sum is : ', c)\n\n\n# In[23]:\n\nprint(sum(7,3))\n\n\n# In[27]:\n\nprint(help(sum))\n\n\n# In[31]:\n\n#2\ndef sum1(a,b):\n    \"\"\" This function performs sum of two numbers\"\"\"\n    c = a + b\n    return c \n\n\n# In[32]:\n\nprint(sum1(7,3))\n\n\n# In[33]:\n\nx = sum1(10,15)\n\n\n# In[34]:\n\nprint(x)\n\n\n# In[35]:\n\ny = sum(10,15)\n\n\n# In[36]:\n\nprint(y)\n\n\n# In[37]:\n\n#3\n\ndef check_number(number):\n    if number % 2 == 0:\n        print (number, \" is even\")\n    else:\n        print (number,\" is odd \")\n\n\n# In[38]:\n\ncheck_number(10)\n\n\n# In[39]:\n\ncheck_number(7)\n\n\n# In[41]:\n\n#4 Multiple values from a function\n\ndef sum_subtract(a,b):\n    c = a + b\n    d = a - b\n    return c,d\n\n\n# In[43]:\n\nx,y=sum_subtract(10,20)\n\n\n# In[44]:\n\nprint(x)\n\n\n# In[45]:\n\nprint(y)\n\n\n# In[46]:\n\n#5\n\ndef sum_add_sub_mul_div(a,b):\n    c = a + b\n    d = a - b\n    e = a * b\n    f = a / b\n    return c,d,e,f\n\n\n# In[47]:\n\nsum_add_sub_mul_div(10,15)\n\n\n# In[56]:\n\np = sum_add_sub_mul_div(10,15)\n\n\n# In[57]:\n\nprint(type(p))\n\n\n# In[58]:\n\nfor i in p:\n    print (i, end=', ')\n\n\n# In[51]:\n\nt = (1,2,3,4)\n\n\n# In[52]:\n\nfor i in t:\n    print (i)\n\n\n# In[53]:\n\nprint(t)\n\n\n# In[59]:\n\n# define function within another function\n\n\n# In[68]:\n\n#6\ndef display(str):\n    def message():\n        return 'Hi, Hello '\n    result = message()+str\n    return result\n\n\n# In[64]:\n\ndisplay(\"Krishna\")\n\n\n# In[65]:\n\ndisplay(\"Ashir\")\n\n\n# In[67]:\n\n# Functions are first class objects\n\n\n# In[71]:\n\n#7 \n\ndef out(input1):\n    return 'Hey ' + input1\n\ndef message():\n    return 'How are you? '\n\n\n# In[72]:\n\nout(message())\n\n\n# In[78]:\n\n#8\n\ndef out(input1):\n    def message():\n        return 'How are you?'\n    return message()\n\n\n# In[79]:\n\nout(message())\n\n\n# In[91]:\n\n#9\ndef out(input1):\n    def message():\n        return 'How are you? ' + input1\n    return message\n\n\n# In[92]:\n\nxyz = out('Hi')\n\n\n# In[93]:\n\nprint(xyz())\n\n\n# In[94]:\n\nname = 'Raj'\n\n\n# In[98]:\n\nprint (name)\n\n\n# In[97]:\n\nprint(xyz())\n\n\n# In[ ]:\n\n# attribute vs function\n\n#asdfasd\n#asdfa()\n\n\n# In[99]:\n\nxyz = out(name)\n\n\n# In[100]:\n\nprint(xyz())\n\n\n# In[101]:\n\n# Pass by object reference\n\n\n# In[102]:\n\na = 10\n\n\n# In[103]:\n\nprint(a)\n\n\n# In[104]:\n\nprint(id(a))\n\n\n# In[105]:\n\nb = 10\n\n\n# In[106]:\n\nprint(id(b))\n\n\n# In[107]:\n\nprint (a,id(a))\n\n\n# In[109]:\n\ndef mod(a):\n    a = 10\n    print (a,id(a))\n\n\n# In[111]:\n\na = 20\nmod(a)\nprint (a,id(a))\n\n\n# In[115]:\n\ndef modify(lst):\n    lst.append(9)\n    print (lst,id(list))\n\n\n# In[113]:\n\nlst = [1,2,3,4]\n\n\n# In[116]:\n\nprint(modify(lst))\n\n\n# In[117]:\n\nprint (lst,id(lst))\n\n\n# In[118]:\n\n# object is immutable = modified value is not available outside the function\n# object is mutable = modified value is available. \n\n\n# In[119]:\n\n# Functional programming\n\n\n# In[1]:\n\nimport re\ntext = ''' show version\nAruba Operating System Software.\nArubaOS (MODEL: 225), Version 8.3.0.0\nWebsite: http://www.arubanetworks.com\n(c) Copyright 2017 Hewlett Packard Enterprise Development LP.\nCompiled on 2017-11-12 at 09:42:46 PST (build 62262) by p4build\nFIPS Mode :disabled\n\nAP uptime is 19 hours 22 minutes 23 seconds\n'''\npattern = r'((Version ([\\d.]+)\\n.*\\n.*\\n.*\\(build ([\\d]+)))'\n#pattern = r'build ([\\d]+)'\nmatch = re.search(pattern,text)\nprint ('Version: '+match.group(3)+' Build: '+match.group(4))\n\n\n# In[ ]:\n\n\n\n","sub_path":"All-Classes_DotPY_Modified_Sublime/Class25-FunctionsIndepth.py","file_name":"Class25-FunctionsIndepth.py","file_ext":"py","file_size_in_byte":4211,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"205206003","text":"\n# get_links()调用\nfrom bs4 import  BeautifulSoup\n\nimport requests\nimport re \nimport time \n#设置表头\nheaders={  \n    'User-Agent':\"Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/17.0.963.56 Safari/535.11\"\n}\n\ninfo_lists =[]\ndr = re.compile(r'<[^>]+>',re.S)\nbuyMoney = 1\n# 获取到页面上的数据_LIST\ndef  get_info (url) :\n     url ='http://www.shenjihua.cc/jihua/9451.html'\n     print('url===============',url)\n     res = requests.get (url)\n     _LIST = []\n     linkes =re.findall('<p>(.*?)</p>',res.text,re.S) \n     for  rank  in linkes:\n          dd = dr.sub('',rank)\n          splic_data =dd.split(' ')\n          _LIST.append(splic_data)\n     aa =_LIST\n     return  BUYHAOMA(analysis(aa),beishu(aa)) \n\n    #  bets[0]['issue'] ='1001011'\n# 解析把药买的号返回 这个是做平买方便\ndef analysis(myList):\n    willBuy_data = myList[0][4].split(':')\n    return  willBuy_data[-1].split(',')\n# 解析把药买的号返回 这个是做平买方便\ndef beishu(aa):\n    # print('aa=========',aa)\n    num =0\n    if aa[1][-1]=='挂':\n        print(aa[1])\n        num =num+1\n        print(num)\n        if aa[2][-1]=='挂':\n            num=num+1\n            if aa[3][-1]=='挂':\n                num=num+1\n                if aa[4][-1]=='挂':\n                    num=num+1\n                    if aa[5][-1]=='挂':\n                        num=num+1\n                        if aa[5][-1]=='挂':\n                           num=num+1\n    return num\n    # return  willBuy_data[-1].split(',')\n\n# 最多追4次,根据数据返回要买多少倍\ndef more4QI(myList):\n    # 是否中了\n    isWinning = myList[1][-1]\n    isWinning2 = myList[2][-1]\n    starQihao = int(myList[0][0][0:3])\n    endQihao  = int(myList[0][5][0:3])\n    # buyToubei =endQihao\n    isbeiTou =0\n    if isWinning =='中':\n        isbeiTou= 0\n    else:\n        isbeiTou= 2\n    if isWinning2!='中':\n        isbeiTou=0\n\n    beilvNum = endQihao-starQihao \n    buyBeilvNum = pow(2, beilvNum) * buyMoney\n    return  buyBeilvNum\n    \n    willBuy_data = myList[0][4].split(':')\n    # print(willBuy_data)\n    return  willBuy_data[-1].split(',')\n\n# 获取网站的第一个计划\ndef  get_links():     \n     web_data = requests.get('http://www.shenjihua.cc/jihua-catid-8-areaid-2-jhms-5.html',headers =headers)\n     soup =BeautifulSoup(web_data.text,'lxml')\n     linkes = soup.select('body > div > div > ul > li:nth-of-type(1) > a')\n     print(linkes[1].get('href'))\n     url =linkes[1].get('href')\n    #  print('url===============',url)\n     return get_info(url)\n\n# 下注的参数\ndef  BUYHAOMA(buyNumber,money):\n    # buyNumber =['0', '3', '4', '5', '7']\n    # money=1\n    print('buyNumber=================',buyNumber)\n    print('money=================',money)\n    orders=[]\n    for  i  in  buyNumber :\n        obj = {'odds':'9.99','play':'B5','code':'cqssc'}\n        obj['money']=pow(2,money)*buyMoney # 钱\n        obj['num']='第五球 '+str(i) # 第几个那个号\n        obj['content']=i # 那个号\n        obj['title']='第五球 '+str(i) # 第几个那个号\n        orders.append(obj) # 拼接数组\n    return  orders \n\nif __name__ == '__main__':\n    # print(get_links())\n    \n#     url ='http://www.shenjihua.cc/jihua/9457.html'\n    while True:\n        pass\n        try:\n            print(1)\n            time.sleep(10)\n            print(22)\n        except Exception as e:\n            pass\n            print(e)\n    \n   \n","sub_path":"jihua_more13-20180416/bb777/shenjihua.py","file_name":"shenjihua.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"398035725","text":"import json\nimport os\nimport sys\n\nclass Logger:\n    def pressAnyKey( self, exitMessage ):\n        print(exitMessage)\n        os.system('pause')\n        sys.exit()\n\nclass JSONLoader:\n    \n    def __init__(self, filename, L1 = Logger()):\n        self.fileName = filename\n        self.jsonLogger = L1\n        \n    def loadJSON( self ):\n        try:\n            with open(self.fileName) as jsonFile:\n                self.jsonData = json.load(jsonFile)\n        except IOError:\n            self.jsonLogger.pressAnyKey(self.fileName + \": file not found\")\n        except ValueError:\n            self.jsonLogger.pressAnyKey(self.fileName + \": syntax error\")\n        finally:\n            print(self.fileName + \" successfully loaded\")\n\n    def parseJSONData(self):\n        try:\n            self.jsonConcentration = []\n            self.jsonThickness = []\n            self.jsonWavelength = self.jsonData[\"wavelength\"]\n            self.jsonLayersNumber = self.jsonData[\"layersNumber\"]\n            for i in range(self.jsonLayersNumber):\n                self.jsonConcentration.append( self.jsonData[\"layers\"][i][\"concentration\"])\n                self.jsonThickness.append( self.jsonData[\"layers\"][i][\"thickness\"])\n        except KeyError:\n            self.jsonLogger.pressAnyKey(\".json: data not found\")\n        except IndexError:\n            self.jsonLogger.pressAnyKey(\".json: actual number of layers does not match with layersNumber value\")\n\n        if isinstance( self.jsonWavelength, (int, float)) == False or isinstance( self.jsonLayersNumber, (int, float)) == False or isinstance( self.jsonThickness, (list)) == False or isinstance( self.jsonConcentration, (list)) == False:\n            self.jsonLogger.pressAnyKey(\".json: type mismatch\")\n        for i in range( self.jsonLayersNumber):\n            if isinstance(self.jsonConcentration[i], (int, float)) == False or isinstance( self.jsonThickness[i], (int, float)) == False:\n                self.jsonLogger.pressAnyKey(\".json: type mismatch\")\n            if self.jsonConcentration[i] <= 0 or self.jsonConcentration[i] >= 1 or self.jsonThickness <= 0:\n                self.jsonLogger.pressAnyKey(\".json: data out of range\")\n\n        if self.jsonWavelength < 0.85 or self.jsonWavelength > 1.5 or self.jsonLayersNumber <= 0:\n            self.jsonLogger.pressAnyKey(\".json: data out of range\")\n\n        print(\".json file successfully parsed\")\n        return(( self.jsonWavelength, self.jsonLayersNumber, self.jsonConcentration, self.jsonThickness))\n\n\n","sub_path":"src/jsonloader.py","file_name":"jsonloader.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"322995989","text":"# coding=utf-8\n'''\n@Title:Predicting Continuous Target Variables with Regression Analysis\n\n@Author: tyee.noprom@qq.com\n@Time: 4/11/16\n'''\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.preprocessing import PolynomialFeatures\nfrom sklearn.metrics import r2_score\nfrom sklearn.cross_validation import train_test_split\nfrom sklearn.metrics import mean_squared_error\n\n\nclass LinearRegressionGD(object):\n    '''\n    线性回归模型,梯度下降\n    使用最小二乘法\n    '''\n\n    def __init__(self, eta=0.001, n_iter=20):\n        self.eta = eta\n        self.n_iter = n_iter\n\n    def fit(self, X, y):\n        self.w_ = np.zeros(1 + X.shape[1])\n        self.cost_ = []\n\n        for i in range(self.n_iter):\n            output = self.net_input(X)\n            errors = (y - output)\n            self.w_[1:] += self.eta * X.T.dot(errors)\n            self.w_[0] += self.eta * errors.sum()\n            cost = (errors ** 2).sum() / 2.0\n            self.cost_.append(cost)\n        return self\n\n    def net_input(self, X):\n        return np.dot(X, self.w_[1:]) + self.w_[0]\n\n    def predict(self, X):\n        return self.net_input(X)\n\n\ndf = pd.read_csv('./housing.data', header=None, sep='\\s+')\ncols = ['LSTAT', 'INDUS', 'NOX', 'RM', 'MEDV']\ndf.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT',\n              'MEDV']\nX = df[['RM']].values\ny = df['MEDV'].values\n\n\ndef look_data():\n    '''\n    查看数据\n    :return:\n    '''\n    df.columns = ['CRIM', 'ZN', 'INDUS', 'CHAS', 'NOX', 'RM', 'AGE', 'DIS', 'RAD', 'TAX', 'PTRATIO', 'B', 'LSTAT',\n                  'MEDV']\n    # print(df.head())\n\n\ndef visualize_data():\n    '''\n    可视化数据\n    '''\n    sns.set(style='whitegrid', context='notebook')\n    sns.pairplot(df[cols], size=2.5)\n    plt.show()\n\n\ndef corelation_data():\n    '''\n    发现数据之间的关联关系\n    '''\n    cm = np.corrcoef(df[cols].values.T)\n    sns.set(font_scale=1.5)\n    # 热力图\n    hm = sns.heatmap(cm, cbar=True, annot=True, fmt='.2f', annot_kws={'size': 15}, yticklabels=cols, xticklabels=cols)\n    plt.show()\n\n\ndef linear_model():\n    '''\n    线性回归\n    :return:\n    '''\n    from sklearn.preprocessing import StandardScaler\n    sc_x = StandardScaler()\n    sc_y = StandardScaler()\n    X_std = sc_x.fit_transform(X)\n    y_std = sc_y.fit_transform(y)\n    lr = LinearRegressionGD()\n    lr.fit(X_std, y_std)\n    plt.plot(range(1, lr.n_iter + 1), lr.cost_)\n    plt.ylabel('SSE')\n    plt.xlabel('Epoch')\n    plt.show()\n    # RM与MEDV关系图\n    lin_regplot(X_std, y_std, lr)\n    plt.xlabel('Average number of rooms [RM] (standardized)')\n    plt.ylabel('Price in $1000\\'s [MEDV] (standardized)')\n    plt.show()\n\n\ndef lin_regplot(X, y, model):\n    '''\n    画线性关系图\n    :param X:\n    :param y:\n    :param model:\n    :return:\n    '''\n    plt.scatter(X, y, c='blue')\n    plt.plot(X, model.predict(X), color='red')\n    return None\n\n\ndef ransac_fit(X, y):\n    '''\n    一个强健的fit\n    :return:\n    '''\n    from sklearn.linear_model import RANSACRegressor\n    ransac = RANSACRegressor(LinearRegression(),\n                             max_trials=100,\n                             min_samples=50,\n                             residual_metric=lambda x: np.sum(np.abs(x), axis=1),\n                             residual_threshold=5.0,\n                             random_state=0)\n    ransac.fit(X, y)\n    # 输出斜率|截距等数据\n    print('Slope: %.3f' % ransac.estimator_.coef_[0])\n    print('Intercept: %.3f' % ransac.estimator_.intercept_)\n    # plot\n    inlier_mask = ransac.inlier_mask_\n    outlier_mask = np.logical_not(inlier_mask)\n    line_X = np.arange(3, 10, 1)\n    line_y_ransac = ransac.predict(line_X[:, np.newaxis])\n    plt.scatter(X[inlier_mask], y[inlier_mask],\n                c='blue', marker='o', label='Inliers')\n    plt.scatter(X[outlier_mask], y[outlier_mask],\n                c='lightgreen', marker='s', label='Outliers')\n    plt.plot(line_X, line_y_ransac, color='red')\n    plt.xlabel('Average number of rooms [RM]')\n    plt.ylabel('Price in $1000\\'s [MEDV]')\n    plt.legend(loc='upper left')\n    plt.show()\n\n\ndef multi_regression():\n    '''\n    多元回归\n    :return:\n    '''\n    from sklearn.cross_validation import train_test_split\n    X = df.iloc[:, :-1].values\n    y = df['MEDV'].values\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n    slr = LinearRegression()\n    slr.fit(X_train, y_train)\n    y_train_pred = slr.predict(X_train)\n    y_test_pred = slr.predict(X_test)\n    # 计算Mean Squared Error (MSE)\n    print('MSE train: %.3f, test: %.3f' % (\n        mean_squared_error(y_train, y_train_pred),\n        mean_squared_error(y_test, y_test_pred)))\n    # MSE train: 19.958, test: 27.196 => over fitting\n\n    # 计算R*R\n    # If R*R =1, the model  ts the data perfectly with a corresponding MSE = 0 .\n    print('R^2 train: %.3f, test: %.3f' % (r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))\n\n    # plot\n    plt.scatter(y_train_pred, y_train_pred - y_train, c='blue', marker='o', label='Training data')\n    plt.scatter(y_test_pred, y_test_pred - y_test, c='lightgreen', marker='s', label='Test data')\n    plt.xlabel('Predicted values')\n    plt.ylabel('Residuals')\n    plt.legend(loc='upper left')\n    plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')\n    plt.xlim([-10, 50])\n    plt.show()\n\n\ndef ridge_regression():\n    '''\n    regularized regression\n    :return:\n    '''\n    from sklearn.linear_model import Ridge\n    ridge = Ridge(alpha=1.0)\n    from sklearn.linear_model import Lasso\n    lasso = Lasso(alpha=1.0)\n    from sklearn.linear_model import ElasticNet\n    lasso = ElasticNet(alpha=1.0, l1_ratio=0.5)\n\n\ndef polynomial_regression():\n    '''\n    多次问题\n    :return:\n    '''\n    X = np.array([258.0, 270.0, 294.0,\n                  320.0, 342.0, 368.0,\n                  396.0, 446.0, 480.0,\n                  586.0])[:, np.newaxis]\n    y = np.array([236.4, 234.4, 252.8,\n                  298.6, 314.2, 342.2,\n                  360.8, 368.0, 391.2,\n                  390.8])\n    # 1. Add a second degree polynomial term\n    lr = LinearRegression()\n    pr = LinearRegression()\n    # 2次方\n    quadratic = PolynomialFeatures(degree=2)\n    X_quad = quadratic.fit_transform(X)\n\n    # 2.Fit a simple linear regression model for comparison:\n    lr.fit(X, y)\n    X_fit = np.arange(250, 600, 10)[:, np.newaxis]\n    y_lin_fit = lr.predict(X_fit)\n    # 3. Fit a multiple regression model on the transformed features for polynomial regression:\n    pr.fit(X_quad, y)\n    y_quad_fit = pr.predict(quadratic.fit_transform(X_fit))\n    # 4.Plot the results:\n    plt.scatter(X, y, label='training points')\n    plt.plot(X_fit, y_lin_fit, label='linear fit', linestyle='--')\n    plt.plot(X_fit, y_quad_fit, label='quadratic fit')\n    plt.legend(loc='upper left')\n    plt.show()\n    # 5.Predict the outcome\n    y_lin_pred = lr.predict(X)\n    y_quad_pred = pr.predict(X_quad)\n    # Training MSE linear: 569.780, quadratic: 61.330\n    print('Training MSE linear: %.3f, quadratic: %.3f' % (\n        mean_squared_error(y, y_lin_pred), mean_squared_error(y, y_quad_pred)))\n    # Training  R^2 linear: 0.832, quadratic: 0.982\n    print('Training  R^2 linear: %.3f, quadratic: %.3f' % (\n        r2_score(y, y_lin_pred), r2_score(y, y_quad_pred)))\n\n\ndef housing_polynomial_regression():\n    '''\n    housing数据1元多次\n    :return:\n    '''\n    X = df[['LSTAT']].values\n    y = df['MEDV'].values\n    regr = LinearRegression()\n    # create polynomial features\n    quadratic = PolynomialFeatures(degree=2)\n    cubic = PolynomialFeatures(degree=3)\n    X_quad = quadratic.fit_transform(X)\n    X_cubic = cubic.fit_transform(X)\n    # linear fit\n    X_fit = np.arange(X.min(), X.max(), 1)[:, np.newaxis]\n    regr = regr.fit(X, y)\n    y_lin_fit = regr.predict(X_fit)\n    linear_r2 = r2_score(y, regr.predict(X))\n    # quadratic fit\n    regr = regr.fit(X_quad, y)\n    y_quad_fit = regr.predict(quadratic.fit_transform(X_fit))\n    quadratic_r2 = r2_score(y, regr.predict(X_quad))\n    # cubic fit\n    regr = regr.fit(X_cubic, y)\n    y_cubic_fit = regr.predict(cubic.fit_transform(X_fit))\n    cubic_r2 = r2_score(y, regr.predict(X_cubic))\n    # plot results\n    plt.scatter(X, y, label='training points', color='lightgray')\n    plt.plot(X_fit, y_lin_fit, label='linear (d=1), $R^2=%.2f$' % linear_r2, color='blue', lw=2, linestyle=':')\n    plt.plot(X_fit, y_quad_fit, label='quadratic (d=2), $R^2=%.2f$' % quadratic_r2, color='red', lw=2, linestyle='-')\n    plt.plot(X_fit, y_cubic_fit, label='cubic (d=3), $R^2=%.2f$' % cubic_r2, color='green', lw=2, linestyle='--')\n    plt.xlabel('% lower status of the population [LSTAT]')\n    plt.ylabel('Price in $1000\\'s [MEDV]')\n    plt.legend(loc='upper right')\n    plt.show()\n\n\ndef log_feature():\n    '''\n     a log transformation of the LSTAT feature variable and the square root of MEDV\n    :return:\n    '''\n    # transform features\n    X = df[['LSTAT']].values\n    y = df['MEDV'].values\n    X_log = np.log(X)\n    y_sqrt = np.sqrt(y)\n    regr = LinearRegression()\n    # fit features\n    X_fit = np.arange(X_log.min() - 1, X_log.max() + 1, 1)[:, np.newaxis]\n    regr = regr.fit(X_log, y_sqrt)\n    y_lin_fit = regr.predict(X_fit)\n    linear_r2 = r2_score(y_sqrt, regr.predict(X_log))\n    # plot results\n    plt.scatter(X_log, y_sqrt, label='training points', color='lightgray')\n    plt.plot(X_fit, y_lin_fit, label='linear (d=1), $R^2=%.2f$' % linear_r2, color='blue', lw=2)\n    plt.xlabel('log(% lower status of the population [LSTAT])')\n    plt.ylabel('$\\sqrt{Price \\; in \\; \\$1000\\'s [MEDV]}$')\n    plt.legend(loc='lower left')\n    plt.show()\n\n\ndef decision_tree_regression():\n    '''\n    决策树\n    :return:\n    '''\n    from sklearn.tree import DecisionTreeRegressor\n    X = df[['LSTAT']].values\n    y = df['MEDV'].values\n    tree = DecisionTreeRegressor(max_depth=3)\n    tree.fit(X, y)\n    sort_idx = X.flatten().argsort()\n    lin_regplot(X[sort_idx], y[sort_idx], tree)\n    plt.xlabel('% lower status of the population [LSTAT]')\n    plt.ylabel('Price in $1000\\'s [MEDV]')\n    plt.show()\n\n\ndef random_forest_tree_regression():\n    X = df.iloc[:, :-1].values\n    y = df['MEDV'].values\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4, random_state=1)\n    from sklearn.ensemble import RandomForestRegressor\n    forest = RandomForestRegressor(n_estimators=1000, criterion='mse', random_state=1, n_jobs=-1)\n    forest.fit(X_train, y_train)\n    y_train_pred = forest.predict(X_train)\n    y_test_pred = forest.predict(X_test)\n    print('MSE train: %.3f, test: %.3f' % (\n        mean_squared_error(y_train, y_train_pred), mean_squared_error(y_test, y_test_pred)))\n    print('R^2 train: %.3f, test: %.3f' % (\n        r2_score(y_train, y_train_pred), r2_score(y_test, y_test_pred)))\n    # MSE train: 3.235, test: 11.635\n    # R^2 train: 0.960, test: 0.871\n    plt.scatter(y_train_pred, y_train_pred - y_train, c='black', marker='o', s=35, alpha=0.5, label='Training data')\n    plt.scatter(y_test_pred, y_test_pred - y_test, c='lightgreen', marker='s', s=35, alpha=0.7, label='Test data')\n    plt.xlabel('Predicted values')\n    plt.ylabel('Residuals')\n    plt.legend(loc='upper left')\n    plt.hlines(y=0, xmin=-10, xmax=50, lw=2, color='red')\n    plt.xlim([-10, 50])\n    plt.show()\n\n# look_data()\n# visualize_data()\n# corelation_data()\n# linear_model()\n# ransac_fit(X, y)\nmulti_regression()\n# polynomial_regression()\n# housing_polynomial_regression()\n# log_feature()\n# decision_tree_regression()\n# random_forest_tree_regression()","sub_path":"ch10/housing.py","file_name":"housing.py","file_ext":"py","file_size_in_byte":11690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"634471103","text":"'''\nSi definiscono divisori propri di un numero tutti i suoi divisori tranne l'uno e il numero stesso.\nScrivere una funzione modi(ls,k) che, presa una lista ls di interi  ed un intero \nnon negativo k:\n    1) cancella  dalla lista ls gli interi che non hanno esattamente k divisori propri\n    2) restituisce una seconda lista che contiene i soli numeri primi di ls.\nNOTA: un numero maggiore di 1 e' primo se ha 0 divisori propri.\n\nad esempio per ls = [121, 4, 37, 441, 7, 16] \nmodi(ls,3) restituisce la lista con i numeri primi [37,7] mentre al termine della funzione si avra' che la lista ls=[16]\n\nPer altri  esempi vedere il file grade.txt\n\nATTENZIONE: NON USATE LETTERE ACCENTATE.\nATTENZIONE: Se il grader non termina entro 30 secondi il punteggio dell'esercizio e' zero.\n'''\n\nimport random\n\ndef modi(ls,k):\n    primels = []\n    iterablels = ls[:]\n    \n    for number in iterablels:\n        counter = 0\n        if is_prime(number):        #controllo numero primo\n            primels.append(number)\n        else:   #else calcolo divisori\n            counter = 1\n            div = 0\n            divpow = {}\n            ntoprime = number\n            \n            while ntoprime > 1:\n                \n                if is_prime(ntoprime):\n                    div = ntoprime\n                else:\n                    div = next_prime(div)\n                    \n                divpow[div] = 0\n                \n                while not ntoprime % div:\n                    ntoprime //= div\n                    divpow[div] += 1\n                    \n                divpow[div] += 1\n                counter *= divpow[div]\n                \n                if counter-2 > k: #trovati piu' divisori di quanto indicati\n                    break\n                \n        if counter-2 != k:  #divisori pari a quanto indicati\n            ls.remove(number)\n    return primels\n\n\ndef next_prime(n):\n    if n < 2:\n        return 2\n \n    if n & 1:\n        n += 2\n    else:\n        n += 1\n        \n    while not is_prime(n):\n        n += 2\n        \n    return n\n\n#*********************************************************************************************\n#primes between 1 and 1000\nprimeList = [2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97,\n             103,107,109,113,127,131,137,139,149,151,157,163,167,173,179,\n             181,191,193,197,199,211,223,227,229,233,239,241,251,257,263,269,\n             271,277,281,283,293,307,311,313,317,331,337,347,349,353,359,367,\n             373,379,383,389,397,401,409,419,421,431,433,439,443,449,457,461,\n             463,467,479,487,491,499,503,509,521,523,541,547,557,563,569,571,\n             577,587,593,599,601,607,613,617,619,631,641,643,647,653,659,661,\n             673,677,683,691,701,709,719,727,733,739,743,751,757,761,769,773,\n             787,797,809,811,821,823,827,829,839,853,857,859,863,877,881,883,\n             887,907,911,919,929,937,941,947,953,967,971,977,983,991,997]\n\n\n#prime test\ndef is_prime(number):\n    if number == 2:\n        return True\n    elif number > 2:\n        if number & 1: #test divisibilita per 2\n            for prime in primeList: #divisibilita altri primi\n                if number == prime:\n                    return True\n                if not number % prime:\n                    return False\n                if number > prime//2+1:\n                    break\n        else:\n            return False\n    return rabin_miller(number)\n\n\n#-------------------------------------------------\n#rabin miller test\ndef rabin_miller(n):\n    if n < 3:\n        return False\n    \n    d = n - 1\n    while not d & 1:\n        d = d >> 1\n    \n\n    r = random.randrange(2,n)\n    p = modular_pow(r,d,n)\n\n    if p == 1 or p == n-1:\n        return True\n        \n    while d != n-1:\n        p = modular_pow(p,2,n)\n        if p == 1:\n            return False\n        elif p == n-1:\n            return True\n        d = d << 1\n    \n    return False\n    \n    \n\ndef modular_pow(x, y, m):\n    res = 1\n    x = x % m\n    \n    while y > 0:\n        if y & 1:\n            res = (res * x) % m\n        y = y >> 1\n        x = (x * x) % m\n        \n    return res\n#-------------------------------------------------\n#*********************************************************************************************","sub_path":"students/1816079/homework01/program01.py","file_name":"program01.py","file_ext":"py","file_size_in_byte":4266,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"258260101","text":"# -*- coding: UTF-8 -*-\n\n\"\"\"\nGoogle Safebrowsing Plugin\nThis plugin does not query Google Safebrowsing database directly.\nInstead it depends on gglsbl-rest API service\nhttps://github.com/mlsecproject/gglsbl-rest\n\nDepending on what malware type hit, mail can be\n- rejected\n- tagged as virus, spam or blocked (needs custom evaluation plugin)\n- tagged with a pseudo header for evaluation in plugins such as SpamAssassin.\n\nSpamAssassin sample rule for tagged messages:\nheader          FUGLU_SAFEBROWSING_MALWARE        X-Safebrowsing =~ /^MALWARE$/\ndescribe        FUGLU_SAFEBROWSING_MALWARE        URI detected by Google Safebrowsing\nscore           FUGLU_SAFEBROWSING_MALWARE        25.0\n\"\"\"\n\n\nfrom fuglu.shared import ScannerPlugin, DUNNO, REJECT, DEFER, string_to_actioncode, apply_template\nimport logging\nimport json\ntry:\n    import urlparse\n    from urllib import quote as urllib_quote\n    from urllib2 import urlopen, HTTPError\nexcept ImportError: #py3\n    from urllib import parse as urlparse\n    from urllib.parse import quote as urllib_quote\n    from urllib.request import urlopen, HTTPError\n\n\n\nBASE_PATH = u'gglsbl/v1/'\nLOOKUP = u'lookup/'\nSTATUS = u'status'\nSAMPLE_URL = 'http://testsafebrowsing.appspot.com/apiv4/LINUX/SOCIAL_ENGINEERING/URL/'\n\n\n\n# https://developers.google.com/safe-browsing/v4/reference/rest/v4/ThreatType\nTT_NA = u'THREAT_TYPE_UNSPECIFIED'\nTT_MW = u'MALWARE'\nTT_SE = u'SOCIAL_ENGINEERING'\nTT_PUA = u'UNWANTED_SOFTWARE'\nTT_PHA = u'POTENTIALLY_HARMFUL_APPLICATION'\n\n\n\nclass GGLSBLClient(object):\n    \n    def __init__(self, baseurl, timeout=3):\n        self.baseurl = baseurl\n        self.timeout=timeout\n        self.logger = logging.getLogger('%s.safebrowseclient' % __package__)\n    \n    \n    \n    def _quote(self, url):\n        return urllib_quote(url)\n    \n    \n    \n    def _query(self, url):\n        data = ''\n        code = 0\n        try:\n            fp = urlopen(url, timeout=self.timeout)\n            data = fp.read()\n            code = fp.getcode()\n            fp.close()\n        except HTTPError as e:\n            code = e.code\n            if code != 404:\n                self.logger.debug('query failed with code %s: %s' % (code, str(e)))\n        except Exception as e:\n            self.logger.error('query failed: %s' % str(e))\n        return data, code\n    \n    \n    \n    def _convert(self, jsondata):\n        data = u''\n        try:\n            if isinstance(jsondata, bytes): # python3\n                jsondata = jsondata.decode('utf-8')\n            data = json.loads(jsondata)\n        except Exception as e:\n            self.logger.error('data conversion failed: %s' % str(e))\n        return data\n    \n    \n    \n    def _urljoin(self, *parts):\n        base = ''\n        for part in parts:\n            base = urlparse.urljoin(base, part)\n        return base\n    \n    \n    \n    def status(self):\n        queryurl = self._urljoin(self.baseurl, BASE_PATH, STATUS)\n        jsondata, code = self._query(queryurl)\n        if code == 200:\n            data = self._convert(jsondata) # type: dict\n        else:\n            data = None\n        return data\n    \n    \n    \n    def lookup(self, url):\n        try:\n            url = self._quote(url)\n        except Exception as e:\n            self.logger.error('Invalid URL %s : %s' % (url, str(e)))\n            return None\n        \n        queryurl = self._urljoin(self.baseurl, BASE_PATH, LOOKUP, url)\n        jsondata, code = self._query(queryurl)\n        if code == 200:\n            data = self._convert(jsondata) # type: dict\n        elif code == 404:\n            data = {}\n        else:\n            data = None\n        return data\n    \n    \n    \n    def assemble(self, data):\n        threat_types = []\n        if data is None:\n            return threat_types\n        if 'matches' in data:\n            for item in data['matches']:\n                if 'threat' in item:\n                    threat = item['threat']\n                    if threat not in threat_types:\n                        threat_types.append(threat)\n        return threat_types\n    \n    \n    \n    def lint(self):\n        success = False\n        data = self.lookup(SAMPLE_URL)\n        if data is None:\n            print('query error')\n        elif data == {}:\n            print('no threat found')\n        else:\n            success = True\n            print('sample threat found - your gglsbl-rest is up, running and reachable')\n        return success\n\n\n\nclass Safebrowsing(ScannerPlugin):\n    def __init__(self, section=None):\n        ScannerPlugin.__init__(self, section)\n        self.logger = self._logger()\n        \n        self.requiredvars = {\n            'baseurl': {\n                'default': 'http://127.0.0.1:5000/',\n                'description': 'base URL of your gglsbl-rest instance',\n            },\n            \n            'timeout': {\n                'default': '3',\n                'description': 'maximum REST API query time in seconds',\n            },\n\n            'problemaction': {\n                'default': 'DEFER',\n                'description': \"action if there is a problem (DUNNO, DEFER)\",\n            },\n            \n            'maxlookups':{\n                'default':'10',\n                'description':'maximum number of URIs to check per message',\n            },\n            \n            'stop_on_first_hit':{\n                'default':'True',\n                'description':'stop queries on first hit. else check up to maxlookups URIs',\n            },\n            \n            'threats_reject':{\n                'default':'',\n                'description':'reject if at least one URL has one of these threat types (only use in prequeue mode)',\n            },\n            \n            'threats_virus':{\n                'default':'',\n                'description':'tag as virus if at least one URL has one of these threat types',\n            },\n            \n            'threats_block':{\n                'default':'',\n                'description':'tag as blocked if at least one URL has one of these threat types',\n            },\n            \n            'threats_highspam':{\n                'default':'',\n                'description':'tag as spam if at least one URL has one of these threat types',\n            },\n            \n            'threats_spam':{\n                'default':'',\n                'description':'tag as spam if at least one URL has one of these threat types',\n            },\n            \n            'threats_tag':{\n                'default':'THREAT_TYPE_UNSPECIFIED MALWARE SOCIAL_ENGINEERING UNWANTED_SOFTWARE POTENTIALLY_HARMFUL_APPLICATION',\n                'description':'add a tag for SpamAssassin if at least one URL has one of these threat types',\n            },\n            \n            'eval_order':{\n                'default':'threats_reject threats_virus threats_block threats_highspam threats_spam threats_tag',\n                'description':'in which order should above threat actions be applied',\n            },\n\n            'rejectmessage': {\n                'default': 'message identified as containing ${threat}',\n                'description': \"reject message template if running in pre-queue mode\",\n            },\n\n            'virusaction': {\n                'default': 'DEFAULTVIRUSACTION',\n                'description': \"action if considered threats_virus (DUNNO, REJECT, DELETE)\",\n            },\n            \n            'max_tag_size': {\n                'default': '256000',\n                'description': \"maximum size in bytes of messages to tag for spamassasin. should be equal to maxsize settings of spamassassin. larger messages will be marked as spam.\",\n            },\n        \n        }\n        \n        self.sbc = None\n    \n    \n    \n    def _init_sbc(self):\n        if self.sbc is None:\n            baseurl = self.config.get(self.section, 'baseurl')\n            timeout = self.config.getint(self.section, 'timeout')\n            self.sbc = GGLSBLClient(baseurl, timeout)\n        \n        \n        \n    def lint(self):\n        allok = self.check_config()\n        if self.config.getint(self.section, 'max_tag_size') > self.config.getint('SAPlugin', 'maxsize'):\n            print('max_tag_size should not be larger than Spamassassin maxsize')\n            allok = False\n        \n        if allok:\n            self._init_sbc()\n            allok = self.sbc.lint()\n        return allok\n    \n    \n    \n    def examine(self, suspect):\n        action = DUNNO\n        message = None\n        self._init_sbc()\n        uris = suspect.get_tag('body.uris')\n        if uris is None:\n            return DUNNO\n        \n        maxlookups = self.config.getint(self.section, 'maxlookups')\n        stop_on_first_hit = self.config.getboolean(self.section, 'stop_on_first_hit')\n        \n        error, threat_types = self._lookup(uris, maxlookups, stop_on_first_hit)\n        if error and not threat_types:\n            self.logger.error('failed to query gglsbl-rest server')\n            action = self._problemcode()\n            message = 'Internal error'\n        elif threat_types:\n            reject, virus, block, highspam, spam, tag = self._eval_threats(threat_types)\n            self.logger.info('%s query result: reject=%s virus=%s block=%s highspam=%s spam=%s tag=%s' % (suspect.id, reject, virus, block, highspam, spam, tag))\n            if reject is not None:\n                action = REJECT\n                message = self._rejectmessage(suspect, reject.get('threat'), reject.get('uri'))\n                self.logger.info('%s do reject' % suspect.id)\n            if virus:\n                suspect.tags['virus']['safebrowsing'] = True\n                suspect.tags['safebrowsing.result.virus'] = virus\n                virusaction = self.config.get(self.section, 'virusaction')\n                action = string_to_actioncode(virusaction, self.config)\n                for threat in virus:\n                    message = self._rejectmessage(suspect, threat, virus[threat][0])\n                self.logger.info('%s mark as virus' % suspect.id)\n            if block:\n                suspect.tags['blocked']['safebrowsing'] = True\n                suspect.set_tag('safebrowsing.blocked', True)\n                suspect.tags['safebrowsing.result.blocked'] = block\n                self.logger.info('%s mark as blocked' % suspect.id)\n            if highspam:\n                suspect.tags['highspam']['safebrowsing'] = True\n                suspect.tags['safebrowsing.result.highspam'] = spam\n                self.logger.info('%s mark as spam' % suspect.id)\n            if spam or tag and suspect.size > self.config.get(self.section, 'max_tag_size'):\n                suspect.tags['spam']['safebrowsing'] = True\n                suspect.tags['safebrowsing.result.spam'] = spam\n                self.logger.info('%s mark as spam' % suspect.id)\n            if tag:\n                for threat in tag:\n                    self._writeheader(suspect, 'X-Safebrowsing', threat)\n                self.logger.info('%s add tag' % suspect.id)\n        #else:\n        #    self.logger.info('%s no safebrowsing threat found' % suspect.id)\n            \n        return action, message\n    \n    \n    \n    def _rejectmessage(self, suspect, threat, uri):\n        values = dict(threat=threat, uri=uri)\n        message = apply_template(self.config.get(self.section, 'rejectmessage'), suspect, values)\n        return message\n    \n    \n    \n    def _writeheader(self, suspect, header, value):\n        hdr = \"%s: %s\" % (header, value)\n        tag = suspect.get_tag('SAPlugin.tempheader')\n        if isinstance(tag, list):\n            tag.append(hdr)\n        elif tag is None:\n            tag = [hdr, ]\n        else:  # str/unicode\n            tag = \"%s\\r\\n%s\" % (tag, hdr)\n        suspect.set_tag('SAPlugin.tempheader', tag)\n    \n    \n    \n    def _eval_threats(self, threat_types):\n        reject = None\n        virus = {}\n        block = {}\n        highspam = {}\n        spam = {}\n        tag = {}\n        \n        eval_order = [x.strip() for x in self.config.get(self.section, 'eval_order').replace(',', ' ').split()]\n        for item in eval_order:\n            threats = [x.strip() for x in self.config.get(self.section, item).replace(',', ' ').split()]\n            for threat in threats:\n                if threat in threat_types.keys():\n                    uri = threat_types[threat][0]\n                    if item == 'threats_reject':\n                        reject = dict(threat=threat, uri=uri)\n                    elif item == 'threats_virus':\n                        if threat not in virus:\n                            virus[threat] = []\n                        virus[threat].append(uri)\n                    elif item == 'threats_block':\n                        if threat not in block:\n                            block[threat] = []\n                        block[threat].append(uri)\n                    elif item == 'threats_highspam':\n                        if threat not in highspam:\n                            highspam[threat] = []\n                        highspam[threat].append(uri)\n                    elif item == 'threats_spam':\n                        if threat not in spam:\n                            spam[threat] = []\n                        spam[threat].append(uri)\n                    elif item == 'threats_tag':\n                        if threat not in tag:\n                            tag[threat] = []\n                        tag[threat].append(uri)\n                    break\n            if reject or virus or block or spam or tag:\n                break\n        \n        return reject, virus, block, highspam, spam, tag\n    \n    \n    \n    def _lookup(self, uris, maxlookups=10, stop_on_first_hit=True):\n        error = False\n        \n        threat_types = {}\n        for uri in uris[:maxlookups]:\n            data = self.sbc.lookup(uri)\n            if data is None: # lookup error\n                error = True\n                break\n            threats = self.sbc.assemble(data)\n            for threat in threats:\n                if not threat in threat_types:\n                    threat_types[threat] = []\n                threat_types[threat].append(uri)\n            if threat_types and stop_on_first_hit:\n                break\n        \n        return error, threat_types\n    \n    \n    \n    def _problemcode(self):\n        retcode = string_to_actioncode(\n            self.config.get(self.section, 'problemaction'), self.config)\n        if retcode is not None:\n            return retcode\n        else:\n            # in case of invalid problem action\n            return DEFER\n    \n    \n    \n    def __str__(self):\n        return \"GGLSBL Safebrowsing\"\n    \n    \n","sub_path":"safebrowsing/gglsbl.py","file_name":"gglsbl.py","file_ext":"py","file_size_in_byte":14563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"530922840","text":"# Thanks StackOverflow for some of this code.\n# Note from other guy who suddenly joined for no reason: I know right. StackOverflow is god.\n\n\ndef turn_list_into_integers(list_):\n    \"\"\"\n    Turns all items inside a list into integers.\n    :param list_: The list to turn into integers\n    :return:\n    \"\"\"\n    result = []\n    for item in list_:\n        result.append(int(item))\n\n    return result\n\n\ndef main():\n    while True:\n        numbers = turn_list_into_integers(input('Type in your numbers, and seperate them with a \",\" to get the mean:')\n                                          .split(','))\n        average = sum(numbers)/int(len(numbers))\n        print('Average: {}'.format(average))\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Mean Calculator.py","file_name":"Mean Calculator.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"227823298","text":"import logging\nimport colorlog\n\ndef init_logger(dunder_name, testing_mode) -> logging.Logger:\n    log_format = (\n        '%(name)s - '\n        '%(levelname)s - '\n        '%(message)s'\n    )\n    bold_seq = '\\033[1m'\n    colorlog_format = (\n        f'{bold_seq} '\n        '%(log_color)s '\n        f'{log_format}'\n    )\n    colorlog.basicConfig(format=colorlog_format)\n    logger = logging.getLogger(dunder_name)\n\n    if testing_mode:\n        logger.setLevel(logging.DEBUG)\n    else:\n        logger.setLevel(logging.INFO)\n\n    return logger","sub_path":"helpers/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"548710357","text":"from sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.datasets import load_boston\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import mean_squared_error\n\nboston = load_boston()\nx_train, x_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.2, random_state=1)\nstd_x = StandardScaler()\nx_train = std_x.fit_transform(x_train)\nx_test = std_x.transform(x_test)\nstd_y = StandardScaler()\ny_train = std_y.fit_transform(y_train.reshape(-1, 1))\ny_test = std_y.transform(y_test.reshape(-1, 1))\nlr = LinearRegression()\nlr.fit(x_train, y_train)\nprint('權重值：{}'.format(lr.coef_)) \nprint('偏置值：{}\\n'.format(lr.intercept_))\n\ny_predict = std_y.inverse_transform(lr.predict(x_test))\ny_real = std_y.inverse_transform(y_test)\nfor i in range(5):\n    print('預測值{}：{}，真實值：{}'.format(i+1, y_predict[i], y_real[i]))\nmerror = mean_squared_error(y_real, y_predict)\nprint('平均方差：{}'.format(merror))\n","sub_path":"_4.python/__code/Python自學聖經(第二版)/ch23/linearRegress.py","file_name":"linearRegress.py","file_ext":"py","file_size_in_byte":1028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"93921314","text":"import datetime\nfrom django_rq import job\nfrom dateutil.parser import parse\n\nfrom tunga.settings import SLACK_STAFF_INCOMING_WEBHOOK, SLACK_STAFF_PROFILES_CHANNEL, SLACK_ATTACHMENT_COLOR_GREEN, \\\n    SLACK_ATTACHMENT_COLOR_BLUE, SLACK_STAFF_HUBSPOT_CHANNEL, SLACK_STAFF_LEADS_CHANNEL\nfrom tunga_utils import slack_utils, hubspot_utils\nfrom tunga_utils.helpers import clean_instance\nfrom tunga_utils.models import InviteRequest, ExternalEvent\n\n\n@job\ndef notify_new_invite_request_slack(invite_request):\n    invite_request = clean_instance(invite_request, InviteRequest)\n\n    slack_msg = \"<!channel> {} wants to join Tunga\".format(\n        invite_request.name\n    )\n\n    attachments = [\n        {\n            slack_utils.KEY_TITLE: invite_request.name,\n            slack_utils.KEY_TITLE_LINK: invite_request.cv_url,\n            slack_utils.KEY_TEXT: '*Name:* {}\\n*Email:* {}\\n*Country*: {}\\n<{}|Download CV>'.format(\n                invite_request.name,\n                invite_request.email,\n                invite_request.country.name,\n                invite_request.cv_url\n            ),\n            slack_utils.KEY_MRKDWN_IN: [slack_utils.KEY_TEXT],\n            slack_utils.KEY_COLOR: SLACK_ATTACHMENT_COLOR_GREEN,\n        },\n        {\n            slack_utils.KEY_TITLE: 'Motivation',\n            slack_utils.KEY_TEXT: invite_request.motivation,\n            slack_utils.KEY_MRKDWN_IN: [slack_utils.KEY_TEXT],\n            slack_utils.KEY_COLOR: SLACK_ATTACHMENT_COLOR_BLUE,\n        }\n    ]\n\n    slack_utils.send_incoming_webhook(\n        SLACK_STAFF_INCOMING_WEBHOOK,\n        {\n            slack_utils.KEY_TEXT: slack_msg,\n            slack_utils.KEY_ATTACHMENTS: attachments,\n            slack_utils.KEY_CHANNEL: SLACK_STAFF_PROFILES_CHANNEL\n        }\n    )\n\n\n@job\ndef notify_hubspot_deal_changes_slack(deal_id, changes, event_ids=None):\n    deal_url = None\n    final_subscription_type = None\n\n    deal_details = hubspot_utils.get_deal(deal_id)\n\n    if deal_details and deal_details.get('properties', None):\n        deal_properties = deal_details['properties']\n        deal_name = deal_properties.get(hubspot_utils.KEY_DEALNAME, {})['value'] or ''\n\n        deal_property_changes = []\n        deal_property_details = hubspot_utils.get_deal_properties()\n        deal_pipelines = hubspot_utils.get_deal_pipelines()\n\n        for payload in changes:\n            if deal_id != payload.get(hubspot_utils.KEY_OBJECT_ID):\n                # Ignore changes from other deals\n                continue\n\n            subscription_type = payload.get(hubspot_utils.KEY_SUBSCRIPTION_TYPE)\n            if subscription_type in [\n                hubspot_utils.KEY_VALUE_DEAL_CREATED,\n                hubspot_utils.KEY_VALUE_DEAL_DELETION,\n                hubspot_utils.KEY_VALUE_DEAL_PROPERTY_CHANGE\n            ]:\n                if subscription_type != hubspot_utils.KEY_VALUE_DEAL_PROPERTY_CHANGE or not final_subscription_type:\n                    final_subscription_type = subscription_type\n\n                if not deal_url:\n                    deal_url = 'https://app.hubspot.com/sales/{}/deal/{}/'.format(\n                        payload.get(hubspot_utils.KEY_PORTAL_ID),\n                        deal_id\n                    )\n\n                if deal_properties and subscription_type == hubspot_utils.KEY_VALUE_DEAL_PROPERTY_CHANGE:\n                    current_deal_property_name = payload.get(hubspot_utils.KEY_PROPERTY_NAME, '')\n                    if current_deal_property_name != hubspot_utils.KEY_DEALSTAGE:\n                        # Deal stage is already shown\n                        current_property_value = payload.get(hubspot_utils.KEY_PROPERTY_VALUE, '')\n\n                        display_property_label, display_property_value = hubspot_utils.clean_property(\n                            current_deal_property_name, current_property_value,\n                            deal_details, deal_property_details, deal_pipelines\n                        )\n\n                        if current_deal_property_name:\n                            deal_property_changes.append(\n                                '*{}:* {}'.format(\n                                    display_property_label,\n                                    display_property_value\n                                )\n                            )\n\n        if deal_url and final_subscription_type:\n            current_deal_stage = deal_properties.get(hubspot_utils.KEY_DEALSTAGE, {})['value']\n            display_deal_stage_label, display_deal_stage_value = hubspot_utils.clean_property(\n                hubspot_utils.KEY_DEALSTAGE, current_deal_stage,\n                deal_details, deal_property_details, deal_pipelines\n            )\n\n            slack_utils.send_incoming_webhook(\n                SLACK_STAFF_INCOMING_WEBHOOK,\n                {\n                    slack_utils.KEY_CHANNEL: SLACK_STAFF_HUBSPOT_CHANNEL,\n                    slack_utils.KEY_TEXT: '{} in HubSpot | <{}|View details>'.format(\n                        final_subscription_type == hubspot_utils.KEY_VALUE_DEAL_CREATED and 'New deal created' or (\n                            'Deal {}'.format(final_subscription_type == hubspot_utils.KEY_VALUE_DEAL_DELETION and 'deleted' or 'updated')\n                        ),\n                        deal_url\n                    ),\n                    slack_utils.KEY_ATTACHMENTS: [\n                        {\n                            slack_utils.KEY_TITLE: deal_name,\n                            slack_utils.KEY_TITLE_LINK: deal_url,\n                            slack_utils.KEY_TEXT: '*Deal Stage:* {}{}'.format(\n                                display_deal_stage_value or 'Unknown',\n                                deal_property_changes and '\\n\\n{}'.format('\\n'.join(deal_property_changes)) or ''\n                            ),\n                            slack_utils.KEY_MRKDWN_IN: [slack_utils.KEY_TEXT],\n                            slack_utils.KEY_COLOR: SLACK_ATTACHMENT_COLOR_GREEN\n                        }\n                    ]\n                }\n            )\n\n            if event_ids:\n                ExternalEvent.objects.filter(id__in=event_ids).update(notification_sent_at=datetime.datetime.utcnow())\n\n\n@job\ndef notify_new_calendly_event(data):\n    event_details = data.get('event', None)\n    start_time = parse(event_details.get('start_time', None))\n\n    invitee_details = data.get('invitee', dict())\n    invitee_name = invitee_details.get('name')\n\n    questions_and_answers = data.get('questions_and_answers', [])\n\n    slack_utils.send_incoming_webhook(\n        SLACK_STAFF_INCOMING_WEBHOOK,\n        {\n            slack_utils.KEY_CHANNEL: SLACK_STAFF_LEADS_CHANNEL,\n            slack_utils.KEY_TEXT: '*{}* scheduled a call with Calendly'.format(\n                invitee_name\n            ),\n            slack_utils.KEY_ATTACHMENTS: [\n                {\n                    slack_utils.KEY_TEXT: '\\n'.join(\n                        ['*{}:* {}'.format(item[0], item[1]) for item in\n                         [\n                             ['Name', invitee_name],\n                             ['Email', invitee_details.get('email')],\n                             ['Start Time', '{} at {} UTC'.format(\n                                 start_time.strftime(\"%a, %d %b, %Y\"),\n                                 start_time.strftime(\"%I:%M %p\")\n                             )]\n                         ]]),\n                    slack_utils.KEY_MRKDWN_IN: [slack_utils.KEY_TEXT],\n                    slack_utils.KEY_COLOR: SLACK_ATTACHMENT_COLOR_GREEN\n                },\n                {\n                    slack_utils.KEY_TEXT: '\\n'.join(\n                        ['*{}:*\\n{}'.format(item.get('question'), item.get('answer')) for item in questions_and_answers]\n                    ),\n                    slack_utils.KEY_MRKDWN_IN: [slack_utils.KEY_TEXT],\n                    slack_utils.KEY_COLOR: SLACK_ATTACHMENT_COLOR_GREEN\n                }\n            ]\n        }\n    )\n","sub_path":"tunga_utils/notifications/slack.py","file_name":"slack.py","file_ext":"py","file_size_in_byte":7932,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"139563384","text":"# <<BEGIN-copyright>>\n# Copyright 2022, Lawrence Livermore National Security, LLC.\n# See the top-level COPYRIGHT file for details.\n# \n# SPDX-License-Identifier: BSD-3-Clause\n# <<END-copyright>>\n\n\"\"\"\nThis module defines the PoPs database class, which is the top-level container in PoPs.\nA database is a collection of particles, each with a unique id.\nIt may also store documentation and 'style' information.\n\nWithin the database, particles are organized by family (i.e. gauge bosons, leptons, baryons or chemical elements).\nFor nuclear data users, the largest part of the database will likely be inside the chemical elements section\nwhere isotopes, nuclides and excited nuclear states are defined.\n\nThe database supports looking up particles by their id. For example, if 'pops' is a PoPs.database instance that\ncontains a particle with id 'Al27',\n\n>>> 'Al27' in pops\nTrue\n>>> type( pops['Al27'] )\n<class PoPs.families.nuclide.particle>\n>>> pops.keys()   # list all particle ids in the database\n['photon', 'n', ... 'Al28', 'Al26_m1', 'Na24_m1']\n\nThe particle database may also define aliases. These can be used to refer to a particle by another name.\nFor example, the id for the electron is normally \"e-\", but could also be aliased to 'e' or 'electron'.\nIn the example above, 'Al26_m1' and 'Na24_m1' are aliases pointing to excited nuclides.\n\n>>> type( pops['Na24_m1'] )\n<class 'PoPs.families.nuclide.alias'>\n\nEach alias has a 'pid' field with the id of the actual particle it points to:\n\n>>> pops['Na24_m1'].pid\n'Na24_e1'\n\nIn this case, the first metastable state of Na24 is also the first excited state (as indicated by the '_e1' suffix).\n\nA PoPs database has methods to write itself out to an XML-formatted file or string, to read back in from XML,\nto convert units, etc.\n\nOne PoPs database may point to another parent database. In that case, if a particle is not found in the current\ndatabase it will also check in the parent database before throwing a KeyError.\nThis option allows an evaluator to override part of the parent database (i.e. to define a different mass\nfor one or more particles).\n\"\"\"\n\nimport os\n\nfrom LUPY import ancestry as ancestryModule\n\nfrom xData.Documentation import documentation as documentationModule\nfrom fudge import GNDS_formatVersion as GNDS_formatVersionModule\n\nfrom .chemicalElements import chemicalElements as chemicalElementsModule\nfrom .chemicalElements import chemicalElement as chemicalElementModule\nfrom .chemicalElements import isotope as isotopeModule\nfrom .chemicalElements import misc as chemicalElementsMiscModule\nfrom .families import particle as particleModule\nfrom .families import gaugeBoson as gaugeBosonModule\nfrom .families import lepton as leptonModule\nfrom .families import baryon as baryonModule\nfrom .families import nuclide as nuclideModule\nfrom .families import nucleus as nucleusModule\nfrom .families import unorthodox as unorthodoxModule\n\nfrom . import IDs as IDsModule\nfrom . import misc as miscModule\nfrom . import styles as stylesModule\nfrom . import alias as aliasModule\n\nclass Database(ancestryModule.AncestryIO):\n\n    moniker = 'PoPs'\n    ancestryMembers = ( 'styles', 'documentation', 'aliases', 'gaugeBosons', 'leptons', 'baryons', 'chemicalElements', 'unorthodoxes' )\n\n    def __init__( self, name, version, formatVersion = GNDS_formatVersionModule.default ) :\n        \"\"\"\n        :param name: string naming the database\n        :param version: database version\n        :param formatVersion: the GNDS format the data are represented in.\n        \"\"\"\n\n        ancestryModule.AncestryIO.__init__(self)\n\n        self.__format = formatVersion\n\n        self.name = name\n\n        self.version = version\n\n        self.__styles = stylesModule.Styles( )\n        self.__styles.setAncestor( self )\n\n        self.__documentation = documentationModule.Documentation( )\n        self.__documentation.setAncestor( self )\n\n        self.__aliases = aliasModule.Suite( )\n        self.__aliases.setAncestor( self )\n\n        self.__gaugeBosons = gaugeBosonModule.Suite( )\n        self.__gaugeBosons.setAncestor( self )\n\n        self.__leptons = leptonModule.Suite( )\n        self.__leptons.setAncestor( self )\n\n        self.__baryons = baryonModule.Suite( )\n        self.__baryons.setAncestor( self )\n\n        self.__chemicalElements = chemicalElementsModule.ChemicalElements( )\n        self.__chemicalElements.setAncestor( self )\n\n        self.__unorthodoxes = unorthodoxModule.Suite( )\n        self.__unorthodoxes.setAncestor( self )\n\n    def __contains__( self, key ) :\n\n        if( key in self.__aliases ) : return( True )\n        if( key in self.__gaugeBosons ) : return( True )\n        if( key in self.__leptons ) : return( True )\n        if( key in self.__baryons ) : return( True )\n        if( key in self.__chemicalElements ) : return( True )\n        if( key in self.__unorthodoxes ) : return( True )\n        parentDB = self.parentDatabase()\n        if parentDB is not None:\n            return key in parentDB\n        return( False )\n\n    def __getitem__( self, key ) :\n        \"\"\"\n        :param key: id or symbol of a particle or particle group\n\n        :return: particle with given key. KeyError is raised if the key is not found.\n        \"\"\"\n\n        if( '{' in key ) : key = key.split( '{' )[0]\n        return( self.__getitem( key, 0 ) )\n\n    def __getitem( self, key, level ) :\n\n        if( not( isinstance( key, str ) ) ) : raise TypeError( 'key must be a str' )\n        if( key in self.__aliases ) :\n            alias = self.__aliases[key]\n            item = self.__getitem( alias.pid, level + 1 )\n            if( level == 0 ) : item = item.alias(key, item)\n            return( item )\n        for suite in [ self.__gaugeBosons, self.__leptons, self.__baryons, self.__chemicalElements, self.__unorthodoxes ] :\n            try :\n                return( suite[key] )\n            except KeyError :\n                continue\n            except :\n                raise\n        parentDB = self.parentDatabase()\n        if parentDB is not None:\n            return parentDB[key]\n        raise KeyError( 'id = \"%s\" not found' % key )\n\n    def __iter__( self ) :\n        \"\"\"\n        Iterates over all particles in the database. Particle groups like the\n        chemicalElement and isotope classes are not included, although the particles they contain are.\n        \"\"\"\n\n        for gaugeBoson in self.__gaugeBosons : yield gaugeBoson\n        for lepton in self.__leptons : yield lepton\n        for baryon in self.__baryons : yield baryon\n        for chemicalElement in self.__chemicalElements :\n            for isotope in chemicalElement :\n                for nuclide in isotope :\n                    yield nuclide\n                    yield nuclide.nucleus\n        for item in self.__unorthodoxes : yield item\n        for item in self.__aliases : yield item\n        parentDB = self.parentDatabase()\n        if parentDB is not None:\n            for item in parentDB: yield item    # FIXME skip particles that are overridden in child database?\n\n    def keys( self ) :\n        \"\"\"Return a list of ids for all particles in self.\"\"\"\n\n        return( [ particle.id for particle in self ] )\n\n    @property\n    def format( self ) :\n\n        return( self.__format )\n\n    @property\n    def name( self ) :\n\n        return( self.__name )\n\n    @name.setter\n    def name( self, value ) :\n\n        if( not( isinstance( value, str ) ) ) : raise TypeError( 'name must be a string' )\n        self.__name = value\n\n    @property\n    def version( self ) :\n\n        return( self.__version )\n\n    @version.setter\n    def version( self, value ) :\n\n        if( not( isinstance( value, str ) ) ) : raise TypeError( 'version must be a string' )\n        self.__version = value\n\n    @property\n    def styles( self ) :\n\n        return( self.__styles )\n\n    @property\n    def gaugeBosons( self ) :\n\n        return( self.__gaugeBosons )\n\n    @property\n    def leptons( self ) :\n\n        return( self.__leptons )\n\n    @property\n    def baryons( self ) :\n\n        return( self.__baryons )\n\n    @property\n    def chemicalElements( self ) :\n\n        return( self.__chemicalElements )\n\n    @property\n    def unorthodoxes( self ) :\n\n        return( self.__unorthodoxes )\n\n    @property\n    def aliases( self ) :\n\n        return( self.__aliases )\n\n    def parentDatabase(self):\n        \"\"\"\n        Search up ancestry chain for another PoPs database.\n        Return PoPs.database if found, otherwise return None.\n        \"\"\"\n        parentDB = None\n        try:\n            grandparent = self.ancestor.ancestor\n            parentDB = grandparent.findAttributeInAncestry(Database.moniker)\n        except Exception as ex:\n            # no parent database found\n            pass\n\n        return parentDB\n\n    @property\n    def documentation( self ) :\n        \"\"\"Returns the documentation instance.\"\"\"\n\n        return( self.__documentation )\n\n    def add( self, particle ) :\n        \"\"\"\n        Add a particle to the database, inside the appropriate particle family.\n\n        :param particle: instance of families.particle.Particle or derived class\n        \"\"\"\n\n        if( isinstance( particle, gaugeBosonModule.Particle ) ) :\n            self.__gaugeBosons.add( particle )\n        elif( isinstance( particle, leptonModule.Particle ) ) :\n            self.__leptons.add( particle )\n        elif( isinstance( particle, baryonModule.Particle ) ) :\n            self.__baryons.add( particle )\n        elif( isinstance( particle, ( chemicalElementModule.ChemicalElement, isotopeModule.Isotope, nuclideModule.Particle ) ) ) :\n            self.__chemicalElements.add( particle )\n        elif( isinstance( particle, unorthodoxModule.Particle ) ) :\n            self.__unorthodoxes.add( particle )\n        elif( isinstance(particle, aliasModule.BaseAlias)) :\n            if( particle.id not in self.__aliases ) :      # Do not allow an alias id to overwrite a particle id.\n                if( particle.id in self ) : raise ValueError( 'particle with id = \"%s\" already in database' % particle.id )\n            self.__aliases.add( particle )\n        else :\n            raise TypeError( 'Unsupported particle family = \"%s\"' % particle.moniker )\n\n    def addFile( self, fileName, replace = False ) :\n        \"\"\"\n        Opens the file *fileName* and adds its particles to self. If replace is False, particles already in self are\n        not replaced.\n        \"\"\"\n\n        pops = self.read(fileName)\n        for particle in pops :\n            if( not( replace ) and particle.id in self ) : continue\n            if( isinstance( particle, nucleusModule.Particle ) ) : continue\n            self.add( particle )\n\n    def check( self, **kwargs ) :\n        \"\"\"\n        Check for physics problems in the database\n\n        :param kwargs: options controlling i.e. how strict checks should be. Currently unused.\n        :return: list of warnings along with context about where they appear in the database\n        \"\"\"\n\n        from . import warning as warningModule\n\n        warnings = []\n        info = kwargs\n        info['PoPs'] = self\n        for suite in ( self.__gaugeBosons, self.__leptons, self.__baryons, self.__chemicalElements, self.__unorthodoxes, self.__aliases ) :\n            suiteWarnings = suite.check( info )\n            if suiteWarnings:\n                warnings.append( warningModule.Context(\"%s\" % suite.moniker, suiteWarnings) )\n\n        result = warningModule.Context('PoPs', warnings)\n        result.info = info\n        return result\n\n    def convertUnits( self, unitMap ) :\n        \"\"\"\n        Recursively searches for units within all sections, converting units that appear in the unitMap.\n\n        :param unitMap: dictionary with old/new unit pairs where the old unit is the key (e.g., { 'eV' : 'MeV', 'b' : 'mb' }).\n        \"\"\"\n\n        self.__styles.convertUnits( unitMap )\n        self.__gaugeBosons.convertUnits( unitMap )\n        self.__leptons.convertUnits( unitMap )\n        self.__baryons.convertUnits( unitMap )\n        self.__chemicalElements.convertUnits( unitMap )\n        self.__unorthodoxes.convertUnits( unitMap )\n\n    def copy( self ) :\n        \"\"\"\n        :return: deep copy of the entire database.\n        \"\"\"\n\n        _database = Database( self.name, self.version, formatVersion = self.format )\n        for item in self.__styles : _database.styles.add( item.copy( ) )\n            # FIXME, need to add documentation.\n        for item in self.__aliases : _database.add( item.copy( ) )\n        for item in self.__gaugeBosons : _database.add( item.copy( ) )\n        for item in self.__leptons : _database.add( item.copy( ) )\n        for item in self.__baryons : _database.add( item.copy( ) )\n        for item in self.__chemicalElements : _database.add( item.copy( ) )\n        for item in self.__unorthodoxes : _database.add( item.copy( ) )\n\n        return( _database )\n\n    def estimateMass(self, pid, unit, maxASpan=4):\n        '''\n        Returns the mass of the particle specified by *pid* if it exists in *self* and has a mass; otherwise,\n        the mass is estimate if possible. Currenlty, estimates are only performed if *pid* is a nuclide. In \n        which case, its mass is estimated by trying to find a near by nuclide with the same Z. If this fails, \n        None is returned.\n\n        If the particle exists and has a mass (ergo, the mass is not estimated), the returned value is positive. \n        If the mass is estimated, a negative value is returned whose absolute value is the estimated mass. \n        Otherwise, None is returned.\n\n        If the mass is estimated for a nuclide, the returned mass is for the ground state of the nuclide. Ergo, the *pid*\n        specifies a nuclear level, the level information is ignored.\n\n        :param pid:             The PoPs id of the particle whose mass is to be returned.\n        :param unit:            The unit of the returned mass.\n        :maxASpan:              If estimating a nuclide mass, only consider A's within this value of the A for *pid*.\n\n        :return:                A positive float if a mass for *pid* exist in *self*, a negative float if the mass is estimated and None otherwise.\n        '''\n\n        mass = None\n\n        try:\n            particle = self[pid]\n            try:\n                return particle.getMass(unit)\n            except:\n                pass\n        except:\n            pass\n\n        dummy, symbol, A, level, dummy, dummy, dummy = chemicalElementsMiscModule.chemicalElementALevelIDsAndAnti(pid)\n        if symbol is None:\n            return None\n        try:\n            chemicalElement = self.chemicalElements[symbol]\n        except:\n            return None\n\n        lowerIsotopes = [999, None]\n        upperIsotopes = [999, None]\n        neutronMass = self[IDsModule.neutron].getMass(unit)\n        for index, isotope in enumerate(chemicalElement):\n            A_diff = A - isotope.A\n            if A_diff > 0:\n                if A_diff < lowerIsotopes[0]:\n                    lowerIsotopes = [A_diff, isotope]\n            else:\n                A_diff = abs(A_diff)\n                if A_diff < upperIsotopes[0]:\n                    upperIsotopes = [A_diff, isotope]\n\n        if lowerIsotopes[0] < maxASpan:\n            isotope = lowerIsotopes[1]\n            mass = isotope[isotope.symbol].getMass(unit) + neutronMass * lowerIsotopes[0]\n        if upperIsotopes[0] < maxASpan:\n            numberOfMass = 2\n            if mass is None:\n                mass = 0.0\n                numberOfMass = 1\n            isotope = upperIsotopes[1]\n            mass = (isotope[isotope.symbol].getMass(unit) - neutronMass * upperIsotopes[0] + mass) / numberOfMass\n        if mass is not None:\n            mass = -mass\n\n        return mass\n\n    def final( self, id ) :\n        \"\"\"\n        Returns the particle matching id. If id is an alias, will follow the alias pid until a non-alias particle is found and that particle is returned.\n        \"\"\"\n\n        particle = self[id]\n        if( isinstance( particle, particleModule.Alias ) ) : particle = particle.particle\n        return( particle )\n\n    def hasAlias( self, id ) :\n\n        if id in self.__aliases:\n            return True\n        parentDB = self.parentDatabase()\n        if parentDB is not None:\n            return parentDB.hasAlias(id)\n        return False\n\n    def QValue(self, ingoingParticles, outgoingParticles, unit='MeV'):\n        '''\n        Calculates the Q value from the list of ingoing and outgoing particle ids.\n\n        :param ingoingParticles:    List of ingoing particle ids.\n        :param outgoingParticles:   List of outgoing particle ids.\n        :param unit:                Unit of returned Q-value.\n\n        :return:                    Q-value.\n        '''\n\n        Q = 0.0\n\n        ingoingParticles = list(ingoingParticles)\n        outgoingParticles = list(outgoingParticles)\n\n        removeDuplicates = True\n        while(removeDuplicates):\n            removeDuplicates = False\n            for particle in ingoingParticles:\n                if particle in outgoingParticles:\n                    removeDuplicates = True\n                    break\n            if removeDuplicates:\n                ingoingParticles.remove(particle)\n                outgoingParticles.remove(particle)\n\n        for particle in ingoingParticles:\n            Q += self[particle].getMass(unit + '/c**2')\n\n        for particle in outgoingParticles:\n            Q -= self[particle].getMass(unit + '/c**2')\n\n        return Q\n\n    def saveToFile( self, fileName, **kwargs ) :\n\n        dirname = os.path.dirname( fileName )\n        if( dirname != '' ) :\n            if( not( os.path.exists( dirname ) ) ) : os.makedirs( dirname )\n        fOut = open( fileName, 'w' )\n        fOut.write( '<?xml version=\"1.0\"?>\\n' )\n        fOut.write( self.toXML( **kwargs ) )\n        fOut.write( '\\n' )\n        fOut.close( )\n\n    def toXML_strList(self, indent = '', **kwargs):\n\n        indent2 = indent + kwargs.get('incrementalIndent', '  ')\n\n        formatVersion = kwargs.get('formatVersion', GNDS_formatVersionModule.default)\n        if formatVersion in (GNDS_formatVersionModule.version_2_0_LLNL_3, GNDS_formatVersionModule.version_2_0_LLNL_4):\n            print('INFO: converting GNDS format from \"%s\" to \"%s\".' % (formatVersion, GNDS_formatVersionModule.version_2_0))\n            formatVersion = GNDS_formatVersionModule.version_2_0\n        if formatVersion not in GNDS_formatVersionModule.allowed:\n            raise Exception(\"Unsupported GNDS structure '%s'!\" % str(formatVersion))\n        kwargs['formatVersion'] = formatVersion\n\n        xmlString = ['%s<%s name=\"%s\" version=\"%s\" format=\"%s\">' % (indent, self.moniker, self.name, self.version, formatVersion)]\n        # FIXME for v1.10 also print link to parent database?\n        xmlString += self.__styles.toXML_strList(indent2, **kwargs)\n\n        if formatVersion == '0.1' and len(self.styles) != 0:\n            xmlString += self.styles.getEvaluatedStyle().documentation.toXML_strList(indent2, **kwargs)\n        # FIXME: currently both PoPs and PoPs/styles/evaluated have documentation nodes according to GNDS-2.0 specs\n        xmlString += self.__documentation.toXML_strList(indent2, **kwargs)\n\n        xmlString += self.__aliases.toXML_strList(indent2, **kwargs)\n        xmlString += self.__gaugeBosons.toXML_strList(indent2, **kwargs)\n        xmlString += self.__leptons.toXML_strList(indent2, **kwargs)\n        xmlString += self.__baryons.toXML_strList(indent2, **kwargs)\n        xmlString += self.__chemicalElements.toXML_strList(indent2, **kwargs)\n        xmlString += self.__unorthodoxes.toXML_strList(indent2, **kwargs)\n\n        if len(xmlString) == 1:\n            xmlString = []\n        else:\n            xmlString[-1] += '</%s>' % self.moniker\n\n        return xmlString\n\n    def parseNode(self, element, xPath, linkData, **kwargs):\n\n        xPath.append( element.tag )\n\n        formatVersion = element.get( 'format' )\n        if( formatVersion == '0.1' ) :                             # PoPs 0.1 and 2.0 are the same so allow for now. There was no 1.10 for PoPs.\n            formatVersion = GNDS_formatVersionModule.version_1_10\n        else :\n            if( formatVersion not in GNDS_formatVersionModule.allowedPlus ) : raise Exception( \"Unsupported GNDS structure '%s'!\" % str( formatVersion ) )\n\n        self.__format = formatVersion\n        kwargs['formatVersion'] = formatVersion\n\n        children = {}\n        for child in ( gaugeBosonModule, leptonModule, baryonModule, unorthodoxModule, aliasModule ) :\n            children[child.Suite.moniker] = child.Suite\n\n        children[chemicalElementsModule.ChemicalElements.moniker] = chemicalElementsModule.ChemicalElements\n\n        for child in element :\n            if( child.tag in children ) :\n                children[child.tag].parseNode(getattr( self, child.tag ), child, xPath, linkData, **kwargs)\n            elif( child.tag == self.__styles.moniker ) :\n                self.__styles.parseNode(child, xPath, linkData, **kwargs)\n            elif( child.tag == 'parentDatabase' and formatVersion in (GNDS_formatVersionModule.version_1_10,\n                        GNDS_formatVersionModule.version_2_0_LLNL_3, GNDS_formatVersionModule.version_2_0_LLNL_4) ) :\n                pass  # ignore\n            elif( child.tag == self.documentation.moniker ) :\n                if formatVersion == GNDS_formatVersionModule.version_1_10 :\n                    self.styles.getEvaluatedStyle().documentation.parseNode(child, xPath, linkData, **kwargs)\n                else:\n                    self.documentation.parseNode(child, xPath, linkData, **kwargs)\n            else :\n                raise ValueError( 'sub-element = \"%s\" not allowed' % child.tag )\n\n        xPath.pop( )\n\n        return self\n\n    @classmethod\n    def parseNodeUsingClass(cls, element, xPath, linkData, **kwargs):\n\n        formatVersion = element.get( 'format' )\n        if formatVersion in [GNDS_formatVersionModule.version_2_0_LLNL_3, GNDS_formatVersionModule.version_2_0_LLNL_4]:\n            formatVersion = GNDS_formatVersionModule.version_2_0\n        if( formatVersion == '0.1' ) :                             # PoPs 0.1 and 2.0 are the same so allow for now. There was no 1.10 for PoPs.\n            formatVersion = GNDS_formatVersionModule.version_1_10\n        else :\n            if( formatVersion not in GNDS_formatVersionModule.allowedPlus ) : raise Exception( \"Unsupported GNDS structure '%s'!\" % str( formatVersion ) )\n\n        self = cls( element.get( 'name' ), element.get( 'version' ), formatVersion = formatVersion )\n        self.parseNode(element, xPath, linkData, **kwargs)\n        return( self )\n\n    @staticmethod\n    def read(fileName, **kwargs):\n        \"\"\"\n        Reads in the file name *fileName* and returns a **database** instance.\n        \"\"\"\n\n        return Database.readXML_file(fileName, **kwargs)\n\ndef read(fileName, **kwargs):\n    \"\"\"\n    Reads in the file name *fileName* and returns a **database** instance.\n    \"\"\"\n\n    return Database.read(fileName)\n","sub_path":"PoPs/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":22923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"628067119","text":"\nimport os\nimport pytest\nimport logging\nfrom webtest import TestApp\n\n\nimport configuration\nconfiguration.RESOURCE_PATH = '/tmp/micropsi_tests'\nconfiguration.SERVER_SETTINGS_PATH = '/tmp/micropsi_tests'\n\nfrom micropsi_core import runtime as micropsi\n\ntry:\n    os.makedirs('/tmp/micropsi_tests/worlds')\nexcept OSError:\n    pass\ntry:\n    os.makedirs('/tmp/micropsi_tests/nodenets')\nexcept OSError:\n    pass\n\n# create testuser:\nfrom micropsi_server.micropsi_app import usermanager\nusermanager.create_user('Pytest User', 'test', 'Administrator', uid='Pytest User')\nuser_token = usermanager.start_session('Pytest User', 'test', True)\n\nworld_uid = 'WorldOfPain'\nnn_uid = 'Testnet'\n\nlogging.getLogger('system').setLevel(logging.WARNING)\nlogging.getLogger('world').setLevel(logging.WARNING)\nlogging.getLogger('nodenet').setLevel(logging.WARNING)\n\n\n@pytest.fixture\ndef nodetype_def():\n    return os.path.join(configuration.RESOURCE_PATH, 'nodetypes.json')\n\n\n@pytest.fixture\ndef nodefunc_def():\n    return os.path.join(configuration.RESOURCE_PATH, 'nodefunctions.py')\n\n\n@pytest.fixture\ndef recipes_def():\n    return os.path.join(configuration.RESOURCE_PATH, 'recipes.py')\n\n\ndef set_logging_levels():\n    logging.getLogger('system').setLevel(logging.WARNING)\n    logging.getLogger('world').setLevel(logging.WARNING)\n    logging.getLogger('nodenet').setLevel(logging.WARNING)\n\n\nclass MicropsiTestApp(TestApp):\n    auth = None\n\n    def set_auth(self):\n        self.auth = user_token\n\n    def unset_auth(self):\n        self.auth = None\n        self.reset()\n\n    def get_json(self, *args, **kwargs):\n        headers = kwargs.setdefault(\"headers\", {})\n        if self.auth is not None:\n            self.set_cookie('token', self.auth)\n        headers[\"X-Requested-With\"] = 'XMLHttpRequest'\n        headers[\"Accept\"] = \"application/json\"\n        return super(MicropsiTestApp, self).get(*args, **kwargs)\n\n    def post_json(self, *args, **kwargs):\n        headers = kwargs.setdefault(\"headers\", {})\n        if self.auth is not None:\n            self.set_cookie('token', self.auth)\n        headers[\"X-Requested-With\"] = 'XMLHttpRequest'\n        headers[\"Accept\"] = \"application/json\"\n        return super(MicropsiTestApp, self).post_json(*args, **kwargs)\n\n\nfrom micropsi_server.micropsi_app import micropsi_app\ntestapp = MicropsiTestApp(micropsi_app)\n\n\n@pytest.fixture(scope=\"session\")\ndef resourcepath():\n    return configuration.RESOURCE_PATH\n\n\n@pytest.fixture()\ndef app(test_world):\n    return testapp\n\n\n@pytest.fixture()\ndef test_world(request):\n    global world_uid\n    worlds = micropsi.get_available_worlds(\"Pytest User\")\n    if world_uid not in worlds:\n        success, world_uid = micropsi.new_world(\"World of Pain\", \"Island\", \"Pytest User\", uid=world_uid)\n\n    def fin():\n        try:\n            micropsi.revert_world(world_uid)\n        except KeyError:\n            pass  # world was deleted in test\n    request.addfinalizer(fin)\n    return world_uid\n\n\n@pytest.fixture()\ndef test_nodenet(request):\n    global nn_uid\n    nodenets = micropsi.get_available_nodenets(\"Pytest User\") or {}\n    if nn_uid not in nodenets:\n        success, nn_uid = micropsi.new_nodenet(\"Testnet\", worldadapter=\"Default\", owner=\"Pytest User\", world_uid=world_uid, uid='Testnet')\n        micropsi.add_node(nn_uid, 'Concept', [10, 10], uid='N1', name='N1')\n        micropsi.add_link(nn_uid, 'N1', 'gen', 'N1', 'gen')\n        micropsi.save_nodenet(nn_uid)\n\n    def fin():\n            micropsi.revert_nodenet(nn_uid)\n    request.addfinalizer(fin)\n    return nn_uid\n\n\ndef pytest_runtest_call(item):\n    if 'fixed_test_nodenet' in micropsi.nodenets:\n        micropsi.revert_nodenet(\"fixed_test_nodenet\")\n    micropsi.logger.clear_logs()\n    set_logging_levels()\n\n\ndef pytest_runtest_teardown(item, nextitem):\n    if nextitem is None:\n        print(\"DELETING ALL STUFF\")\n        import shutil\n        shutil.rmtree(configuration.RESOURCE_PATH)\n\n","sub_path":"micropsi_server/tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"554687644","text":"#!/usr/bin/env python\n\nimport sys\nimport math, random, re\n\nfrom PyQt5.QtCore import (QPoint, QPointF, QRect, QRectF, QSize, Qt, QTime,\n        QTimer)\nfrom PyQt5.QtGui import (QBrush, QColor, QFontMetrics, QImage, QPainter,\n        QRadialGradient, QSurfaceFormat)\nfrom PyQt5.QtWidgets import QApplication, QOpenGLWidget\n\nimport OpenGL.GL as gl\nimport py3toolbox as tb\n\n\n\nclass Bubble(object):\n    def __init__(self, position, radius, velocity):\n        self.position = position\n        self.vel = velocity\n        self.radius = radius\n\n        self.innerColor = self.randomColor()\n        self.outerColor = self.randomColor()\n        self.updateBrush()\n\n    def updateBrush(self):\n        gradient = QRadialGradient(QPointF(self.radius, self.radius),\n                self.radius, QPointF(self.radius*0.5, self.radius*0.5))\n\n        gradient.setColorAt(0, QColor(255, 255, 255, 255))\n        gradient.setColorAt(0.25, self.innerColor)\n        gradient.setColorAt(1, self.outerColor)\n        self.brush = QBrush(gradient)\n\n\n    def randomColor(self):\n        red = random.randrange(205, 256)\n        green = random.randrange(205, 256)\n        blue = random.randrange(205, 256)\n        alpha = random.randrange(91, 192)\n\n        return QColor(red, green, blue, alpha)\n\n    def move(self, bbox):\n        self.position += self.vel\n        leftOverflow = self.position.x() - self.radius - bbox.left()\n        rightOverflow = self.position.x() + self.radius - bbox.right()\n        topOverflow = self.position.y() - self.radius - bbox.top()\n        bottomOverflow = self.position.y() + self.radius - bbox.bottom()\n\n        if leftOverflow < 0.0:\n            self.position.setX(self.position.x() - 2 * leftOverflow)\n            self.vel.setX(-self.vel.x())\n        elif rightOverflow > 0.0:\n            self.position.setX(self.position.x() - 2 * rightOverflow)\n            self.vel.setX(-self.vel.x())\n\n        if topOverflow < 0.0:\n            self.position.setY(self.position.y() - 2 * topOverflow)\n            self.vel.setY(-self.vel.y())\n        elif bottomOverflow > 0.0:\n            self.position.setY(self.position.y() - 2 * bottomOverflow)\n            self.vel.setY(-self.vel.y())\n\n    def rect(self):\n        return QRectF(self.position.x() - self.radius,\n                self.position.y() - self.radius, 2 * self.radius,\n                2 * self.radius)\n\n\nclass GLWidget(QOpenGLWidget):\n    def __init__(self, parent=None):\n        super(GLWidget, self).__init__(parent)\n\n        midnight = QTime(0, 0, 0)\n        random.seed(midnight.secsTo(QTime.currentTime()))\n\n        self.object = 0\n        self.xRot = 0\n        self.yRot = 0\n        self.zRot = 0\n        self.image = QImage()\n        self.bubbles = []\n        self.lastPos = QPoint()\n\n        self.trolltechGreen = QColor.fromCmykF(0.40, 0.0, 1.0, 0.0)\n        self.trolltechPurple = QColor.fromCmykF(0.39, 0.39, 0.0, 0.0)\n\n        self.animationTimer = QTimer()\n        self.animationTimer.setSingleShot(False)\n        self.animationTimer.timeout.connect(self.animate)\n        self.animationTimer.start(25)\n\n        self.setAutoFillBackground(False)\n        self.setMinimumSize(200, 200)\n        self.setWindowTitle(\"Overpainting a Scene\")\n\n    def setXRotation(self, angle):\n        angle = self.normalizeAngle(angle)\n        if angle != self.xRot:\n            self.xRot = angle\n\n    def setYRotation(self, angle):\n        angle = self.normalizeAngle(angle)\n        if angle != self.yRot:\n            self.yRot = angle\n\n    def setZRotation(self, angle):\n        angle = self.normalizeAngle(angle)\n        if angle != self.zRot:\n            self.zRot = angle\n\n    def initializeGL(self):\n        # gl = self.context().versionFunctions()\n        # gl.initializeOpenGLFunctions()\n\n        self.object = self.makeObject()\n\n    def mousePressEvent(self, event):\n        self.lastPos = event.pos()\n\n    def mouseMoveEvent(self, event):\n        dx = event.x() - self.lastPos.x()\n        dy = event.y() - self.lastPos.y()\n\n        if event.buttons() & Qt.LeftButton:\n            self.setXRotation(self.xRot + 8 * dy)\n            self.setYRotation(self.yRot + 8 * dx)\n        elif event.buttons() & Qt.RightButton:\n            self.setXRotation(self.xRot + 8 * dy)\n            self.setZRotation(self.zRot + 8 * dx)\n\n        self.lastPos = event.pos()\n\n    def paintEvent(self, event):\n        self.makeCurrent()\n\n        gl.glMatrixMode(gl.GL_MODELVIEW)\n        gl.glPushMatrix()\n\n        self.setClearColor(self.trolltechPurple.darker())\n        gl.glShadeModel(gl.GL_SMOOTH)\n        gl.glEnable(gl.GL_DEPTH_TEST)\n        #gl.glEnable(gl.GL_CULL_FACE)\n        gl.glEnable(gl.GL_LIGHTING)\n        gl.glEnable(gl.GL_LIGHT0)\n        gl.glEnable(gl.GL_MULTISAMPLE)\n        gl.glLightfv(gl.GL_LIGHT0, gl.GL_POSITION, (0.5, -5.0, 7.0, 1.0)) \n\n        self.setupViewport(self.width(), self.height())\n\n        gl.glClear(\n                gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT)\n        gl.glLoadIdentity()\n        gl.glTranslated(0.0, 0.0, -10.0)\n        gl.glRotated(self.xRot / 16.0, 1.0, 0.0, 0.0)\n        gl.glRotated(self.yRot / 16.0, 0.0, 1.0, 0.0)\n        gl.glRotated(self.zRot / 16.0, 0.0, 0.0, 1.0)\n        gl.glCallList(self.object)\n\n        gl.glMatrixMode(gl.GL_MODELVIEW)\n        gl.glPopMatrix()\n\n        painter = QPainter(self)\n        painter.setRenderHint(QPainter.Antialiasing)\n\n        \"\"\"\n        for bubble in self.bubbles:\n            if bubble.rect().intersects(QRectF(event.rect())):\n                bubble.drawBubble(painter)\n        \"\"\"\n        \n        \n        self.drawInstructions(painter)\n        painter.end()\n\n    def resizeGL(self, width, height):\n        self.setupViewport(width, height)\n\n    def showEvent(self, event):\n        #self.createBubbles(20 - len(self.bubbles))\n        pass\n        \n    def sizeHint(self):\n        return QSize(600, 600)\n\n        \n\n    def makeObject(self):\n        genList = gl.glGenLists(1)\n        gl.glNewList(genList, gl.GL_COMPILE)\n        vertices = []\n        colors   = [(0.0,0.0,1.0), (0.0,1.0,0.0),(1.0,0.0,0.0)]\n        vnormals = []\n        gl.glBegin(gl.GL_TRIANGLES)     \n        \n        #gl.glBegin(gl.GL_LINES)      \n        \n        obj_lines = tb.read_file(file_name='shuttle4.obj',return_type='list')\n        for obj_line in obj_lines:\n          if obj_line.startswith('v ') : \n            obj_line = obj_line.replace('v ','')\n            [x,y,z] = obj_line.split(\" \")\n            vertices.append ([float(x), float(y),float(z)])\n         \n          if obj_line.startswith('vn ') : \n            obj_line = obj_line.replace('vn ','')\n            [vn_x,vn_y,vn_z] = obj_line.split(\" \")\n            vnormals.append ([float(vn_x), float(vn_y),float(vn_z)])           \n\n        i = 0\n        \n        for obj_line in obj_lines:\n          if obj_line.startswith('f ') : \n            obj_line = obj_line.replace('f ','')\n            v = re.findall(r\"\\s*(\\d+)\\/\\/\\d+\\s*\", obj_line)\n            vn = re.findall(r\"\\s*\\d+\\/\\/(\\d+)\\s*\", obj_line)\n            #print (int(v[0]) + int(v[1]) + int(v[2]))\n            i +=1\n            gl.glNormal3d(vnormals[int(vn[0])-1][0],vnormals[int(vn[0])-1][1],vnormals[int(vn[0])-1][2])\n            gl.glVertex3f(vertices[int(v[0])-1][0],vertices[int(v[0])-1][1],vertices[int(v[0])-1][2])\n            gl.glVertex3f(vertices[int(v[1])-1][0],vertices[int(v[1])-1][1],vertices[int(v[1])-1][2])\n            gl.glVertex3f(vertices[int(v[2])-1][0],vertices[int(v[2])-1][1],vertices[int(v[2])-1][2])\n         \n   \n        gl.glEnd()\n        gl.glEndList()\n\n        return genList\n    \n        \n\n    def normalizeAngle(self, angle):\n        while angle < 0:\n            angle += 360 * 16\n        while angle > 360 * 16:\n            angle -= 360 * 16\n        return angle\n\n    def createBubbles(self, number):\n        for i in range(number):\n            position = QPointF(self.width()*(0.1 + 0.8*random.random()),\n                               self.height()*(0.1 + 0.8*random.random()))\n            radius = min(self.width(), self.height())*(0.0125 + 0.0875*random.random())\n            velocity = QPointF(self.width()*0.0125*(-0.5 + random.random()),\n                               self.height()*0.0125*(-0.5 + random.random()))\n\n            self.bubbles.append(Bubble(position, radius, velocity))\n\n    def animate(self):\n        for bubble in self.bubbles:\n            bubble.move(self.rect())\n\n        self.update()\n\n    def setupViewport(self, width, height):\n        side = min(width, height)\n        gl.glViewport((width - side) // 2, (height - side) // 2, side,\n                side)\n\n        gl.glMatrixMode(gl.GL_PROJECTION)\n        gl.glLoadIdentity()\n        gl.glOrtho(-4, +4, +4, -4, 1.0, 30.0)\n        gl.glMatrixMode(gl.GL_MODELVIEW)\n\n    def drawInstructions(self, painter):\n        text = \"Click and drag with the left mouse button to rotate the Qt \" \\\n                \"logo.\"\n        metrics = QFontMetrics(self.font())\n        border = max(4, metrics.leading())\n\n        rect = metrics.boundingRect(0, 0, self.width() - 2*border,\n                int(self.height()*0.125), Qt.AlignCenter | Qt.TextWordWrap,\n                text)\n        painter.setRenderHint(QPainter.TextAntialiasing)\n        painter.fillRect(QRect(0, 0, self.width(), rect.height() + 2*border),\n                QColor(0, 0, 0, 127))\n        painter.setPen(Qt.white)\n        painter.fillRect(QRect(0, 0, self.width(), rect.height() + 2*border),\n                QColor(0, 0, 0, 127))\n        painter.drawText((self.width() - rect.width())/2, border, rect.width(),\n                rect.height(), Qt.AlignCenter | Qt.TextWordWrap, text)\n\n    def setClearColor(self, c):\n        gl.glClearColor(c.redF(), c.greenF(), c.blueF(), c.alphaF())\n\n    def setColor(self, c):\n        gl.glColor4f(c.redF(), c.greenF(), c.blueF(), c.alphaF())\n\n\nif __name__ == '__main__':\n\n    app = QApplication(sys.argv)\n\n    fmt = QSurfaceFormat()\n    fmt.setSamples(4)\n    QSurfaceFormat.setDefaultFormat(fmt)\n\n    window = GLWidget()\n    window.show()\n    sys.exit(app.exec_())","sub_path":"archives/build_2/test_6.py","file_name":"test_6.py","file_ext":"py","file_size_in_byte":10001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"351947900","text":"# -*- coding: utf-8 -*-\n\"\"\"\n/dms/hessen/trainingdb/views_show.py\n\n.. zeigt die Inhalte der Fortbildungsdatenbank an\n         Django content Management System\n\nHans Rauch\nhans.rauch@gmx.net\n\nDie Programme des dms-Systems koennen frei genutzt und den spezifischen\nBeduerfnissen entsprechend angepasst werden.\n\n0.01  18.02.2008  Beginn der Arbeit\n0.02  20.02.2008  gueltig\n\"\"\"\n\nfrom django.template.loader import get_template\nfrom django.template    import Context\nfrom django.shortcuts   import render_to_response\nfrom django             import newforms as forms\nfrom django.shortcuts   import render_to_response\nfrom django.utils.safestring  import mark_safe\n\nfrom django.utils.translation import ugettext as _\n\nfrom dms.hessen.trainingdb.queries    import get_fach_list\nfrom dms.hessen.trainingdb.queries    import get_gueltig_list\nfrom dms.hessen.trainingdb.queries    import get_schulart_list\nfrom dms.hessen.trainingdb.queries    import get_zielgruppe_list\nfrom dms.hessen.trainingdb.queries    import get_anbieter_list\nfrom dms.hessen.trainingdb.queries    import get_veranstaltung_count\nfrom dms.hessen.trainingdb.queries    import get_veranstaltungen_by_filter\nfrom dms.hessen.trainingdb.queries    import get_schularten_by_veranst_iq_id\nfrom dms.hessen.trainingdb.queries    import get_zielgruppen_by_veranst_iq_id\n\nfrom dms_ext.extension    import * # dms-Funktionen ueberschreiben\n\ndef has_search_items(data):\n  \"\"\" wurden Einträge in Formular vorgenommen? \"\"\"\n  has_data = False\n  if data == {}:\n    return has_data\n  has_data = has_data or (data.has_key('training_fach'))\n  has_data = has_data or (data.has_key('training_text') and data['training_text']!='')\n  has_data = has_data or (data.has_key('training_schulart'))\n  has_data = has_data or (data.has_key('training_zielgruppe') and data.has_key('training_zielgruppe'))\n  has_data = has_data or (data.has_key('training_iq_nummer') and data['training_iq_nummer']!='')\n  has_data = has_data or (data.has_key('training_intern_nummer') and data['training_intern_nummer']!='')\n  has_data = has_data or (data.has_key('training_anbieter') and data['training_anbieter']!='')\n  return has_data\n\ndef get_form_list(items):\n  \"\"\" \"\"\"\n  ret = []\n  for item in items:\n    ret.append( (item.CodeKey, item.TextKey) )\n  return ret\n\ndef get_fach_choices(item_container):\n  \"\"\" liefert die Liste der moeglichen Faecher \"\"\"\n  ret = []\n  ret.append((-1, '---'))\n  faecher = get_fach_list(item_container)\n  for fach in faecher:\n    ret.append((fach.fach_id, fach.fach_name))\n  return ret\n\ndef get_gueltig_choices():\n  \"\"\" liefert die Liste der moeglichen Gültigkeitsbereiche \"\"\"\n  ret = []\n  ret.append((-1, '---'))\n  gueltigs = get_gueltig_list()\n  for gueltig in gueltigs:\n    ret.append((gueltig.gueltig_id, gueltig.gueltig_name))\n  return ret\n\ndef get_schulart_choices(item_container):\n  \"\"\" liefert die Liste der moeglichen Schularten \"\"\"\n  ret = []\n  ret.append((-1, '---'))\n  schularten = get_schulart_list(item_container)\n  for schulart in schularten:\n    ret.append((schulart.schulart_id, schulart.schulart_name))\n  return ret\n\ndef get_zielgruppe_choices(item_container):\n  \"\"\" liefert die Liste der moeglichen Zielgruppen \"\"\"\n  ret = []\n  ret.append((-1, '---'))\n  zielgruppen = get_zielgruppe_list(item_container)\n  for zielgruppe in zielgruppen:\n    ret.append((zielgruppe.zielgruppe_id, zielgruppe.zielgruppe_name))\n  return ret\n\ndef get_anbieter_choices(item_container):\n  \"\"\" liefert die Liste der moeglichen Anbieter \"\"\"\n  ret = []\n  ret.append((-1, '---'))\n  anbieters = get_anbieter_list(item_container)\n  for anbieter in anbieters:\n    ret.append((anbieter.anbieter_iq_id, anbieter.anbieter_name[:60]))\n  return ret\n\n# -----------------------------------------------------\ndef get_datum(item):\n  \"\"\" liefert das zusammengesetzte Datum \"\"\"\n  if item[0].veranst_datum_von != None:\n    von_datum = item[0].veranst_datum_von.strftime('%d.%m.%Y')\n    try:\n      bis_datum = item[0].veranst_datum_bis.strftime('%d.%m.%Y')\n    except:\n      bis_datum = von_datum\n    von_zeit = item[0].veranst_datum_von.strftime('%H:%M')\n    try:\n      bis_zeit = item[0].veranst_datum_bis.strftime('%H:%M')\n    except:\n      bis_zeit = von_zeit\n    datum = von_datum\n    if von_zeit != '00:00':\n      datum += '/' + von_zeit\n    if von_datum != bis_datum:\n      datum += ' - ' + bis_datum\n      if bis_zeit != '00:00':\n        datum += '/' + bis_zeit\n    elif von_zeit != bis_zeit and bis_zeit != '00:00':\n      datum += '-' + bis_zeit\n  else:\n    datum = 'Abrufangebot'\n  return datum\n\n# -----------------------------------------------------\ndef get_schularten_str(items):\n  ret = ''\n  for item in items:\n    if ret != '':\n      ret += ' &middot; '\n    ret += item[1].schulart_name\n  return mark_safe(ret)\n\n# -----------------------------------------------------\ndef get_zielgruppen_str(items):\n  ret = ''\n  for item in items:\n    if ret != '':\n      ret += ' &middot; '\n    ret += item[1].zielgruppe_name\n  return mark_safe(ret)\n\n# -----------------------------------------------------\ndef get_faecher_str(items):\n  ret = ''\n  for item in items:\n    if ret != '':\n      ret += ' &middot; '\n    ret += item[1].fach_name\n  return mark_safe(ret)\n\n","sub_path":"hessen/trainingdb/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"583288946","text":"\"\"\"\n동전을 제한없이 사용할 수 있을 때 어떤 금액을 지불할 때 사용하는 동전 개수의 최소값 구하기\n\nn: 동전의 종류 \ns: 지불할 금액 \n\"\"\"\n# DP\nMAX = 1e9\n\ndef dp_solve(coins, n, s):\n    result_list = [MAX] * (s + 1)\n    result_list[0] = 0\n    \n    # 1원부터 계산\n    for i in range(1, s + 1):\n        for j in range(n):\n\n            # 현재 구하는 금액보다 작은 금액의 동전만 검사\n            if coins[j] <= i:\n                temp = result_list[i - coins[j]]\n                if temp != MAX and temp + 1 < result_list[i]:\n                    result_list[i] = temp + 1\n\n    return result_list[s]\n\ndef solution(coins, n, k):\n    dp = [0] * (k + 1)\n\n    for i in range(1, k + 1):\n        temp = []\n        for coin in coins:\n            if coin <= i and dp[i - coin] != -1:\n                temp.append(dp[i - coin])\n\n        if not temp:\n            dp[i] = -1\n        else:\n            dp[i] =min(temp) + 1\n\n    return dp[k]\n\ndef solution2(coins, n, k):\n    dp = [MAX] * (k + 1)\n    dp[0] = 0\n\n    for coin in coins:\n        for i in range(coin, k + 1):\n            dp[i] = min(dp[i], dp[i - coin] + 1)\n\n    if dp[k] == MAX:\n        return -1\n    else:\n        return dp[i]\n\n# 재귀\ndef recursive_solve(coins: list, n: int, s: int):\n    if s == 0: return\n\n    result = MAX\n\n    for i in range(n):\n        if coins[i] <= s:\n            temp = recursive_solve(coins, n, s - coins[i])\n            result = min(result, temp + 1)\n\n    return result\n\n\"\"\"\n금액을 만들기 위해 필요한 동전 개수의 경우의 수 구하기\n\"\"\"\n\ndef solution3(coins, n, k):\n    dp = [0] * (k + 1)\n    dp[0] = 1\n\n    for coin in coins:\n        for i in range(coin, k + 1):\n            dp[i] += dp[i - coin]\n\n    return dp[k]","sub_path":"dp/거스름돈.py","file_name":"거스름돈.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"77476076","text":"import pydicom\nimport csv\nimport numpy as np\nimport os\nimport csv\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\npatient_files_path = os.getenv(\"CT_DATABASE_PATH\")\nct_acc_date_out = os.getenv(\"CT_ACC_DATE\")\n\n\ndata_batches = [os.path.join(patient_files_path, f) for f in os.listdir(patient_files_path)]\n\npatient_folders = []\nfor datapath in data_batches:\n    patient_folders += [os.path.join(datapath, f, 'XY') for f in os.listdir(datapath)]\n\nmetadate = {}\ncnt = 0\nfor patient_folder in patient_folders:\n    patient_cts = [os.path.join(patient_folder, f) for f in os.listdir(patient_folder) if \"CT\" in f]\n    print (cnt, len(patient_cts), patient_folder)\n    for ct_file in patient_cts:\n        ds = pydicom.read_file(ct_file)\n        \n        creationDate = ds.InstanceCreationDate \n        creationTime = ds.InstanceCreationTime\n        studyDate = ds.StudyDate \n        studyTime = ds.StudyTime\n        try:\n            seriesDate = ds.SeriesDate\n            seriesTime = ds.SeriesTime\n        except:\n            seriesDate = \"\"\n            seriesTime = \"\"\n\n        contentDate = ds.ContentDate\n        contentTime = ds.ContentTime\n\n        if cnt not in metadate:\n            \n            metadate[cnt] = {\n            'patient_folder': patient_folder,\n            'creationDateTime' : [creationDate+creationTime,creationDate+creationTime],\n            'studyDateTime' : [studyDate+studyTime,studyDate+studyTime],\n            'seriesDateTime' : [seriesDate+seriesTime,seriesDate+seriesTime],\n            'contentDateTime' : [contentDate+contentTime,contentDate+contentTime],\n            }\n        else:\n            if creationDate+creationTime != \"\" and metadate[cnt]['creationDateTime'][0] == '':\n                metadate[cnt]['creationDateTime'][0] = creationDate+creationTime\n            if creationDate+creationTime != \"\" and metadate[cnt]['creationDateTime'][1] == '':\n                metadate[cnt]['creationDateTime'][1] = creationDate+creationTime\n            if creationDate+creationTime != \"\" and metadate[cnt]['creationDateTime'][0] != '':\n                if float(creationDate+creationTime) < float(metadate[cnt]['creationDateTime'][0]):\n                    metadate[cnt]['creationDateTime'][0] = creationDate+creationTime\n            if creationDate+creationTime != \"\" and metadate[cnt]['creationDateTime'][0] != '':\n                if float(creationDate+creationTime) > float(metadate[cnt]['creationDateTime'][1]):\n                    metadate[cnt]['creationDateTime'][1] = creationDate+creationTime\n                metadate[cnt]['creationDateTime'][0] = creationDate+creationTime\n           \n            if studyDate+studyTime != \"\" and metadate[cnt]['studyDateTime'][0] == '':\n                metadate[cnt]['studyDateTime'][0] = studyDate+studyTime\n            if studyDate+studyTime != \"\" and metadate[cnt]['studyDateTime'][1] == '':\n                metadate[cnt]['studyDateTime'][1] = studyDate+studyTime\n            if studyDate+studyTime != \"\" and metadate[cnt]['studyDateTime'][0] != '':\n                if float(studyDate+studyTime) < float(metadate[cnt]['studyDateTime'][0]):\n                    metadate[cnt]['studyDateTime'][0] = studyDate+studyTime\n            if studyDate+studyTime != \"\" and metadate[cnt]['studyDateTime'][0] != '':\n                if float(studyDate+studyTime) > float(metadate[cnt]['studyDateTime'][1]):\n                    metadate[cnt]['studyDateTime'][1] = studyDate+studyTime\n                metadate[cnt]['studyDateTime'][0] = studyDate+studyTime\n           \n            if contentDate+contentTime != \"\" and metadate[cnt]['contentDateTime'][0] == '':\n                metadate[cnt]['contentDateTime'][0] = contentDate+contentTime\n            if contentDate+contentTime != \"\" and metadate[cnt]['contentDateTime'][1] == '':\n                metadate[cnt]['contentDateTime'][1] = contentDate+contentTime\n            if contentDate+contentTime != \"\" and metadate[cnt]['contentDateTime'][0] != '':\n                if float(contentDate+contentTime) < float(metadate[cnt]['contentDateTime'][0]):\n                    metadate[cnt]['contentDateTime'][0] = contentDate+contentTime\n            if contentDate+contentTime != \"\" and metadate[cnt]['contentDateTime'][0] != '':\n                if float(contentDate+contentTime) > float(metadate[cnt]['contentDateTime'][1]):\n                    metadate[cnt]['contentDateTime'][1] = contentDate+contentTime\n                metadate[cnt]['contentDateTime'][0] = contentDate+contentTime\n            \n            if seriesDate+seriesTime != \"\" and metadate[cnt]['seriesDateTime'][0] == '':\n                metadate[cnt]['seriesDateTime'][0] = seriesDate+seriesTime\n            if seriesDate+seriesTime != \"\" and metadate[cnt]['seriesDateTime'][1] == '':\n                metadate[cnt]['seriesDateTime'][1] = seriesDate+seriesTime\n            if seriesDate+seriesTime != \"\" and metadate[cnt]['seriesDateTime'][0] != '':\n                if float(seriesDate+seriesTime) < float(metadate[cnt]['seriesDateTime'][0]):\n                    metadate[cnt]['seriesDateTime'][0] = seriesDate+seriesTime\n            if seriesDate+seriesTime != \"\" and metadate[cnt]['seriesDateTime'][0] != '':\n                if float(seriesDate+seriesTime) > float(metadate[cnt]['seriesDateTime'][1]):\n                    metadate[cnt]['seriesDateTime'][1] = seriesDate+seriesTime\n                metadate[cnt]['seriesDateTime'][0] = seriesDate+seriesTime\n           \n            # if metadate[cnt]['studyDateTime'] != studyDate+'_'+studyTime:\n                # print('Error')\n            # if metadate[cnt]['contentDate'] != contentDate+'_'+contentTime:\n                # print ('Error content')\n    cnt += 1\n\n            \n        # print(creationDate, creationTime)\n\n# print(metadate)\n# print(contentDate,contentTime)\n\n# ct_times = 'ct_times.csv'%cnt\nwith open(ct_acc_date_out, 'wb') as csvfile:\n    csvwriter = csv.writer(csvfile)\n    csvwriter.writerow(['patient_folder',\n        'MinCreationDateTime', 'MaxCreationDateTime', \n        'MinSeriesDateTime', 'MaxSeriesDateTime',\n        'MinStudyDateTime', 'MaxStudyDateTime',\n        'MinContentDateTime', 'MaxContentDateTime'])\n    for key in metadate:\n        csvwriter.writerow([metadate[key]['patient_folder']]+\n            metadate[key]['creationDateTime']+\n            metadate[key]['seriesDateTime']+\n            metadate[key]['studyDateTime']+\n            metadate[key]['contentDateTime'])","sub_path":"tools/0_get_ct_acquisition_date.py","file_name":"0_get_ct_acquisition_date.py","file_ext":"py","file_size_in_byte":6405,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"344563587","text":"import os\nimport time\nimport sys\n\nimport requests\nimport pdb\n\nPOP20_CC = ('IN US ID PK NG RU JP '\n            'MX PH VN ET EG DE IR TR FR').split()\n\nBASE_URL = 'https://www.baidu.com'\nDEST_DIR = 'downloads/'\n\n\ndef save_flag(img, filename):\n    path = os.path.join(DEST_DIR, filename)\n    print(\"@save_flags\", path)\n    with open(path, 'wb') as fp:\n        fp.write(img)\n\n\ndef get_flag(cc):\n    url = '{}/{cc}/{cc}.git'.format(BASE_URL, cc=cc.lower())\n    print(url)\n    #  pdb.set_trace()\n    resp = requests.get(url)\n    return resp.content\n\n\ndef show(text):\n    print(text, end=' ')\n    sys.stdout.flush()\n\n\ndef download_many(cc_list):\n    for cc in sorted(cc_list):\n        image = get_flag(cc)\n        show(cc)\n        save_flag(image, str(cc) + '.txt')\n\n    return len(cc_list)\n\n\ndef main(download_many):\n    t0 = time.time()\n    count = download_many(POP20_CC)\n    elapsed = time.time() - t0\n    msg = '\\n{} flags downloaded in {:.2f}s'\n    print(msg.format(count, elapsed))\n\n\nif __name__ == '__main__':\n    main(download_many)\n","sub_path":"Concurrency/asyncio_test/flags.py","file_name":"flags.py","file_ext":"py","file_size_in_byte":1034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"361609539","text":"\"\"\"\nBased on new1: add encoded(coords) to features\nIncluding:\n    1. class token, k neighbors + 1, in each transformer block stage, as develop8, Done.\n    2. group conv (in_dim, head_dim*heads, groups=heads), replac FC(in_dim, head_dim*heads) maps, as develop9, Done.\n    3. positional encoding in Trans block, fea + encoded, as develop6max, Done.\n    4. Different from develop4Amax, we add two droput(0.1) to FFN. Done\n\"\"\"\nimport torch.nn as nn\nimport torch.utils.data\nimport torch.nn.functional as F\nimport torch\nfrom torch import einsum\nimport torch.nn.parallel\nimport torch.utils.data\nfrom torch.autograd import Variable\nimport numpy as np\nfrom einops import rearrange, repeat\nimport math\nfrom typing import Any\nfrom pointnet2_ops.pointnet2_utils import furthest_point_sample\n\n\ndef square_distance(src, dst):\n    \"\"\"\n    Calculate Euclid distance between each two points.\n    src^T * dst = xn * xm + yn * ym + zn * zm；\n    sum(src^2, dim=-1) = xn*xn + yn*yn + zn*zn;\n    sum(dst^2, dim=-1) = xm*xm + ym*ym + zm*zm;\n    dist = (xn - xm)^2 + (yn - ym)^2 + (zn - zm)^2\n         = sum(src**2,dim=-1)+sum(dst**2,dim=-1)-2*src^T*dst\n    Input:\n        src: source points, [B, N, C]\n        dst: target points, [B, M, C]\n    Output:\n        dist: per-point square distance, [B, N, M]\n    \"\"\"\n    return torch.sum((src[:, :, None] - dst[:, None]) ** 2, dim=-1)\n\n\ndef index_points(points, idx):\n    \"\"\"\n    Input:\n        points: input points data, [B, N, C]\n        idx: sample index data, [B, S, [K]]\n    Return:\n        new_points:, indexed points data, [B, S, [K], C]\n    \"\"\"\n    raw_size = idx.size()\n    idx = idx.reshape(raw_size[0], -1)\n    res = torch.gather(points, 1, idx[..., None].expand(-1, -1, points.size(-1)))\n    return res.reshape(*raw_size, -1)\n\n\ndef farthest_point_sample(xyz, npoint):\n    \"\"\"\n    Input:\n        xyz: pointcloud data, [B, N, 3]\n        npoint: number of samples\n    Return:\n        centroids: sampled pointcloud index, [B, npoint]\n    \"\"\"\n    device = xyz.device\n    B, N, C = xyz.shape\n    centroids = torch.zeros(B, npoint, dtype=torch.long).to(device)\n    distance = torch.ones(B, N).to(device) * 1e10\n    farthest = torch.randint(0, N, (B,), dtype=torch.long).to(device)\n    batch_indices = torch.arange(B, dtype=torch.long).to(device)\n    for i in range(npoint):\n        centroids[:, i] = farthest\n        centroid = xyz[batch_indices, farthest, :].view(B, 1, 3)\n        dist = torch.sum((xyz - centroid) ** 2, -1)\n        distance = torch.min(distance, dist)\n        farthest = torch.max(distance, -1)[1]\n    return centroids\n\n\nclass Attention(nn.Module):\n    def __init__(self, dim, heads=8, dim_head=64):\n        \"\"\"\n        Implement self-attention layer\n        :param dim: input data dim\n        :param heads: attention heads\n        :param dim_head: dimension in each head\n        \"\"\"\n        super().__init__()\n        inner_dim = dim_head * heads\n        # project_out = not (heads == 1 and dim_head == dim)\n        self.heads = heads\n        self.scale = dim_head ** -0.5  # the 1/sqrt(d_k) in Eq.1 in Attention all you need\n        self.attend = nn.Softmax(dim=-1)\n        self.to_q = nn.Conv1d(dim, inner_dim, 1, groups=heads, bias=False)\n        self.to_k = nn.Conv1d(dim, inner_dim, 1, groups=heads, bias=False)\n        self.to_v = nn.Conv1d(dim, inner_dim, 1, groups=heads, bias=False)\n        self.to_out = nn.Linear(inner_dim, dim)\n        self.to_out = nn.Sequential(\n            nn.Conv1d(inner_dim, dim,1,bias=False),\n            nn.BatchNorm1d(dim),\n            nn.ReLU(inplace=True)\n\n        )\n\n        # self.position = nn.Sequential(\n        #     nn.Linear(3, dim // 8),\n        #     nn.LayerNorm(dim // 8),\n        #     nn.ReLU(inplace=True),\n        #     nn.Linear(dim // 8, dim),\n        #     nn.LayerNorm(dim),\n        #     nn.ReLU(inplace=True)\n        # )\n\n    def forward(self, x):\n        \"\"\"\n        :input x: [b batch, p points,  d dimension]\n        # :input coords: [b batch, p points, 3]\n        :return: [b batch, p points,  d dimension]\n        \"\"\"\n        trans = x.permute(0,2,1)\n        b, d, p, h = *trans.shape, self.heads\n        query = self.to_q(trans)\n        key = self.to_k(trans)\n        value = self.to_v(trans)\n        query, key, value = map(lambda t: rearrange(t, 'b (h d) n -> b h n d', h=h), [query, key, value])\n        dots = einsum('b h i d, b h j d -> b h i j', query, key) * self.scale\n        attn = self.attend(dots)\n        out = einsum('b h i j, b h j d -> b h i d', attn, value)\n        out = rearrange(out, 'b h n d -> b (h d) n')\n        out = self.to_out(out).permute(0,2,1)\n        return out\n\n\nclass TransformerBlock(nn.Module):\n    def __init__(self, dim, heads=8, dim_head=64, **kwargs):\n        \"\"\"\n        Building Transformer block\n        :param dim: input data dimension\n        :param heads: heads number\n        :param dim_head: dimension in each head\n        :param kwargs:\n        \"\"\"\n        super(TransformerBlock, self).__init__()\n        self.attention = Attention(dim=dim, heads=heads, dim_head=dim_head)\n        # self.ffn = FeedForward(dim=dim, hidden_dim=dim)  # modify hidden_dim accordingly, e.g, 2*dim or dim/2.\n        self.ffn = nn.Sequential(\n            nn.Conv1d(dim, dim,1),\n            nn.BatchNorm1d(dim),\n            nn.ReLU(inplace=True)\n        )\n\n        self.position = nn.Sequential(\n            nn.Conv1d(3, dim // 8,1,bias=False),\n            nn.BatchNorm1d(dim // 8),\n            nn.ReLU(inplace=True),\n            nn.Conv1d(dim // 8, dim,1,bias=False),\n            nn.BatchNorm1d(dim),\n            nn.ReLU(inplace=True)\n        )\n\n\n    def forward(self, x):\n        \"\"\"\n        :input x: [b batch, p points,  3+d dimension]\n        :return: [b batch, p points, 3+d dimension]\n        \"\"\"\n        coords, features = x[:, :, :3], x[:, :, 3:]\n        # relative_position = coords.unsqueeze(dim=2) - coords.unsqueeze(dim=1)\n        pos = self.position(coords.permute(0,2,1)).permute(0,2,1)  #[n,p,dim]\n        features = features+pos\n        att = self.attention(features)\n        att = att + features\n        out = self.ffn(att.permute(0,2,1))\n        out = out.permute(0,2,1) + att\n        out = torch.cat([coords, out], dim=-1)\n        return out\n\n\nclass FPSKNNGrouper(nn.Module):\n    def __init__(self, points, knn=16, **kwargs):\n        \"\"\"\n        Given a list of unordered data, return the fps neighbors (first neighbor is the sampled point).\n        :param points: number of sampled data points\n        :param knn: k-neighbors for each sampled point\n        :param kwargs:\n        \"\"\"\n        super(FPSKNNGrouper, self).__init__()\n        self.points = points  # points number of Farthest Points Sampling\n        self.knn = knn  # number of k neighbors\n\n    def forward(self, x):\n        \"\"\"\n        :param x: input data points corrdications [b, n, 3+c] first 3 dims are coordinates\n        :return: grouped_points [b,points, knn, 3+c]\n        !!! Notice that: the sampled points = grouped_points[:,:,0,:]\n        \"\"\"\n        # sampeld_points = index_points(x, farthest_point_sample(x[:, :, :3], self.points))  # [b,points, 3]\n        idx = furthest_point_sample((x[:, :, :3]).contiguous(), self.points).long()\n        sampeld_points = index_points(x, idx)  # [b,points, 3]\n\n        distances = square_distance(sampeld_points[:, :, :3], x[:, :, :3])  # including sampled points self.\n        knn_idx = distances.argsort()[:, :, :self.knn]\n        grouped_points = index_points(x, knn_idx)  # [b,points, knn, 3+c]\n        return grouped_points\n\n\nclass LocalGather(nn.Module):\n    def __init__(self, in_channel, out_channel, **kwargs):\n        \"\"\"\n        :input: [b,p,k,3+c]\n        :output: [b,p,3+c]\n        :param channel:\n        :param kwargs:\n        \"\"\"\n        super(LocalGather, self).__init__()\n        self.fcn = nn.Sequential(\n            nn.Conv2d(2*in_channel, in_channel, 1),\n            nn.BatchNorm2d(in_channel),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(in_channel, out_channel, 1),\n            nn.BatchNorm2d(out_channel),\n            nn.ReLU(inplace=True)\n        )\n    def forward(self, x):  # x [b,p,k,3+c], p: selected points, k is nerighbor number\n        xyz = x[:,:,0,:3]\n        fea = x[:,:,:,3:]\n        off_set = fea - (fea[:,:,0,:]).unsqueeze(dim=-2)\n        fea = torch.cat([fea, off_set], dim=-1)\n        fea = fea.permute(0,3,1,2)\n        fea = self.fcn(fea)\n        fea = (fea.max(dim=-1)[0]).permute(0,2,1)\n        return torch.cat([xyz,fea],dim=-1)\n\n\nclass New4(nn.Module):\n    def __init__(self, num_classes=40, use_normals=False, points=512,\n                 blocks=[1, 2, 1, 1], embed_channel=32, k_neighbors=[16, 16, 16, 16],\n                 heads=8, dim_head=16, expansion=2, reducer=4, pool=\"max\", **kwargs):\n        super(New4, self).__init__()\n        self.stages = len(blocks)\n        self.num_classes = num_classes\n        channel = 6 if use_normals else 3\n        self.embed = nn.Sequential(\n            nn.Conv1d(channel, embed_channel,1),\n            nn.BatchNorm1d(embed_channel),\n            nn.ReLU(inplace=True),\n            nn.Conv1d(embed_channel, embed_channel,1),\n            nn.BatchNorm1d(embed_channel),\n            nn.ReLU(inplace=True)\n        )\n        self.local_gathers = nn.ModuleList()\n        self.transformer_stages = nn.ModuleList()\n        # self.class_tokens = nn.ParameterList()\n        self.groupers = nn.ModuleList()\n        for stage, block_num in enumerate(blocks):\n            # for appending transformer blocks\n            factor = expansion ** stage\n            factor_d = int(math.sqrt(factor))\n            factor_h = factor // factor_d\n            transformer_blocks = []\n            for _ in range(block_num):\n                transformer_blocks.append(\n                    TransformerBlock(dim=embed_channel * factor * expansion, heads=heads * factor_h, dim_head=dim_head * factor_d)\n                )\n            transformer_blocks = nn.Sequential(*transformer_blocks)\n            self.transformer_stages.append(transformer_blocks)\n\n            # # for class token\n            # self.class_tokens.append( nn.Parameter(torch.rand(1, 1, 1, embed_channel * factor + 3)) )\n\n            # for appending transformer groups\n            knn = k_neighbors[stage]\n            self.groupers.append(FPSKNNGrouper(points=points // (reducer ** stage), knn=knn))\n\n            # for appending local gathers\n            self.local_gathers.append(\n                LocalGather(in_channel=embed_channel*factor, out_channel=embed_channel * factor * expansion)\n            )\n            # # for appending transformer downs\n            # self.transformer_downs.append(\n            #     TransformerDown(in_dim=embed_channel * factor, out_dim=embed_channel * factor * expansion,\n            #                     hid_dim=embed_channel)\n            # )\n\n        self.pool = nn.AdaptiveAvgPool1d(1) if pool==\"avg\" else nn.AdaptiveMaxPool1d(1)\n        # self.classify = nn.Linear(embed_channel * factor * expansion, num_classes)\n        self.classify = nn.Sequential(\n            nn.Linear(embed_channel * factor * expansion, 256),\n            nn.BatchNorm1d(256),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.5),\n            nn.Linear(256, 256),\n            nn.BatchNorm1d(256),\n            nn.ReLU(inplace=True),\n            nn.Dropout(0.5),\n            nn.Linear(256,num_classes)\n\n        )\n\n\n    def forward(self, x):  # x[b,d,n]\n        coords = (x[:,:3,:]).contiguous()  # [b,3,n]\n        out = self.embed(x)  # [b,embed_dim, n]\n\n        coords = coords.permute(0,2,1)  # [b,n,3]\n        out = out.permute(0,2,1)  # [b,n,embed_dim]\n        out = torch.cat([coords, out], dim=-1)\n        for i in range(self.stages):\n            out = self.groupers[i](out)  # [b,p,k,3+c], p: selected points, k is nerighbor number\n            out = self.local_gathers[i](out)  # [b, p, 3+c]\n            out = self.transformer_stages[i](out)\n            # coords, features = out[:, :, :3], out[:, :, 3:]\n            # # print(f\"features.shape: {features.shape}\")\n            # sampled_points = (features[:, :, 0, :]).unsqueeze(dim=-2)\n            # coords = coords[:, :, 0, :]\n            # out = self.transformer_downs[i](sampled_points)\n\n        # now, out shape is [b, sampled points, d]\n        xyz, fea = out[:,:,:3], out[:,:,3:]\n        out = self.pool(fea.transpose(1,2)).squeeze(dim=-1)\n        out = self.classify(out)\n        return out\n\n\ndef new4A(num_classes=40, **kwargs: Any) -> New4:\n    return New4(num_classes=num_classes, blocks=[2, 2, 2, 2], reducer=4, k_neighbors=[32,32,32,32], **kwargs)\n\ndef new4B(num_classes=40, **kwargs: Any) -> New4:\n    return New4(num_classes=num_classes, blocks=[1, 1, 1, 1], reducer=4, k_neighbors=[32,32,32,32], **kwargs)\n\ndef new4C(num_classes=40, **kwargs: Any) -> New4:\n    return New4(num_classes=num_classes, blocks=[1, 1, 1, 1], reducer=4, k_neighbors=[16,16,16,16], **kwargs)\n\n\ndef new4D(num_classes=40, **kwargs: Any) -> New4:\n    return New4(num_classes=num_classes, blocks=[2, 3, 6, 4], reducer=4, k_neighbors=[32,32,32,32], **kwargs)\n\ndef new4E(num_classes=40, **kwargs: Any) -> New4:\n    return New4(num_classes=num_classes, embed_channel=64, blocks=[1,1,1,1], reducer=4, k_neighbors=[32,32,32,32], **kwargs)\n\n\nif __name__ == '__main__':\n    print(\"===> testing localgather...\")\n    channel=16\n    gather = LocalGather(in_channel=channel, out_channel=2*channel)\n    data = torch.rand(2,512,32,channel+3)\n    out = gather(data)\n    print(out.shape)\n\n    data = torch.rand(2, 3, 1024)\n    print(\"===> testing new4A ...\")\n    pointsformer = new4A()\n    out = pointsformer(data)\n    print(out.shape)\n    print(\"===> testing new4B ...\")\n    pointsformer = new4B()\n    out = pointsformer(data)\n    print(out.shape)\n\n    print(\"===> testing new4C ...\")\n    pointsformer = new4C()\n    out = pointsformer(data)\n    print(out.shape)\n\n    print(\"===> testing new4D ...\")\n    pointsformer = new4D()\n    out = pointsformer(data)\n    print(out.shape)\n\n    pointsformer = new4E()\n    out = pointsformer(data)\n    print(out.shape)\n\n\n","sub_path":"models/New4.py","file_name":"New4.py","file_ext":"py","file_size_in_byte":14015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"150405013","text":"def multiplica (num1,num2):\n    return num1*num2\n\ndef divide (num1,num2):\n    try:\n        return num1/num2\n\n    except ZeroDivisionError:\n        print(\"No se puede dividir entre cero\")\n        return \"Operacion erronea\"\n\nop1 = (int(input(\"Introduce el primer numero : \")))\n\nop2 = (int(input(\"Introduce el segundo numero : \")))\n\noperacion = input(\"multi, divide : \")\n\nif operacion == \"multi\":\n    print(multiplica(op1,op2))\n\nelif operacion == \"divide\" : \n    print(divide(op1, op2))\n\nelse:  \n    print(\"Operacion no completada\")\n\n\nprint(\"Operacion ejecutada. \")","sub_path":"A2PildorasPython/ExcepcionesTryException.py","file_name":"ExcepcionesTryException.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"178396703","text":"# coding=gbk\r\n########copy slected fq###########\r\n#import numpy as np\r\nimport pandas as pd\r\nimport os,re,shutil,csv\r\nos.chdir(\"/home/cyto/nipt_back_up\")\r\n#data = pd.read_csv(r\"nipt_hbv.csv\")\r\n#data = csv.reader(open(\"nipt_hbv.csv\"))\r\n#data = data[['sample_id','fcl','lane','barcode']]\r\na = os.listdir(\"./fq\")\r\nfor i in a:\r\n\tif re.match(\".*sult\",i, flags=0) == None:\r\n\t\ta.remove(i)\r\ntarget = './files/html/'\r\nwidth = len(a)\r\nk=0\r\nlane = ['L01','L02']\r\nfile_type =['allCycleHeatmap.html','bestFovReport.html','heatmapReport.html','summaryReport.html']\r\ndef copy_html(a,lane,target_dir,file_type):\r\n\twidth = len(a)\r\n\tfor i in range(width):\r\n\t\tfcl = a[i][39:50]\t\r\n\t\tprint(a[i][0:-7])\r\n\t\t\t#file_name = fcl+'_'+lane+'_'+str(barcode)+'.fq.gz'\r\n\t\tfile_name = a[i][0:-7]+'_'+fcl+'_'+lane+'.'+file_type\r\n\t\tdir_fq = \"./fq/\" + a[i] + \"/\" + fcl +'/'+lane+'/'\r\n\t\tfile_dir = dir_fq+file_name\r\n\t\t#print(str(k)+'/'+str(60)+'----'+file_name)\r\n\t\tshutil.copyfile(file_dir,(target_dir+file_type+'/'+file_name))\r\nfor i in range(0,4):\r\n\tcopy_html(a,lane[0],target,file_type[i])\r\n\tcopy_html(a,lane[1],target,file_type[i])\r\n","sub_path":"python/upload_selected.py","file_name":"upload_selected.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"595523663","text":"from _thread import *\nfrom Action import *\nfrom Character import *\nfrom Item import *\nfrom Roster import *\nfrom kivy.app import App\nfrom kivy.lang import Builder\nfrom kivy.properties import StringProperty\nfrom kivy.uix.accordion import Accordion, AccordionItem\nfrom kivy.uix.actionbar import ActionBar, ActionButton, ActionPrevious, ActionView\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.button import Button\nfrom kivy.uix.label import Label\nfrom kivy.uix.popup import Popup\nfrom kivy.uix.screenmanager import Screen, ScreenManager\nfrom kivy.uix.scrollview import ScrollView\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.togglebutton import ToggleButton\nimport Pyro4, Pyro4.util, random, socket, sys, threading, time\n\nCombatants = None\nScreens = []\nsm = ScreenManager()\nsys.excepthook = Pyro4.util.excepthook\nisDM = False\n\nBuilder.load_string('''\n<ScrollableLabel>:\n    Label:\n        size_hint_y: None\n        height: self.texture_size[1]\n        text_size: self.width, None\n        text: root.text\n''')\n\nclass ScrollableLabel(ScrollView):\n    text = StringProperty('')\n\nclass ConnectionScreen(Screen):\n    def __init__(self, **kwargs):\n        super(ConnectionScreen, self).__init__(**kwargs)\n\n        self.root = Accordion()\n        roleItem = AccordionItem(title='Role')\n        self.root.add_widget(roleItem)\n        serverItem = AccordionItem(title='Server')\n        self.root.add_widget(serverItem)\n        self.dmBtn = ToggleButton(text=\"DM\", group=\"role\")\n        self.pcBtn = ToggleButton(text=\"Player\", group=\"role\", state=\"down\")\n        self.textinput = TextInput(text='John-LAPTOP', multiline=False)\n        self.textinput.bind(on_text_validate=self.on_confirm)\n        roleItem.add_widget(self.dmBtn)\n        roleItem.add_widget(self.pcBtn)\n        serverItem.add_widget(self.textinput)\n        self.add_widget(self.root)\n\n    def on_confirm(self, instance):\n        global Combatants\n        if self.dmBtn.state == \"down\":\n            Combatants = Roster()\n            t = threading.Thread(target = self.startServer)\n            t.start()\n            global isDM\n            isDM = True\n        elif self.pcBtn.state == \"down\":\n            nameserver = Pyro4.locateNS(host=self.textinput.text)\n            Combatants = Pyro4.Proxy(\"PYRONAME:Combatants\")\n        SS = SelectionScreen(name=\"Selection Screen\")\n        sm.add_widget(SS)\n        self.manager.current = 'Selection Screen'\n\n    def startServer(self):\n        Pyro4.Daemon.serveSimple({Combatants: \"Combatants\"}, host=socket.gethostname(), ns=True)\n\nclass SelectionScreen(Screen):\n    def __init__(self, **kwargs):\n        super(SelectionScreen, self).__init__(**kwargs)\n\n        global Combatants\n        self.root = Accordion()\n        charItem = AccordionItem(title='Characters')\n        self.root.add_widget(charItem)\n        enItem = AccordionItem(title='Enemies')\n        self.root.add_widget(enItem)\n        confirmItem = AccordionItem(title='Done')\n        self.root.add_widget(confirmItem)\n        self.CharButtons = []\n        self.EnemyButtons = []\n        self.enemyIter = 0\n        self.quantity = 0\n        self.content = TextInput(text='1', multiline=False)\n        self.content.bind(on_text_validate=self.setQuantity)\n        i = 0\n        while i < Combatants.getNumChars():\n            btn = ToggleButton(text=Combatants.getCharName(i))\n            charItem.add_widget(btn)\n            self.CharButtons.append(btn)\n            i = i+ 1\n        i = 0\n        while i < Combatants.getNumEn():\n            btn = ToggleButton(text=Combatants.getEnName(i))\n            enItem.add_widget(btn)\n            self.EnemyButtons.append(btn)\n            i = i + 1\n        B = Button(text='Go!')\n        B.bind(on_press=self.showCombatants)\n        confirmItem.add_widget(B)\n        self.add_widget(self.root)\n\n    def showCombatants(self, obj):\n        i = 0\n        NoEnemies = True\n        for c in self.CharButtons:\n            if c.state == 'down':\n                Combatants.add(i, True)\n            i += 1\n        i = 0\n        for e in self.EnemyButtons:\n            if e.state == 'down':\n                labStr = \"How many \" + e.text + \"?\"\n                NoEnemies = False\n                self.popup = Popup(title=labStr, content=self.content)\n                self.popup.open()\n                self.enemyIter = i\n            i += 1\n        if NoEnemies == True:\n            self.beginBattle()\n\n    def setQuantity(self, instance):\n        self.quantity = self.content.text\n        j = 0\n        while j < int(self.quantity):\n            Combatants.add(self.enemyIter, False)\n            j += 1\n            if int(self.quantity) > 1:\n                Combatants.getLast().name = Combatants.getLast().name + \" \" + str(j)\n        self.popup.dismiss()\n        self.beginBattle()\n\n    def beginBattle(self):\n        if isDM == True:\n            Combatants.isWaiting = False\n            BS = BattleScreen(name=\"Battle Screen\")\n            sm.add_widget(BS)\n            Screens.append(BS)\n            self.manager.current = 'Battle Screen'\n        else:\n            WS = WaitScreen(name=\"Wait Screen\")\n            sm.add_widget(WS)\n            self.manager.current = 'Wait Screen'\n\nclass WaitScreen(Screen):\n    def __init__(self, **kwargs):\n        super(WaitScreen, self).__init__(**kwargs)\n\n        self.button = Button(text='Check if Ready')\n        self.button.bind(on_press=self.waitForPlayers)\n        self.add_widget(self.button)\n\n    def waitForPlayers(self, obj):\n        if Combatants.waiting() == False:\n            BS = BattleScreen(name=\"Battle Screen\")\n            sm.add_widget(BS)\n            Screens.append(BS)\n            self.manager.current = 'Battle Screen'\n\nclass BattleScreen(Screen):\n    def __init__(self, **kwargs):\n        super(BattleScreen, self).__init__(**kwargs)\n\n        self.bScene = BattleScene()\n        self.layout = BoxLayout(orientation='vertical')\n        self.actionBar = ActionBar()\n        self.view = ActionView()\n        self.prompt = self.bScene.getTurn()\n        self.nametag = ActionPrevious(title=self.prompt)\n        self.MoveButton = ActionButton(text='Move')\n        self.AttackButton = ActionButton(text='Attack')\n        self.AbilityButton = ActionButton(text='Item')\n        self.ConfirmButton = ActionButton(text='Take Turn')\n        self.MoveButton.bind(on_press=self.Move)\n        self.AttackButton.bind(on_press=self.Action)\n        self.AbilityButton.bind(on_press=self.Ability)\n        self.ConfirmButton.bind(on_press=self.TakeTurn)\n        self.battleLog = 'Now Beginning Battle...'\n        self.label = ScrollableLabel(text=self.battleLog)\n        self.layout.add_widget(self.actionBar)\n        self.layout.add_widget(self.label)\n        self.actionBar.add_widget(self.view)\n        self.view.add_widget(self.nametag)\n        self.view.add_widget(self.MoveButton)\n        self.view.add_widget(self.AttackButton)\n        self.view.add_widget(self.AbilityButton)\n        self.view.add_widget(self.ConfirmButton)\n        self.add_widget(self.layout)\n        self.Target = Combatants.get(0)\n        Combatants.checkItems(0)\n        if Combatants.getHasItems(0) == False:\n            self.AbilityButton.disabled = True\n\n    def TakeTurn(self, obj):\n        if isDM == True:\n            if(Combatants.get(0).hasMoved == True and Combatants.get(0).hasActed == True):\n                delay = 100\n            elif(Combatants.get(0).hasMoved == False and Combatants.get(0).hasActed == False):\n                delay = 60\n            else:\n                delay = 80\n            Combatants.get(0).CT -= delay\n            Combatants.get(0).hasMoved = False\n            Combatants.get(0).hasActed = False\n            self.prompt = self.bScene.getTurn()\n            self.nametag.title = self.prompt\n        self.MoveButton.disabled = False\n        self.AttackButton.disabled = False\n        Combatants.checkItems(0)\n        if Combatants.getHasItems(0) == True:\n            self.AbilityButton.disabled = False\n        else:\n            self.AbilityButton.disabled = True\n\n    def Move(self, obj):\n        Combatants.get(0).hasMoved = True\n        self.MoveButton.disabled = True\n\n    def Action(self, obj):\n        Screens[0].populate()\n        self.manager.current = 'Action Menu'\n\n    def Ability(self, obj):\n        Screens[1].populateItems()\n        Screens[1].populateTargs()\n        self.manager.current = 'Ability Menu'\n\n    def Attack(self):\n        damage = Action.Attack(Combatants.get(0).acc, self.Target.eva, Combatants.get(0).attack, self.Target.defense)\n        if(damage == -1):\n            msg = \"%s missed %s!\" %(Combatants.get(0).name, self.Target.name)\n            self.battleLog = self.battleLog + \"\\n\" + msg\n            self.label.text = self.battleLog\n        else:\n            msg = \"%s hit %s for %d points of damage!\" %(Combatants.get(0).name, self.Target.name, damage)\n            self.battleLog = self.battleLog + \"\\n\" + msg\n            self.label.text = self.battleLog\n            self.Target.HP -= damage\n            if(self.Target.HP <= 0):\n                msg = \"%s was defeated!\" %(self.Target.name)\n                self.battleLog = self.battleLog + \"\\n\" + msg\n                self.label.text = self.battleLog\n\n    def UseItem(self, num):\n        Combatants.get(0).inventory[num].quantity -= 1\n        methodToCall = getattr(Item, Combatants.get(0).inventory[num].name)\n        msg = methodToCall(self.Target)\n        self.battleLog = self.battleLog + \"\\n\" + msg\n        self.label.text = self.battleLog\n\nclass ActionMenu(Screen):\n    def __init__(self, **kwargs):\n        super(ActionMenu, self).__init__(**kwargs)\n\n        self.root = Accordion(orientation='vertical')\n        self.targItem = AccordionItem(title='Target')\n        self.root.add_widget(self.targItem)\n        self.conItem = AccordionItem(title='Confirm')\n        self.root.add_widget(self.conItem)\n        B = Button(text='Confirm')\n        B2 = Button(text='Cancel')\n        B.bind(on_press=self.confirmTarget)\n        B2.bind(on_press=self.cancel)\n        self.conItem.add_widget(B)\n        self.conItem.add_widget(B2)\n        self.add_widget(self.root)\n\n    def populate(self):\n        self.ComButtons = []\n        for c in Combatants.retList():\n            btn = ToggleButton(text=c.name, group='targets')\n            self.targItem.add_widget(btn)\n            self.ComButtons.append(btn)\n\n    def confirmTarget(self, obj):\n        hasTarget = False\n        i = 0\n        for b in self.ComButtons:\n            if b.state == 'down':\n                hasTarget = True\n                Screens[2].Target = Combatants.get(i)\n            i += 1\n        if hasTarget == True:\n            hasTarget = False\n            self.targItem.clear_widgets(children=self.ComButtons)\n            self.ComButtons.clear()\n            Combatants.get(0).hasActed = True\n            Screens[2].AttackButton.disabled = True\n            self.manager.current = 'Battle Screen'\n            Screens[2].Attack()\n\n    def cancel(self, obj):\n        self.targItem.clear_widgets(children=self.ComButtons)\n        self.ComButtons.clear()\n        self.manager.current = 'Battle Screen'\n\nclass AbilityMenu(Screen):\n    def __init__(self, **kwargs):\n        super(AbilityMenu, self).__init__(**kwargs)\n\n        self.root = Accordion(orientation='vertical')\n        self.invItem = AccordionItem(title='Item')\n        self.root.add_widget(self.invItem)\n        self.targItem = AccordionItem(title='Target')\n        self.root.add_widget(self.targItem)\n        self.conItem = AccordionItem(title='Confirm')\n        self.root.add_widget(self.conItem)\n        B = Button(text='Confirm')\n        B2 = Button(text='Cancel')\n        B.bind(on_press=self.confirmTarget)\n        B2.bind(on_press=self.cancel)\n        self.conItem.add_widget(B)\n        self.conItem.add_widget(B2)\n        self.add_widget(self.root)\n\n    def populateItems(self):\n        self.ItemButtons = []\n        for item in Combatants.get(0).inventory:\n            if item.quantity > 0:\n                btn = ToggleButton(text=item.name, group='items')\n                self.invItem.add_widget(btn)\n                self.ItemButtons.append(btn)\n\n    def populateTargs(self):\n        self.ComButtons = []\n        for c in Combatants.retList():\n            btn = ToggleButton(text=c.name, group='targets')\n            self.targItem.add_widget(btn)\n            self.ComButtons.append(btn)\n\n    def confirmTarget(self, obj):\n        hasTarget = False\n        hasItem = False\n        itemNum = None\n        i = 0\n        for a in self.ItemButtons:\n            if a.state == 'down':\n                hasItem = True\n                itemNum = i\n            i += 1\n        i = 0\n        for b in self.ComButtons:\n            if b.state == 'down':\n                hasTarget = True\n                Screens[2].Target = Combatants.get(i)\n            i += 1\n        if hasTarget == True and hasItem == True:\n            hasTarget = False\n            hasItem = False\n            self.targItem.clear_widgets(children=self.ComButtons)\n            self.ComButtons.clear()\n            self.invItem.clear_widgets(children=self.ItemButtons)\n            self.ItemButtons.clear()\n            Combatants.get(0).abilitiesUsed = Combatants.get(0).abilitiesUsed + 1\n            Screens[2].AbilityButton.disabled = True\n            self.manager.current = 'Battle Screen'\n            Screens[2].UseItem(itemNum)\n\n    def cancel(self, obj):\n        self.targItem.clear_widgets(children=self.ComButtons)\n        self.ComButtons.clear()\n        self.invItem.clear_widgets(children=self.ItemButtons)\n        self.ItemButtons.clear()\n        self.manager.current = 'Battle Screen'\n\nclass CombatApp(App):\n    def build(self):\n        CS = ConnectionScreen(name=\"Connection Screen\")\n        sm.add_widget(CS)\n        AM = ActionMenu(name=\"Action Menu\")\n        Screens.append(AM)\n        sm.add_widget(AM)\n        AbM = AbilityMenu(name=\"Ability Menu\")\n        Screens.append(AbM)\n        sm.add_widget(AbM)\n        return sm\n\nclass BattleScene():\n    def __init__(self):\n        if isDM == True:\n            self.Initiative()\n    \n    def Initiative(self):\n        for c in Combatants.retList():\n            c.CT = random.randint(0, 100)\n\n    def getTurn(self):\n        if isDM == True:\n            Combatants.retList().sort(key = lambda Combatant: Combatant.CT, reverse = True)\n            while Combatants.get(0).CT < 100:\n                self.ClockTick()\n        prompt = Combatants.getName(0)\n        return prompt\n\n    def ClockTick(self):\n        for c in Combatants.retList():\n            if c.HP > 0:\n                c.CT += 5\n\nclass main():\n    CombatApp().run()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Combat.py","file_name":"Combat.py","file_ext":"py","file_size_in_byte":14650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"66322905","text":"import os\nimport csv\n\nimport twitter\n\nTOKENS = {\n    'consumer_key': os.environ.get('TWITTER_CONSUMER_KEY'),\n    'consumer_secret': os.environ.get('TWITTER_CONSUMER_SECRET'),\n    'access_token_key': os.environ.get('TWITTER_ACCESS_TOKEN_KEY'),\n    'access_token_secret': os.environ.get('TWITTER_ACCESS_TOKEN_SECRET')\n}\n\nFIELDS = ['link', 'favorites', 'retweets', 'user']\n\n\ndef main():\n    favorites = []\n    offset = None\n\n    # Twitter API Client bootstrapping\n    api = twitter.Api(**TOKENS)\n\n    print(f'Fetching...')\n    while True:\n\n        # Get 200 results\n        try:\n            results = api.GetFavorites(count=200, max_id=offset)\n        except twitter.errors.TwitterError:\n            print('Rate limit exceeded. Exiting.')\n            break\n        except:\n            print('Exiting.')\n            break\n\n        # Save results\n        favorites = [*favorites, *[res.AsDict() for res in results]]\n        print(f'  Fetched {len(favorites)}. max_id={offset}', end='\\r')\n\n        # Save last ID for later\n        offset = results[-1].id\n\n        # Game over ?\n        if len(results) == 1:\n            print(f'\\nFetched all favorites. Exiting.')\n            break\n\n    # Export result in CSV format.\n    with open('out.csv', 'w', newline='') as csvfile:\n        writer = csv.DictWriter(csvfile, fieldnames=FIELDS, quoting=csv.QUOTE_MINIMAL)\n        writer.writeheader()\n\n        for favorite in favorites:\n            writer.writerow({\n                'link': 'https://twitter.com/team_vitality/status/'+favorite['id_str'],\n                'favorites': favorite['favorite_count'],\n                'retweets': favorite.get('retweet_count', 0),\n                'user': favorite['user']['screen_name']\n            })\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"489884409","text":"from __future__ import unicode_literals, print_function, division\n# from io import open\n# import unicodedata\n# import string\n# import re\n# import random\n# from nnet.util import *\n\nimport numpy as np\nimport torch\n# import math\nimport torch.nn as nn\nimport torch.autograd\nfrom torch.autograd import Variable\n# from torch import optim\nimport torch.nn.functional as F\nimport torch.nn.utils.rnn as rnn\nimport torch.nn.init as init\n# from numpy import random as nr\n# from operator import itemgetter\n\n_BIG_NUMBER = 10. ** 6.\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef cat(l, dimension=-1):\n    valid_l = l\n    if dimension < 0:\n        dimension += len(valid_l[0].size())\n    return torch.cat(valid_l, dimension)\n\n\n'''\n--hps \"{'id': 1, 'sent_edim': 100, 'sent_hdim': 512,\n'frame_edim': 128, 'role_edim': 128, 'pos_edim': 16, 'rec_layers': 1,\n'gc_layers': 0, 'pos': True, 'rm':0, 'alpha': 0.25,\n'p_lemmas':True}\"\n'''\n\n# Interpreting the hyperparameters used:\n# edim - embedding dimension\n# hdim - ???\n\n#\n\n\nclass BiLSTMTagger(nn.Module):\n\n    # def __init__(self, embedding_dim, hidden_dim, vocab_size, tagset_size):\n    def __init__(self, hps, *_):  # *_ means no interest in further arguments\n        super(BiLSTMTagger, self).__init__()\n\n        batch_size = hps['batch_size']\n        lstm_hidden_dim = hps['sent_hdim']\n\n        # DEP  - short for dependency, i.e. the part of the system which\n        # extracts syntactic information\n        # sent - most likely refers to sentence\n        # pos  - part of speech\n\n        # The input of the dependency extractor has 2 word embeddings (one\n        # randomly initialized and one pre-trained) and 1 part of speech\n        # embedding + region dimension\n        # sent_embedding_dim_DEP = 2 * \\\n        #     hps['sent_edim'] + 1 * hps['pos_edim'] + 16\n\n        # The input of the dependency extractor has 2 word embeddings and 1\n        # part of speech embedding + region dimension\n        sent_embedding_dim_SRL = 3 * \\\n            hps['sent_edim'] + 1 * hps['pos_edim'] + 16\n        self.SRL_input_dim = sent_embedding_dim_SRL\n        # for the region mark\n\n        # Set to 128 dimensions\n        # role_embedding_dim = hps['role_edim']\n\n        # frame_embedding_dim = role_embedding_dim\n\n        # Word vocabulary size, based on file size\n        vocab_size = hps['vword']\n\n        # Role labels number based on the size of the label file\n        self.tagset_size = hps['vbio']\n\n        # Numbers of parts of speech\n        self.pos_size = hps['vpos']\n\n        # TODO Is this types of links or labeled links?\n        self.dep_size = hps['vdep']\n\n        # Frameset size, refers to the frame that a predicate belongs to\n        self.frameset_size = hps['vframe']\n\n        # Number of LSTM layers in the BiLSTM, set to 1 in the hyperparameters\n        self.num_layers = hps['rec_layers']\n        self.batch_size = batch_size\n\n        # Number of hidden states that are outputed by the LSTM\n        self.hidden_dim = lstm_hidden_dim\n\n        self.word_emb_dim = hps['sent_edim']\n\n        # Specific Dependencies are all the labels of the syntactic\n        # relationships\n        self.specific_dep_size = hps['svdep']\n\n        self.word_embeddings_SRL = nn.Embedding(\n            num_embeddings=vocab_size, embedding_dim=hps['sent_edim'])\n\n        # self.word_embeddings_DEP = nn.Embedding(vocab_size,\n        #   hps['sent_edim']) # Not used?\n\n        # Part of speech embeddings\n        self.pos_embeddings = nn.Embedding(\n            num_embeddings=self.pos_size, embedding_dim=hps['pos_edim'])\n\n        # self.pos_embeddings_DEP = nn.Embedding(self.pos_size,\n        #   hps['pos_edim']) # Not used?\n\n        # Embeddings for the predicate lemma\n        self.p_lemma_embeddings = nn.Embedding(\n            num_embeddings=self.frameset_size, embedding_dim=hps['sent_edim'])\n\n        # Embeddings for the dependencies\n        self.dep_embeddings = nn.Embedding(\n            num_embeddings=self.dep_size, embedding_dim=self.pos_size)\n\n        # TODO What is the region?\n        self.region_embeddings = nn.Embedding(2, 16)\n        # self.lr_dep_embeddings = nn.Embedding(self.lr_dep_size,\n        #     hps[])\n\n        # Pre-trained word embeddings from conll file\n        self.word_fixed_embeddings = nn.Embedding(vocab_size, hps['sent_edim'])\n\n        # These pre-trained weitghts come in the\n        # word_embeddings_proper.sskip.conll2009.txt file\n        self.word_fixed_embeddings.weight.data.copy_(\n            torch.from_numpy(hps['word_embeddings']))\n\n        # self.word_fixed_embeddings_DEP = nn.Embedding(\n        #     vocab_size, hps['sent_edim'])\n        # self.word_fixed_embeddings_DEP.weight.data.copy_(\n        #     torch.from_numpy(hps['word_embeddings']))\n\n        # SRL embeddings\n        # self.role_embeddings = nn.Embedding(\n        #     self.tagset_size, role_embedding_dim)\n\n        # Frame embeddings?? I assumed frame and lemma are the same\n        # self.frame_embeddings = nn.Embedding(\n        #     self.frameset_size, frame_embedding_dim)\n\n        # This are the NNs used for getting the hidden value from the hidden\n        # layers of the BiLSTM, but they are not used\n        # self.hidden2tag = nn.Linear(4 * lstm_hidden_dim, 2 * lstm_hidden_dim)\n        # self.MLP = nn.Linear(2 * lstm_hidden_dim, self.dep_size)\n        # self.tag2hidden = nn.Linear(self.dep_size, self.pos_size)\n        # self.hidden2tag_spe = nn.Linear(\n        #     2 * lstm_hidden_dim, 2 * lstm_hidden_dim)\n        # self.MLP_spe = nn.Linear(2 * lstm_hidden_dim, 4)\n        # self.tag2hidden_spe = nn.Linear(4, self.pos_size)\n        # # self.elmo_embeddings_0 = nn.Embedding(vocab_size, 1024)\n        # # self.elmo_embeddings_0.weight.data.copy_(torch.from_numpy\n        #   (hps['elmo_embeddings_0']))\n\n        # # self.elmo_embeddings_1 = nn.Embedding(vocab_size, 1024)\n        # # self.elmo_embeddings_1.weight.data.\n        #   copy_(torch.from_numpy(hps['elmo_embeddings_1']))\n\n        # self.elmo_emb_size = 200\n        # self.elmo_mlp_word = nn.Sequential(\n        #     nn.Linear(1024, self.elmo_emb_size), nn.ReLU())\n        # self.elmo_word = nn.Parameter(torch.Tensor([0.5, 0.5]))\n        # self.elmo_gamma_word = nn.Parameter(torch.ones(1))\n\n        # self.elmo_mlp = nn.Sequential(\n        #     nn.Linear(2 * lstm_hidden_dim, self.elmo_emb_size), nn.ReLU())\n        # self.elmo_w = nn.Parameter(torch.Tensor([0.5, 0.5]))\n        # self.elmo_gamma = nn.Parameter(torch.ones(1))\n\n        self.SRL_input_dropout = nn.Dropout(p=0.3)\n        # self.DEP_input_dropout = nn.Dropout(p=0.3)\n        self.hidden_state_dropout = nn.Dropout(p=0.3)\n        # self.word_dropout = nn.Dropout(p=0.0)\n        # self.predicate_dropout = nn.Dropout(p=0.0)\n        # self.label_dropout = nn.Dropout(p=0.5)\n        # self.link_dropout = nn.Dropout(p=0.5)\n        # self.use_dropout = nn.Dropout(p=0.2)\n\n        # The LSTM takes word embeddings as inputs, and outputs hidden states\n        # with dimensionality hidden_dim.\n        # self.num_layers = 1\n        # self.BiLSTM_0 = nn.LSTM(input_size=sent_embedding_dim_DEP,\n        #                         hidden_size=lstm_hidden_dim,\n        #                         batch_first=True,\n        #                         bidirectional=True,\n        #                         num_layers=self.num_layers)\n\n        # print(\"Size of all_weights\",\n        #   (numpy.ndarray(self.BiLSTM_0.all_weights)).shape)\n        # init.orthogonal_(self.BiLSTM_0.all_weights[0][0])\n        # init.orthogonal_(self.BiLSTM_0.all_weights[0][1])\n        # init.orthogonal_(self.BiLSTM_0.all_weights[1][0])\n        # init.orthogonal_(self.BiLSTM_0.all_weights[1][1])\n\n        # self.num_layers = 1\n        # self.BiLSTM_1 = nn.LSTM(input_size=lstm_hidden_dim * 2,\n        #                         hidden_size=lstm_hidden_dim,\n        #                         batch_first=True,\n        #                         bidirectional=True,\n        #                         num_layers=self.num_layers)\n\n        # init.orthogonal_(self.BiLSTM_1.all_weights[0][0])\n        # init.orthogonal_(self.BiLSTM_1.all_weights[0][1])\n        # init.orthogonal_(self.BiLSTM_1.all_weights[1][0])\n        # init.orthogonal_(self.BiLSTM_1.all_weights[1][1])\n\n        self.num_layers = 3\n        self.BiLSTM_SRL = nn.LSTM(input_size=sent_embedding_dim_SRL,\n                                  hidden_size=lstm_hidden_dim,\n                                  batch_first=True,\n                                  bidirectional=True,\n                                  num_layers=self.num_layers)\n\n        init.orthogonal_(self.BiLSTM_SRL.all_weights[0][0])\n        init.orthogonal_(self.BiLSTM_SRL.all_weights[0][1])\n        init.orthogonal_(self.BiLSTM_SRL.all_weights[1][0])\n        init.orthogonal_(self.BiLSTM_SRL.all_weights[1][1])\n\n        # non-linear map to role embedding\n        # self.role_map = nn.Linear(\n        #     in_features=role_embedding_dim * 2,\n        #     out_features=self.hidden_dim * 4)\n\n        # Is this the hidden value?\n        self.VR_embedding = nn.Parameter(\n            torch.from_numpy(np.zeros((1, sent_embedding_dim_SRL),\n                                      dtype='float32')))\n\n        self.map_dim = lstm_hidden_dim\n\n        # self.mlp_word = MLP(\n        #     in_features=2 * lstm_hidden_dim,\n        #     out_features=self.map_dim ,\n        #     activation=nn.LeakyReLU(0.1),\n        #     dropout=0.33)\n        # nn.init.orthogonal(self.mlp_word.weight)\n        # self.mlp_predicate = MLP(\n        #     in_features=2 * lstm_hidden_dim,\n        #     out_features=self.map_dim ,\n        #     activation=nn.LeakyReLU(0.1),\n        #     dropout=0.33)\n        # nn.init.orthogonal(self.mlp_predicate.weight)\n\n        self.Non_Predicate_Proj = nn.Linear(\n            2 * lstm_hidden_dim, lstm_hidden_dim)\n        self.Predicate_Proj = nn.Linear(2 * lstm_hidden_dim, lstm_hidden_dim)\n        self.W_R = nn.Parameter(torch.rand(\n            self.map_dim + 1, self.tagset_size * (self.map_dim + 1)))\n\n        # Init hidden state\n        # self.hidden = self.init_hidden_spe()\n        # self.hidden_2 = self.init_hidden_spe()\n        # self.hidden_3 = self.init_hidden_spe()\n        self.hidden_4 = self.init_hidden_share()\n\n    def init_hidden_share(self):\n        # Before we've done anything, we dont have any hidden state.\n        # Refer to the Pytorch documentation to see exactly\n        # why they have this dimensionality.\n        # The axes semantics are (num_layers, minibatch_size, hidden_dim)\n        # return (Variable(torch.zeros(1, self.batch_size, self.hidden_dim)),\n        #        Variable(torch.zeros(1, self.batch_size, self.hidden_dim)))\n        return (torch.zeros(3 * 2, self.batch_size, self.hidden_dim,\n                            requires_grad=False).to(device),\n                torch.zeros(3 * 2, self.batch_size, self.hidden_dim,\n                            requires_grad=False).to(device))\n\n    def init_hidden_spe(self):\n        # Before we've done anything, we dont have any hidden state.\n        # Refer to the Pytorch documentation to see exactly\n        # why they have this dimensionality.\n        # The axes semantics are (num_layers, minibatch_size, hidden_dim)\n        # return (Variable(torch.zeros(1, self.batch_size, self.hidden_dim)),\n        #        Variable(torch.zeros(1, self.batch_size, self.hidden_dim)))\n        return (torch.zeros(1 * 2, self.batch_size, self.hidden_dim,\n                            requires_grad=False).to(device),\n                torch.zeros(1 * 2, self.batch_size, self.hidden_dim,\n                            requires_grad=False).to(device))\n\n    def forward(self, sentence, p_sentence, pos_tags, lengths, target_idx_in,\n                region_marks, local_roles_voc, frames, local_roles_mask,\n                sent_pred_lemmas_idx, dep_tags, dep_heads, targets,\n                specific_dep_tags, specific_dep_relations,\n                test=False):\n        \"\"\"\n\n        \"\"\"\n\n        fixed_embeds = self.word_fixed_embeddings(p_sentence)\n        fixed_embeds = fixed_embeds.view(\n            self.batch_size, len(sentence[0]), self.word_emb_dim)\n\n        # Create sentence predicate lemma embeddigns\n        sent_pred_lemmas_embeds = self.p_lemma_embeddings(sent_pred_lemmas_idx)\n        embeds_SRL = self.word_embeddings_SRL(sentence)\n        embeds_SRL = embeds_SRL.view(\n            self.batch_size, len(sentence[0]), self.word_emb_dim)\n        pos_embeds = self.pos_embeddings(pos_tags)\n        region_marks = self.region_embeddings(region_marks).view(\n            self.batch_size, len(sentence[0]), 16)\n\n        # This seems to be the word representation mentioned in section 2.2\n        # x = x_re (embeds_SRL) o x_pe (fixed_embeds) o x_pos(pos_embeds) o\n        # o x_le(sent_pred_lemmas_embeds)\n        SRL_hidden_states = torch.cat(\n            (embeds_SRL, fixed_embeds, sent_pred_lemmas_embeds, pos_embeds,\n             region_marks), 2)\n        add_zero = torch.zeros(\n            (self.batch_size, 1, self.SRL_input_dim)).to(device)\n        # Concatenating VR_embedding with x (SRL_hidden_states)\n        # TODO Why is zero added?\n        SRL_hidden_states_cat = torch.cat(\n            (self.VR_embedding + add_zero, SRL_hidden_states), 1)\n        # Applying dropout to the tensor\n        SRL_hidden_states = self.SRL_input_dropout(SRL_hidden_states_cat)\n\n        # SRL layer\n        embeds_sort, lengths_sort, unsort_idx = self.sort_batch(\n            SRL_hidden_states, lengths+1)\n\n        # Packing the padded sequence to reduce computation\n        embeds_sort = rnn.pack_padded_sequence(\n            embeds_sort, lengths_sort.cpu().numpy(), batch_first=True)\n\n        # hidden states [time_steps * batch_size * hidden_units]\n        # Run the SRL BiLSTM\n        hidden_states, self.hidden_4 = self.BiLSTM_SRL(\n            embeds_sort, self.hidden_4)\n\n        # it seems that hidden states is already batch first, we don't need\n        # swap the dims\n        # hidden_states = hidden_states.permute(1, 2, 0).contiguous().view(\n        #   self.batch_size, -1, )\n\n        # Unpacking the hidden_states\n        hidden_states, lens = rnn.pad_packed_sequence(\n            hidden_states, batch_first=True)\n\n        # hidden_states = hidden_states.transpose(0, 1)\n\n        # Unsort the hidden_states\n        hidden_states = hidden_states[unsort_idx]\n        hidden_states = self.hidden_state_dropout(hidden_states)\n\n        # B * H\n        hidden_states_3 = hidden_states\n\n        # This next block appears only in the _VR file\n        target_idx_in = list(target_idx_in)\n        for i in range(len(target_idx_in)):\n            target_idx_in[i] += 1\n        target_idx_in = tuple(target_idx_in)\n\n        predicate_embeds = F.relu(\n            self.Predicate_Proj(\n                hidden_states_3[np.arange(0,\n                                          hidden_states_3.size()[0]),\n                                target_idx_in]))\n\n        hidden_states = F.relu(self.Non_Predicate_Proj(hidden_states))\n\n        # T * B * H\n        # added_embeds = torch.zeros(hidden_states_3.size()[1],\n        #   hidden_states_3.size()[0], hidden_states_3.size()[2]).to(device)\n        # predicate_embeds = added_embeds + predicate_embeds\n        # B * T * H\n        # predicate_embeds = predicate_embeds.transpose(0, 1)\n        # print(hidden_states)\n        # non-linear map and rectify the roles' embeddings\n        # roles = Variable(torch.from_numpy(np.arange(0, self.tagset_size)))\n\n        # B * roles\n        # log(local_roles_voc)\n        # log(frames)\n\n        bias_one = torch.ones(\n            (self.batch_size, len(sentence[0])+1, 1)).to(device)\n        hidden_states_word = torch.cat((hidden_states, Variable(bias_one)), 2)\n\n        bias_one = torch.ones((self.batch_size, 1)).to(device)\n        hidden_states_predicate = torch.cat(\n            (predicate_embeds, Variable(bias_one)), 1)\n\n        left_part = torch.mm(hidden_states_word.view(\n            self.batch_size * (len(sentence[0])+1), -1), self.W_R)\n        left_part = left_part.view(\n            self.batch_size, (len(sentence[0])+1) * self.tagset_size, -1)\n        hidden_states_predicate = hidden_states_predicate.view(\n            self.batch_size, -1, 1)\n        tag_space = torch.bmm(left_part, hidden_states_predicate).view(\n            (len(sentence[0])+1) * self.batch_size, -1)\n\n        SRLprobs = F.softmax(tag_space, dim=1)\n\n        # +++++++++++++++++++++++\n        wrong_l_nums = 0.0\n        all_l_nums = 1.0\n        right_noNull_predict = 1.0\n        noNull_predict = 1.0\n        noNUll_truth = 1.0\n\n        # +++++++++++++++++++++++\n        # wrong_l_nums_spe = 0.0\n        # all_l_nums_spe = 0.0\n\n        right_noNull_predict_spe = 1.0\n        noNull_predict_spe = 1.0\n        noNUll_truth_spe = 1.0\n        targets = targets.view(-1)\n        loss_function = nn.CrossEntropyLoss(ignore_index=0)\n\n        SRLloss = loss_function(tag_space, targets)\n\n        return SRLloss, 0, 0, 0, SRLprobs, wrong_l_nums, all_l_nums,\\\n            wrong_l_nums, all_l_nums, right_noNull_predict, noNull_predict,\\\n            noNUll_truth, right_noNull_predict_spe, noNull_predict_spe,\\\n            noNUll_truth_spe\n\n    @staticmethod\n    def sort_batch(tensor, lengths):\n        \"\"\"\n        Static method for sorting a batch of tensors based on lenghts\n\n        Returns the sorted tensor, their respective sorted lenghts and a list\n        of indexes thst can be used to reverse this sorting\n        \"\"\"\n\n        lengths = torch.from_numpy(np.asarray(lengths))\n        lengths_sorted, sorted_idx = lengths.sort(dim=0, descending=True)\n        tensor_sorted = tensor[sorted_idx]\n        _, unsort_idx = sorted_idx.sort()\n        return tensor_sorted, lengths_sorted, unsort_idx\n","sub_path":"nnet/nn_models/Biaffine_SRL_only_VR.py","file_name":"Biaffine_SRL_only_VR.py","file_ext":"py","file_size_in_byte":17753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"26317896","text":"#!/Users/admin/anaconda/bin/python\n\n\n# The file contains the edges of a directed graph. Vertices are labeled as positive integers from 1 to 875714. Every row indicates an edge, the vertex label in first column is the tail and the vertex label in second column is the head (recall the graph is directed, and the edges are directed from the first column vertex to the second column vertex). So for example, the 11th row looks liks : \"2 47646\". This just means that the vertex with label 2 has an outgoing edge to the vertex with label 47646\n\n# Your task is to code up the algorithm from the video lectures for computing strongly connected components (SCCs), and to run this algorithm on the given graph.\n\n# Output Format: You should output the sizes of the 5 largest SCCs in the given graph, in decreasing order of sizes, separated by commas (avoid any spaces). So if your algorithm computes the sizes of the five largest SCCs to be 500, 400, 300, 200 and 100, then your answer should be \"500,400,300,200,100\" (without the quotes). If your algorithm finds less than 5 SCCs, then write 0 for the remaining terms. Thus, if your algorithm computes only 3 SCCs whose sizes are 400, 300, and 100, then your answer should be \"400,300,100,0,0\" (without the quotes). (Note also that your answer should not have any spaces in it.)\n\n# WARNING: This is the most challenging programming assignment of the course. Because of the size of the graph you may have to manage memory carefully. The best way to do this depends on your programming language and environment, and we strongly suggest that you exchange tips for doing this on the discussion forums.\n\nimport sys\nfrom copy import deepcopy\n\n# fname = '/Users/admin/Dropbox/Learn2Code/Coursera/StanfordAlgorithms/week5/assignment/SCC.txt'\nfname = '/Users/admin/Dropbox/Learn2Code/Coursera/StanfordAlgorithms/week5/assignment/test1.txt'\n\n# list of each edge\nedgeList = []\nadjList = []\n\n# track max value of max node\nmaxNodeNum = 0\n\ng = open(fname)\n# edgeList\nfor line in g:\n    u = int(line.split()[0])\n    v = int(line.split()[1])\n    edgeList.append([u, v])\n    # determine max V number\n    if u > maxNodeNum: maxNodeNum = u\n    elif v > maxNodeNum: maxNodeNum = v\n\nprint(edgeList)\nprint(maxNodeNum)\n\n# array with value x for each V\n# [vertex, firstVisted, firstFinish,\n# vertList = [ [x, 0, None] for x in range(0, maxNodeNum + 1) ]\n# print(vertList)\n\n# build adjacency list from vectList & edgeList\n# for V in range(0, maxNodeNum + 1):\n#     adjList.append([V, []])\n# for edge in edgeList:\n#     adjList[edge[0]][1].append(edge[1])\n# print(adjList)\n\n# Test adjList->edgeList\n# for V in adjList:\n#     for edge in V[1]:\n#         print((V[0],edge))\n\n# edgeList is a list of each edge\n# adjList is a list of each Vertex and the other Vertex(s) it points to\n# vertList is a list of each Vertex, and if it's been visited\n\n# class Graph(edgeList, maxNodeNum):\nclass Graph():\n\n    def __init__(self):\n        #     [Vertex, firstVisited, finTime, secondVisited leader]\n        self.vertList = [ [x, 0, 0, 0] for x in range(0,\n                                                                maxNodeNum +\n                                                           1) ]\n        self.adjList = []\n        self.adjListRev = []\n        self.numVertex = maxNodeNum\n\n        # build adjList for natural graph\n        for V in range(0, maxNodeNum + 1):\n            self.adjList.append([V, []])\n        for edge in edgeList:\n            self.adjList[edge[0]][1].append(edge[1])\n\n        # build adjListRev for Grev\n        for V in range(0, maxNodeNum + 1):\n            self.adjListRev.append([V, []])\n        for edge in edgeList:\n            self.adjListRev[edge[1]][1].append(edge[0])\n\ng = Graph()\nprint(\"vertList [Vertex, firstVisited, finTime, secondVisited, leader]:\\n\",\n      g.vertList)\nprint(\"g.adjList:\\n\", g.adjList)\nprint(\"g.adjListRev:\\n\", g.adjListRev)\n\nt = 0\ns = None\n\n# entry to DFS - takes a graph object\ndef DFS_Loop(G):\n    global s, t\n    # Move through adjListRev from maxVertex to minVertex\n    for i in range(G.numVertex, 1, -1):\n        if G.vertList[i][1] == 0: # Vertex unexplored\n            s = G.vertList[i][0]\n            DFS(G,i)\n\ndef DFS(G, i):\n    global s, t\n    G.vertList[i][1] = 1\n    # set leader = s\n    G.vertList[i][3] = s\n    # leader = s\n    for p in G.adjListRev[i][1]: # i = current Vertex; p = iterative point\n        if G.vertList[p][1] == 0:\n            DFS(G, p)\n    t+=1\n    G.vertList[i][2] = t\n    # G.vertList[i][3] = leader\n\nDFS_Loop(g)\n\nprint(\"## First Pass\")\n# sort vertList by finishing times\ng.vertList = sorted(g.vertList, key=lambda x: x[2])\n# reset visited flag\nfor vert in g.vertList:\n    vert[1] = 0\nprint(\"vertList [Vertex, firstVisited, finTime, leader]:\\n\", g.vertList)\nprint(\"g.adjList:\\n\", g.adjList)\nprint(\"g.adjListRev:\\n\", g.adjListRev)\n\n\n# print(g.vertList)\n\nDFS_Loop(g)\n\nprint(\"## Second Pass\")\nprint(\"vertList [Vertex, firstVisited, finTime, leader]:\\n\", g.vertList)\n\n\n\n\n\n# sort vertList by finishing time\n\n## Notes\n# DFS-Loop(graph G)\n#   mark all nodes unexplored\n#   current-label = n  (to keep track of ordering)\n\n# for each vertex\n#     if v not yet explored (in previous DFS call)\n#         DFS(G, v)\n\n# DFS(graph G, start vertex s)\n#     for every edge (s,v)\n#         if v not yet explored\n#             mark v explored\n#             DFS(G, v)\n#     set f(s) = current_label\n#     current_label = current_label - 1\n\n# Kosaraju's Two-Pass Algorithm\n# 1) Let Grev = G with all arvs reversed\n# 2) Run DFS-Loop on Grev (Goal: computer \"magical ordering\" of nodes)\n#     Let f(v) = \"finishing time\" of each V in V\n# 1) Run DFS-Loop on G (Goal: discover the SCCs one-by-one)\n#     processing nodes in decreasing order of finishing times\n# [SCCs = nodes with the same \"leader\"]\n\n## DFS-Loop\n# DFS-Loop(graph G)\n#   Global variable t = 0 (For finishing times in 1st pass)\n#       [# of nodes processed so far]\n#   Global variable s = NULL (For leaders in 2nd pass)\n#       [current source vertex]\n#   Assume nodes labeled 1 to n\n#   For i = n down to 1\n#       if i not yet explored\n#           s = i\n#           DFS(G, i)\n#\n# DFS(graph G, node i)\n#     mark i as explored (For rest of DFS-Loop)\n#     set leader(i) = node s\n#     for each arc(i,j) in G\n#         if j not yet explored\n#             DFS(G, j)\n#     t++\n#     set f(i) = t (i's finishing time)\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"specialization-stanford-algorithms/divide-and-conquer-sorting-and-searching-and-randomized-algorithms/week5/assignment/computeSCC.py","file_name":"computeSCC.py","file_ext":"py","file_size_in_byte":6370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"554068684","text":"import math\n\n\nclass Solution:\n    def minFallingPathSum(self, A):\n        \"\"\"\n        :type A: List[List[int]]\n        :rtype: int\n        \"\"\"\n        rows = len(A)\n        cols = len(A[0])\n        dp = A[0][:]\n        for r in range(1, rows):\n            ndp = []\n            for c in range(cols):\n                ndp.append(min(dp[c - 1] if c > 0 else math.inf, dp[c],\n                               dp[c + 1] if c < cols - 1 else math.inf) + A[r][c])\n            dp = ndp\n        return min(dp)\n","sub_path":"src/minimum-falling-path-sum.py","file_name":"minimum-falling-path-sum.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"135085617","text":"\"\"\"Implemention of various Tractography metods\n\nthese tools are ment to be paired with diffusion reconstruction methods from\ndipy.reconst\n\nThis module uses the trackvis coordinate system, for more information about\nthis coordinate system please see dipy.tracking.utils\nThe following modules also use this coordinate system:\ndipy.tracking.utils\ndipy.tracking.integration\ndipy.reconst.interpolate\n\"\"\"\nfrom __future__ import division\nfrom numpy import array, asarray, broadcast_arrays, signbit, sqrt\n\nclass BoundryIntegrator(object):\n    \"\"\"Integrates along step until the closest voxel boundry\"\"\"\n    def __init__(self, voxel_size=(1, 1, 1), overstep=.1):\n        \"\"\"Creates a BoundryIntegrator instance\n\n        Parameters:\n        -----------\n        voxel_size : array-like\n            Size of voxels in data volume\n        overstep : float\n            A small number used to prevent the track from getting stuck at the\n            edge of a voxel.\n        \"\"\"\n        self.overstep = overstep\n        self.voxel_size = asarray(voxel_size, 'float')\n\n    def integrate(self, location, step):\n        \"\"\"takes a step just past the edge of the next voxel along step\n\n        given a location and a step, finds the smallest step needed to move\n        into the next voxel\n\n        Parameters:\n        -----------\n        location : ndarray, (3,)\n            location to integrate from\n        step : ndarray, (3,)\n            direction in 3 space to integrate along\n        \"\"\"\n        step_sizes = self.voxel_size*(~signbit(step))\n        step_sizes -= location % self.voxel_size\n        step_sizes /= step\n        smallest_step = min(step_sizes) + self.overstep\n        return location + smallest_step*step\n\nclass FixedStepIntegrator(object):\n    \"\"\"An Intigrator that uses a fixed step size\"\"\"\n    def __init__(self, step_size=.5):\n        \"\"\"Creates an Intigrator\"\"\"\n        self.step_size = step_size\n    def integrate(self, location, step):\n        \"\"\"Takes a step of step_size from location\"\"\"\n        new_location = self.step_size*step + location\n        return new_location\n\ndef generate_streamlines(stepper, integrator, seeds, start_steps, maxlen=500):\n    \"\"\"Creates streamlines using a stepper and integrator\n\n    Creates streamlines starting at each of the seeds, and integrates them\n    using the integrator, getting the direction of each step from stepper.\n\n    Parameters:\n    -----------\n    stepper : object with next_step method\n        The next_step method should take a location, (x, y, z) cordinates in\n        3-space, and the previous step, a unit vector in 3-space, and return a\n        new step\n    integrator : object with a integrate method\n        The integrate method should take a location and a step, as defined\n        above and return a new location.\n    seeds : array-like (N, 3)\n        Locations where to start tracking.\n    start_steps : array-like\n        start_steps should be broadcastable with seeds and is used in place of\n        prev_step when determining the initial step for each seed.\n\n    Returns:\n    --------\n    streamlines : generator\n        A generator of streamlines, each streamline is an (M, 3) array of\n        points in 3-space.\n    \"\"\"\n    start_steps = asarray(start_steps)\n    norm_start_steps = sqrt((start_steps*start_steps).sum(-1))\n    norm_start_steps = norm_start_steps.reshape((-1,1))\n    start_steps = start_steps/norm_start_steps\n    seeds, start_steps = broadcast_arrays(seeds, start_steps)\n\n    next_step = stepper.next_step\n    integrate = integrator.integrate\n    for ii in xrange(len(seeds)):\n        location = seeds[ii]\n        step = start_steps[ii]\n        streamline = []\n        try:\n            for i in xrange(maxlen):\n                step = next_step(location, step)\n                streamline.append(location)\n                location = integrate(location, step)\n        except IndexError:\n            pass\n        except StopIteration:\n            streamline.append(location)\n        streamline = array(streamline)\n        yield streamline\n\n","sub_path":"dipy/tracking/integration.py","file_name":"integration.py","file_ext":"py","file_size_in_byte":4026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"529053069","text":"import pandas as pd\n\n\ndef read_local_data(data_dir):\n    static_vars = pd.read_csv(data_dir + 'static_vars.csv')\n    dynamic_vars = pd.read_csv(data_dir + 'dynamic_vars.csv')\n    outcome_vars = pd.read_csv(data_dir + 'outcome_vars.csv')\n    input_vars = pd.read_csv(data_dir + 'input_vars.csv')\n\n    return static_vars, dynamic_vars, outcome_vars, input_vars\n\ndef read_all_data(data_dir):\n    inpatients_records = pd.read_csv(\n        data_dir + 'CDSL_01.csv', sep=',')\n    inpatients_records.rename({'PATIENT ID': 'patient_id',\n                               'EDAD/AGE': 'age',\n                               'SEXO/SEX': 'sex',\n                               'DIAG ING/INPAT': 'inpatient_covid_dx',\n                               'F_INGRESO/ADMISSION_D_ING/INPAT': 'inpatient_admit_date',\n                               'F_ENTRADA_UC/ICU_DATE_IN': 'icu_admit_date',\n                               'F_SALIDA_UCI/ICU_DATE_OUT': 'icu_discharge_date',\n                               'UCI_DIAS/ICU_DAYS': 'icu_days',\n                               'F_ALTA/DISCHARGE_DATE_ING': 'inpatient_discharge_date',\n                               'MOTIVO_ALTA/DESTINY_DISCHARGE_ING': 'discharge_destination',\n                               'F_INGRESO/ADMISSION_DATE_URG/EMERG': 'emergency_visit_date',\n                               'HORA/TIME_ADMISION/ADMISSION_URG/EMERG': 'emergency_visit_time',\n                               'ESPECIALIDAD/DEPARTMENT_URG/EMERG': 'emergency_visit_department',\n                               'DIAG_URG/EMERG': 'emergency_visit_dx',\n                               'DESTINO/DESTINY_URG/EMERG': 'emergency_visit_destination',\n                               'HORA/TIME_CONSTANT_PRIMERA/FIRST_URG/EMERG': 'emergency_first_vitals_record_time',\n                               'TEMP_PRIMERA/FIRST_URG/EMERG': 'emergency_first_temperature',\n                               'FC/HR_PRIMERA/FIRST_URG/EMERG': 'emergency_first_heart_rate',\n                               'GLU_PRIMERA/FIRST_URG/EMERG': 'emergency_first_glucose',\n                               'SAT_02_PRIMERA/FIRST_URG/EMERG': 'emergency_first_spo2',\n                               'TA_MAX_PRIMERA/FIRST/EMERG_URG': 'emergency_first_max_bp',\n                               'TA_MIN_PRIMERA/FIRST_URG/EMERG': 'emergency_first_min_bp',\n                               'HORA/TIME_CONSTANT_ULTIMA/LAST_URG/EMERG': 'emergency_last_vitals_record_time',\n                               'FC/HR_ULTIMA/LAST_URG/EMERG': 'emergency_last_temperature',\n                               'TEMP_ULTIMA/LAST_URG/EMERG': 'emergency_last_heart_rate',\n                               'GLU_ULTIMA/LAST_URG/EMERG': 'emergency_last_glucose',\n                               'SAT_02_ULTIMA/LAST_URG/EMERG': 'emergency_last_spo2',\n                               'TA_MAX_ULTIMA/LAST_URGEMERG': 'emergency_last_max_bp',\n                               'TA_MIN_ULTIMA/LAST_URG/EMERG': 'emergency_last_min_bp',\n                               }, axis=1, inplace=True)\n\n    for timecol in ['inpatient_admit_date',\n                    'inpatient_discharge_date',\n                    'icu_admit_date',\n                    'icu_discharge_date', ]:\n        inpatients_records.loc[:, timecol] = pd.to_datetime(\n            inpatients_records[timecol], dayfirst=True)\n\n    drugs = pd.read_csv(data_dir + 'CDSL_04.csv', sep=';',\n                        encoding='unicode_escape')\n    drugs.rename({'IDINGRESO': 'patient_id',\n                    'DRUG_COMERCIAL_NAME': 'drug_name',\n                    'DAILY_AVRG_DOSE': 'average_daily_dose',\n                    'DRUG_START_DATE': 'start_date',\n                    'DRUG_END_DATE': 'end_date',\n                    'ATC5_NAME': 'ATC5_description',\n                    'ID_ATC5': 'ATC5_id',\n                    'ATC7_NAME': 'ATC7_description',\n                    'ID_ATC7': 'ATC7_id'}, axis=1, inplace=True)\n\n    for timecol in ['start_date', 'end_date']:\n        drugs.loc[:, timecol] = pd.to_datetime(\n            drugs[timecol], dayfirst=True)\n\n    # this next dataframe is not great, only (in my trials) gets data after icu discharge, like in\n    # an observation period\n    clinical_vars = pd.read_csv(\n        data_dir + 'CDSL_02.csv', sep=';', encoding='unicode_escape')\n\n    clinical_vars.rename({'IDINGRESO': 'patient_id',\n                          'CONSTANTS_ING_DATE': 'record_date',\n                          'CONSTANTS_ING_TIME': 'record_time',\n                          'FC_HR_ING': 'heart_rate',\n                          'GLU_GLY_ING': 'glucose',\n                          'SAT_02_ING': 'spo2',\n                          'SAT_02_ING_OBS': 'spo2_obs',\n                          'TA_MAX_ING': 'max_bp',\n                          'TA_MIN_ING': 'min_bp',\n                          'TEMP_ING': 'temperature'}, axis=1, inplace=True)\n    clinical_vars.loc[:, 'datetime'] = pd.to_datetime(clinical_vars['record_date'] + ' ' + clinical_vars['record_time'],\n                                                      dayfirst=True)\n    clinical_vars.drop(['record_date', 'record_time'], axis=1, inplace=True)\n\n    # need to find better way to read this one\n    # note this skips lots of lines with extra descriptions\n        \n    labs = pd.read_csv(data_dir + 'CDSL_06.csv', sep=';',\n                        encoding='unicode_escape',\n                        usecols=['IDINGRESO',\n                                    'LAB_NUMBER',\n                                    'LAB_DATE',\n                                    'TIME_LAB',\n                                    'ITEM_LAB',\n                                    'VAL_RESULT',\n                                    'UD_RESULT',\n                                    'REF_VALUES', ])\n\n    labs.rename({'IDINGRESO': 'patient_id',\n                    'LAB_NUMBER': 'lab_request_id',\n                    'LAB_DATE': 'lab_date',\n                    'TIME_LAB': 'lab_time',\n                    'ITEM_LAB': 'label',\n                    'VAL_RESULT': 'value',\n                    'UD_RESULT': 'uom',\n                    'REF_VALUES': 'ref_values', }, axis=1, inplace=True)\n\n    labs.loc[:, 'datetime'] = pd.to_datetime(labs['lab_date'] + ' ' + labs['lab_time'],\n                                             dayfirst=True)\n    labs.drop(['lab_date', 'lab_time'], axis=1, inplace=True)\n\n    labs_v2 = pd.read_csv(data_dir + 'CDSL_06_v2.csv', sep=';',\n                        encoding='unicode_escape',\n                        usecols=['IDINGRESO',\n                                    'LAB_NUMBER',\n                                    'LAB_DATE',\n                                    'TIME_LAB',\n                                    'ITEM_LAB',\n                                    'VAL_RESULT',\n                                    'UD_RESULT',\n                                    'REF_VALUES', ])\n\n    labs_v2.rename({'IDINGRESO': 'patient_id',\n                    'LAB_NUMBER': 'lab_request_id',\n                    'LAB_DATE': 'lab_date',\n                    'TIME_LAB': 'lab_time',\n                    'ITEM_LAB': 'label',\n                    'VAL_RESULT': 'value',\n                    'UD_RESULT': 'uom',\n                    'REF_VALUES': 'ref_values', }, axis=1, inplace=True)\n\n    labs_v2.loc[:, 'datetime'] = pd.to_datetime(labs_v2['lab_date'] + ' ' + labs_v2['lab_time'],\n                                             dayfirst=True)\n    labs_v2.drop(['lab_date', 'lab_time'], axis=1, inplace=True)\n\n    codes_emergency = pd.read_csv(\n        data_dir + 'CDSL_03.csv', sep=';', encoding='unicode_escape')\n    codes_emergency.rename({'IDINGRESO': 'patient_id'}, axis=1, inplace=True)\n\n    codes_inpatient = pd.read_csv(\n        data_dir + 'CDSL_05.csv', sep=';', encoding='unicode_escape')\n    codes_inpatient.rename({'IDINGRESO': 'patient_id'}, axis=1, inplace=True)\n\n    return inpatients_records, drugs, clinical_vars, labs, labs_v2, codes_emergency, codes_inpatient\n","sub_path":"src/data/data_utils/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":7884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"12668363","text":"import os\nimport setuptools\n\nrequirements = []\npath = os.path.dirname(os.path.realpath(__file__)) + '/requirements.txt'\nif os.path.isfile(path):\n    try:\n        with open(path) as f:\n            requirements = f.read().split()\n    except Exception as ex:\n        print(ex)\n\nsetuptools.setup(\n    name='plag-cli',\n    version='1.0',\n    author='divyansh',\n    description='A CLI application to detect plagiarism in source code files.',\n    packages=setuptools.find_packages(),\n    install_requires=requirements,\n    entry_points={\n        'console_scripts': [\n            'plag = package.__main__:start',\n        ]\n    }\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"425918413","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.feature_extraction.text import TfidfTransformer, CountVectorizer, TfidfVectorizer\nfrom nltk.corpus import stopwords\nfrom sklearn.cluster import KMeans\n\ndef open_file():\n    with open('football.txt') as f_in:\n        football = f_in.readlines()\n\n    return football\n\ndef get_datetime(football):\n    data_time = []\n    for i in football[1:]:\n        data_time.append(i.split('\\t')[1])\n\n    data_time = [data_time[i][:10] for i in range(len(data_time))]\n    data_time = pd.DataFrame(data_time, columns=['date'])\n\n    return data_time\n\ndef get_text(data_time, football):\n    text = []\n    for i in football[1:]:\n        text.append(i.split('\\t')[0])\n\n    data_time['text'] = text\n    return data_time\n\n\ndef preproc_data(data_time):\n    data_time['date'] = pd.to_datetime(data_time['date'])\n    data_time = data_time.sort_values(by='date')\n\n    return data_time\n\n\ndef resize_logs(logs):\n    if len(logs) < 10000000:\n        for i in [4, 2, 2]:\n            logs = logs[::i]\n        # print(len(logs))\n        return logs\n\n    elif len(logs) > 20000000:\n        for i in [4, 2, 2, 2]:\n            logs = logs[::i]\n        # print(len(logs))\n        return logs\n\n    else:\n        for i in range(2, 4):\n            logs = logs[::i]\n        # print(len(logs))\n        return logs\n\ndef stage_split(data_time):\n    text_begin = data_time[data_time['date'].isin(pd.date_range(start='2018-06-14', end='2018-06-20'))]['text'].values\n    text_middle = data_time[data_time['date'].isin(pd.date_range(start='2018-06-21', end='2018-07-07'))]['text'].values\n    text_final = data_time[data_time['date'].isin(pd.date_range(start='2018-07-08', end='2018-07-15'))]['text'].values\n\n    text_begin = resize_logs(text_begin)\n    text_middle = resize_logs(text_middle)\n    text_final = resize_logs(text_final)\n\n    return text_begin, text_middle, text_final\n\n\ndef cluster_logs(text, n_clusters=10):\n    stopWords = stopwords.words('russian')\n    vectorizer = TfidfVectorizer(stop_words=stopWords)\n    X = vectorizer.fit_transform(text)\n    true_k = n_clusters\n    model = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1)\n    model.fit(X)\n    # print(\"Top terms per cluster:\")\n    order_centroids = model.cluster_centers_.argsort()[:, ::-1]\n    terms = vectorizer.get_feature_names()\n    cluster_words = []\n    for i in range(true_k):\n        with open('clusters.txt', 'a') as f_out:\n            # print(\"Cluster %d:\" % i),\n            f_out.write((\"Cluster %d:\" % i))\n            for ind in order_centroids[i, :10]:\n                # print(' %s ' % terms[ind], end=' '),\n                f_out.write((' %s ' % str(terms[ind])))\n            f_out.write(('\\n'))\n            # print()\n        with open('clusters.txt', 'a') as f_out:\n            f_out.write('\\n')\n        print('clister %d:' % i ,'is done.')\n    with open('clusters.txt', 'a') as f_out:\n        f_out.write('---------------------------------------------')\n        f_out.write('\\n')\n\n\ndef get_cluster(text_begin, text_middle, text_final):\n    cluster_logs(text_begin)\n    cluster_logs(text_middle)\n    cluster_logs(text_final)\n\n\ndef main():\n    print('run.')\n    football = open_file()\n    print('open file done.')\n    data_time = get_datetime(football)\n    print('wait...')\n    data_time = get_text(data_time, football)\n    print('get data done')\n    data_time = preproc_data(data_time)\n    print('wait...')\n    text_begin, text_middle, text_final = stage_split(data_time)\n    print('stage split done')\n    get_cluster(text_begin, text_middle, text_final)\n    print('result save in cluster.txt')\n    print('done')\n\n\n\nif __name__ == '__main__':\n    main()","sub_path":"cluster_logs/Logs_cluster_small.py","file_name":"Logs_cluster_small.py","file_ext":"py","file_size_in_byte":3662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"174728454","text":"#Software Todo:\n\n#Add UI\n#   button for things like doc_type and makePDF\n\n#Add settings menu:\n#    change things like output path and ui color permanently\n#    Prompt user to permanently save their user information so it doesn't have to be re-entered every time\n\n#Figure out PDF issues\n#    seems to only print pdfs on drive that has word installed (Could be something else)\n\n#Add database for providers\n#    Autocomplete provider information with data in database\n\n\n\n\n\nimport os\nimport re\nimport time\nfrom datetime import datetime\nfrom docx import Document\nimport comtypes.client\n\n#Path to files\n#new comment\n#another comment\npath = os.getcwd()\nlop_pl = (path + \"\\placeholders\\LoP.docx\")\nrr_pl = (path + \"\\placeholders\\RR.docx\")\nlor_pl = (path + \"\\placeholders\\LoR.docx\")\n\n#define replace_string , docxtopdf, and set values\n\ndef DocxtoPDF(inputFileName, outputFileName, formatType = 17):\n    Word = comtypes.client.CreateObject(\"Word.Application\")\n    Word.Visible = 1\n\n    if outputFileName[-3:] != 'pdf':\n        outputFileName = outputFileName + \".pdf\"\n    deck = Word.Documents.Open(inputFileName)\n    deck.SaveAs(outputFileName, formatType)\n    deck.Close()\n    Word.Quit()\n\nprint(DocxtoPDF)\n\ndef replace_string(doc_obj, regex, replace):\n    for p in doc_obj.paragraphs:\n        if regex.search(p.text):\n            inline = p.runs\n            for i in range(len(inline)):\n                if regex.search(inline[i].text):\n                    text = regex.sub(replace, inline[i].text)\n                    inline[i].text = text\n\n    for table in doc_obj.tables:\n        for row in table.rows:\n            for cell in row.cells:\n                replace_string(cell, regex, replace)\n\nprovider_int = 0\n\n#Placeholder values\n\n#LoP and universal\n\nprovider_email_PL = re.compile(\"Provider Email\")\nprovider_name_PL = re.compile(\"Provider Name\")\nclient_name_PL = re.compile(\"Client Name\")\ndoa_PL = re.compile(\"Date of Accident\")\ntoday_date_PL = re.compile(\"Today Date\")\nprovider_address_PL = re.compile(\"Provider Address\")\nlop_amount_PL = re.compile(\"LOP Amount\")\nattorney_name_PL = re.compile(\"Attorney Name\")\n\n#LoR\n\ndef_insurance_pl = re.compile(\"Defendant Insurance\")\nclaim_num_pl = re.compile(\"CNumber\")\ndef_adjuster_name_pl = re.compile(\"AdjusterName\")\ndef_adjuster_address_pl = re.compile(\"Defendant Adjuster Address\")\ndef_adjuster_csz_pl = re.compile(\"Defendant Adjuster CSZ\")\ndef_adjuster_fax_pl = re.compile(\"Defendant Adjuster Fax\")\nuser_name_pl = re.compile(\"User Name\")\nuser_number_pl = re.compile(\"User Number\")\n\n#RR\n\nuser_fax_pl = re.compile(\"User Fax\")\nuser_email_pl = re.compile(\"User Email\")\ncurrent_balance_pl = re.compile(\"Current Balance\")\nrequested_balance_pl = re.compile(\"Requested Balance\")\n\n#Create date\nnow = datetime.now()\ntoday_date = now.strftime('%m/%d/%Y')\n\n#prompt users for input\ndoc_type = input('Document Type (RR/LOP/LOR): ')\nclient_name = input(\"Client Name: \")\ndoa = input('Date of Accident: ')\nattorney_name= input(\"Attorney's Name: \")\n\nif doc_type == \"LOR\":\n    user_name = input(\"Your Name: \")\n    user_num = input(\"Your Phone Number: \")\n    def_insurance = input(\"Defendant's Insurance Company: \")\n    claim_num = input(\"Claim Number: \")\n    def_adjuster_name = input(\"Defendant Adjuster's Name: \")\n    def_adjuster_address = input(\"Defendant Adjuster's Address: \")\n    def_adjuster_csz = input(\"Defendant Adjuster's City, State, Zip: \")\n    def_adjuster_fax = input(\"Defendant Adjuster's Fax Number: \")\n\n\nif doc_type == \"RR\":\n    user_name = input(\"Your Name: \")\n    user_email = input(\"Your Email: \")\n    user_fax = input(\"Your Fax Number: \")\n\nif doc_type == \"LOP\":\n    lop_amount = input(\"LoP Amount: \")\n\n\nif not doc_type == \"LOR\":\n    provider_num = input(\"How many providers?(1-10): \")\n    provider_int = int(provider_num)\n    provider_name = input(\"First Provider Name: \")\n    provider_email = input(provider_name + \"'s Email/Fax: \")\n    provider_address = input(provider_name + \"'s Address: \")\n\nif doc_type == \"RR\":\n    current_balance = input(provider_name + \"'s Current Balance: \")\n    requested_balance = input(provider_name + \"'s Requested Balance: \")\n\n\n\n\n\n\nif provider_int > 1:\n    provider_name2 = input(\"Second Provider Name: \")\n    provider_email2 = input(provider_name2 + \"'s Email/Fax: \")\n    provider_address2 = input(provider_name2 + \"'s Address: \")\n    if doc_type == \"RR\":\n        current_balance2 = input(provider_name2 + \"'s Current Balance: \")\n        requested_balance2 = input(provider_name2 + \"'s Requested Balance: \")\n\nif provider_int > 2:\n    provider_name3 = input(\"Third Provider Name: \")\n    provider_email3 = input(provider_name3 + \"'s Email/Fax: \")\n    provider_address3 = input(provider_name3 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance3 = input(provider_name3 + \"'s Current Balance: \")\n        requested_balance3 = input(provider_name3 + \"'s Requested Balance: \")\n\nif provider_int > 3:\n    provider_name4 = input(\"Fourth Provider Name: \")\n    provider_email4 = input(provider_name4 + \"'s Email/Fax: \")\n    provider_address4 = input(provider_name4 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance4 = input(provider_name4 + \"'s Current Balance: \")\n        requested_balance4 = input(provider_name4 + \"'s Requested Balance: \")\n\nif provider_int > 4:\n    provider_name5 = input(\"Fifth Provider Name: \")\n    provider_email5 = input(provider_name5 + \"'s Email/Fax: \")\n    provider_address5 = input(provider_name5 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance5 = input(provider_name5 + \"'s Current Balance: \")\n        requested_balance5 = input(provider_name5 + \"'s Requested Balance: \")\n\nif provider_int > 5:\n    provider_name6 = input(\"Sixth Provider Name: \")\n    provider_email6 = input(provider_name6 + \"'s Email/Fax: \")\n    provider_address6 = input(provider_name6 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance6 = input(provider_name6 + \"'s Current Balance: \")\n        requested_balance6 = input(provider_name6 + \"'s Requested Balance: \")\n\nif provider_int > 6:\n    provider_name7 = input(\"Seventh Provider Name: \")\n    provider_email7 = input(provider_name7 + \"'s Email/Fax: \")\n    provider_address7 = input(provider_name7 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance7 = input(provider_name7 + \"'s Current Balance: \")\n        requested_balance7 = input(provider_name7 + \"'s Requested Balance: \")\n\nif provider_int > 7:\n    provider_name8 = input(\"Eighth Provider Name: \")\n    provider_email8 = input(provider_name8 + \"'s Email/Fax: \")\n    provider_address8 = input(provider_name8 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance8 = input(provider_name8 + \"'s Current Balance: \")\n        requested_balance8 = input(provider_name8 + \"'s Requested Balance: \")\n\nif provider_int > 8:\n    provider_name9 = input(\"Ninth Provider Name: \")\n    provider_email9 = input(provider_name9 + \"'s Email/Fax: \")\n    provider_address9 = input(provider_name9 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance9 = input(provider_name9 + \"'s Current Balance: \")\n        requested_balance9 = input(provider_name9 + \"'s Requested Balance: \")\n\nif provider_int > 9:\n    provider_name10 = input(\"Tenth Provider Name: \")\n    provider_email10 = input(provider_name10 + \"'s Email/Fax: \")\n    provider_address10 = input(provider_name10 + \"'s Address: \")\n\n    if doc_type == \"RR\":\n        current_balance10 = input(provider_name10 + \"'s Current Balance: \")\n        requested_balance10 = input(provider_name10 + \"'s Requested Balance: \")\n\n\n#link placeholder document and make path\nif doc_type == \"LOP\":\n\n    doc = Document(lop_pl)\n\n    if not os.path.exists('/DocuLegal/LOPs/Word'):\n        os.makedirs('/DocuLegal/LOPs/Word')\n\n    if not os.path.exists('/DocuLegal/LOPs/PDF'):\n        os.makedirs('/DocuLegal/LOPs/PDF')\n\n    doc.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name.upper() + \".docx\")\n\n\nif doc_type == \"RR\":\n\n    doc = Document(rr_pl)\n\n    if not os.path.exists('/DocuLegal/Reduction Requests/Word'):\n        os.makedirs('/DocuLegal/Reduction Requests/Word')\n\n    if not os.path.exists('/DocuLegal/Reduction Requests/PDF'):\n        os.makedirs('/DocuLegal/Reduction Requests/PDF')\n\n    doc.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name.upper() + \".docx\")\n\n\nif doc_type == \"LOR\":\n\n    doc = Document(lor_pl)\n\n    if not os.path.exists('/DocuLegal/LORs/Word'):\n        os.makedirs('/DocuLegal/LORs/Word')\n\n    if not os.path.exists('/DocuLegal/LORs/PDF'):\n        os.makedirs('/DocuLegal/LORs/PDF')\n\n    doc.save('/DocuLegal/LORs/Word/' + client_name.upper() + ' LOR ' + def_insurance.upper()+\".docx\")\n\n#replace placeholders with user input\nreplace_string(doc, client_name_PL, client_name)\nreplace_string(doc, doa_PL, doa)\nreplace_string(doc, today_date_PL, today_date)\n\n\n#LOR\nif doc_type == \"LOR\":\n    replace_string(doc, client_name_PL, client_name)\n    replace_string(doc, def_insurance_pl, def_insurance)\n    replace_string(doc, claim_num_pl, claim_num)\n    replace_string(doc, def_adjuster_name_pl, def_adjuster_name)\n    replace_string(doc, def_adjuster_address_pl, def_adjuster_address)\n    replace_string(doc, def_adjuster_csz_pl, def_adjuster_csz)\n    replace_string(doc, def_adjuster_fax_pl, def_adjuster_fax)\n    replace_string(doc, user_name_pl, user_name)\n    replace_string(doc, user_number_pl, user_num)\n    replace_string(doc, attorney_name_PL, attorney_name)\n    replace_string(doc, def_adjuster_name_pl, def_adjuster_name)\n\n#LOP\nif doc_type == \"LOP\":\n    replace_string(doc, provider_address_PL, provider_address)\n    replace_string(doc, provider_email_PL, provider_email)\n    replace_string(doc, provider_name_PL, provider_name)\n    replace_string(doc, lop_amount_PL, lop_amount)\n    replace_string(doc, attorney_name_PL, attorney_name)\n\n#RR\nif doc_type == \"RR\":\n    replace_string(doc, user_name_pl, user_name)\n    replace_string(doc, user_fax_pl, user_fax)\n    replace_string(doc, user_email_pl, user_email)\n\n    replace_string(doc, current_balance_pl, current_balance)\n    replace_string(doc, requested_balance_pl, requested_balance)\n    replace_string(doc, provider_email_PL, provider_email)\n    replace_string(doc, provider_name_PL, provider_name)\n\n#save\nif doc_type == \"LOP\":\n    doc.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name.upper() + \".docx\")\nif doc_type == \"RR\":\n    doc.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name.upper() + \".docx\")\nif doc_type == \"LOR\":\n    doc.save('/DocuLegal/LORs/Word/' + client_name.upper() + ' LOR ' + def_insurance.upper() + \".docx\")\n\n#loop for extra providers\nif provider_int > 1:\n    if doc_type == \"LOP\":\n        doc2 = Document(lop_pl)\n        doc2.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name2.upper() + \".docx\")\n        replace_string(doc2, lop_amount_PL, lop_amount)\n        replace_string(doc2, provider_address_PL, provider_address2)\n        replace_string(doc2, provider_email_PL, provider_email2)\n        replace_string(doc2, provider_name_PL, provider_name2)\n\n    if doc_type == \"RR\":\n        doc2 = Document(rr_pl)\n        doc2.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name2.upper() + \".docx\")\n        replace_string(doc2, provider_address_PL, provider_address2)\n        replace_string(doc2, provider_email_PL, provider_email2)\n        replace_string(doc2, provider_name_PL, provider_name2)\n        replace_string(doc2, user_name_pl, user_name)\n        replace_string(doc2, user_fax_pl, user_fax)\n        replace_string(doc2, user_email_pl, user_email)\n        replace_string(doc2, current_balance_pl, current_balance2)\n        replace_string(doc2, requested_balance_pl, requested_balance2)\n\n    replace_string(doc2, client_name_PL, client_name)\n    replace_string(doc2, doa_PL, doa)\n    replace_string(doc2, today_date_PL, today_date)\n    replace_string(doc2, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc2.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name2.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc2.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name2.upper() + \".docx\")\n\n\nif provider_int > 2:\n\n    if doc_type == \"LOP\":\n        doc3 = Document(lop_pl)\n        doc3.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name3.upper() + \".docx\")\n        replace_string(doc3, lop_amount_PL, lop_amount)\n        replace_string(doc3, provider_address_PL, provider_address3)\n        replace_string(doc3, provider_email_PL, provider_email3)\n        replace_string(doc3, provider_name_PL, provider_name3)\n\n    if doc_type == \"RR\":\n        doc3 = Document(rr_pl)\n        doc3.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name3.upper() + \".docx\")\n        replace_string(doc3, provider_address_PL, provider_address3)\n        replace_string(doc3, provider_email_PL, provider_email3)\n        replace_string(doc3, provider_name_PL, provider_name3)\n        replace_string(doc3, user_name_pl, user_name)\n        replace_string(doc3, user_fax_pl, user_fax)\n        replace_string(doc3, user_email_pl, user_email)\n        replace_string(doc3, current_balance_pl, current_balance3)\n        replace_string(doc3, requested_balance_pl, requested_balance3)\n\n    replace_string(doc3, client_name_PL, client_name)\n    replace_string(doc3, doa_PL, doa)\n    replace_string(doc3, today_date_PL, today_date)\n    replace_string(doc3, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc3.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name3.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc3.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name3.upper() + \".docx\")\n\nif provider_int > 3:\n\n    if doc_type == \"LOP\":\n        doc4 = Document(lop_pl)\n        doc4.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name4.upper() + \".docx\")\n        replace_string(doc4, lop_amount_PL, lop_amount)\n        replace_string(doc4, provider_address_PL, provider_address4)\n        replace_string(doc4, provider_email_PL, provider_email4)\n        replace_string(doc4, provider_name_PL, provider_name4)\n\n    if doc_type == \"RR\":\n        doc4 = Document(rr_pl)\n        doc4.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name4.upper() + \".docx\")\n        replace_string(doc4, provider_address_PL, provider_address4)\n        replace_string(doc4, provider_email_PL, provider_email4)\n        replace_string(doc4, provider_name_PL, provider_name4)\n        replace_string(doc4, user_name_pl, user_name)\n        replace_string(doc4, user_fax_pl, user_fax)\n        replace_string(doc4, user_email_pl, user_email)\n        replace_string(doc4, current_balance_pl, current_balance4)\n        replace_string(doc4, requested_balance_pl, requested_balance4)\n\n    replace_string(doc4, client_name_PL, client_name)\n    replace_string(doc4, doa_PL, doa)\n    replace_string(doc4, today_date_PL, today_date)\n    replace_string(doc4, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc4.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name4.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc4.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name4.upper() + \".docx\")\n\n\nif provider_int > 4:\n\n    if doc_type == \"LOP\":\n        doc5 = Document(lop_pl)\n        doc5.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name5.upper() + \".docx\")\n        replace_string(doc5, lop_amount_PL, lop_amount)\n        replace_string(doc5, provider_address_PL, provider_address5)\n        replace_string(doc5, provider_email_PL, provider_email5)\n        replace_string(doc5, provider_name_PL, provider_name5)\n\n    if doc_type == \"RR\":\n        doc5 = Document(rr_pl)\n        doc5.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name5.upper() + \".docx\")\n        replace_string(doc5, provider_address_PL, provider_address5)\n        replace_string(doc5, provider_email_PL, provider_email5)\n        replace_string(doc5, provider_name_PL, provider_name5)\n        replace_string(doc5, user_name_pl, user_name)\n        replace_string(doc5, user_fax_pl, user_fax)\n        replace_string(doc5, user_email_pl, user_email)\n        replace_string(doc5, current_balance_pl, current_balance5)\n        replace_string(doc5, requested_balance_pl, requested_balance5)\n\n    replace_string(doc5, client_name_PL, client_name)\n    replace_string(doc5, doa_PL, doa)\n    replace_string(doc5, today_date_PL, today_date)\n    replace_string(doc5, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc5.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name5.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc5.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name5.upper() + \".docx\")\n\n\nif provider_int > 5:\n\n    if doc_type == \"LOP\":\n        doc6 = Document(lop_pl)\n        doc6.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name6.upper() + \".docx\")\n        replace_string(doc6, lop_amount_PL, lop_amount)\n        replace_string(doc6, provider_address_PL, provider_address6)\n        replace_string(doc6, provider_email_PL, provider_email6)\n        replace_string(doc6, provider_name_PL, provider_name6)\n\n    if doc_type == \"RR\":\n        doc6 = Document(rr_pl)\n        doc6.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name6.upper() + \".docx\")\n        replace_string(doc6, provider_address_PL, provider_address6)\n        replace_string(doc6, provider_email_PL, provider_email6)\n        replace_string(doc6, provider_name_PL, provider_name6)\n        replace_string(doc6, user_name_pl, user_name)\n        replace_string(doc6, user_fax_pl, user_fax)\n        replace_string(doc6, user_email_pl, user_email)\n        replace_string(doc6, current_balance_pl, current_balance6)\n        replace_string(doc6, requested_balance_pl, requested_balance6)\n\n    replace_string(doc6, client_name_PL, client_name)\n    replace_string(doc6, doa_PL, doa)\n    replace_string(doc6, today_date_PL, today_date)\n    replace_string(doc6, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc6.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name6.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc6.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name6.upper() + \".docx\")\n\n\nif provider_int > 6:\n\n    if doc_type == \"LOP\":\n        doc7 = Document(lop_pl)\n        doc7.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name7.upper() + \".docx\")\n        replace_string(doc7, lop_amount_PL, lop_amount)\n        replace_string(doc7, provider_address_PL, provider_address7)\n        replace_string(doc7, provider_email_PL, provider_email7)\n        replace_string(doc7, provider_name_PL, provider_name7)\n\n    if doc_type == \"RR\":\n        doc7 = Document(rr_pl)\n        doc7.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name7.upper() + \".docx\")\n        replace_string(doc7, provider_address_PL, provider_address7)\n        replace_string(doc7, provider_email_PL, provider_email7)\n        replace_string(doc7, provider_name_PL, provider_name7)\n        replace_string(doc7, user_name_pl, user_name)\n        replace_string(doc7, user_fax_pl, user_fax)\n        replace_string(doc7, user_email_pl, user_email)\n        replace_string(doc7, current_balance_pl, current_balance7)\n        replace_string(doc7, requested_balance_pl, requested_balance7)\n\n    replace_string(doc7, client_name_PL, client_name)\n    replace_string(doc7, doa_PL, doa)\n    replace_string(doc7, today_date_PL, today_date)\n    replace_string(doc7, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc7.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name7.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc7.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name7.upper() + \".docx\")\n\n\nif provider_int > 7:\n\n    if doc_type == \"LOP\":\n        doc8 = Document(lop_pl)\n        doc8.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name8.upper() + \".docx\")\n        replace_string(doc8, lop_amount_PL, lop_amount)\n        replace_string(doc8, provider_address_PL, provider_address8)\n        replace_string(doc8, provider_email_PL, provider_email8)\n        replace_string(doc8, provider_name_PL, provider_name8)\n\n    if doc_type == \"RR\":\n        doc8 = Document(rr_pl)\n        doc8.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name8.upper() + \".docx\")\n        replace_string(doc8, provider_address_PL, provider_address8)\n        replace_string(doc8, provider_email_PL, provider_email8)\n        replace_string(doc8, provider_name_PL, provider_name8)\n        replace_string(doc8, user_name_pl, user_name)\n        replace_string(doc8, user_fax_pl, user_fax)\n        replace_string(doc8, user_email_pl, user_email)\n        replace_string(doc8, current_balance_pl, current_balance8)\n        replace_string(doc8, requested_balance_pl, requested_balance8)\n\n    replace_string(doc8, client_name_PL, client_name)\n    replace_string(doc8, doa_PL, doa)\n    replace_string(doc8, today_date_PL, today_date)\n    replace_string(doc8, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc8.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name8.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc8.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name8.upper() + \".docx\")\n\n\nif provider_int > 8:\n\n    if doc_type == \"LOP\":\n        doc9 = Document(lop_pl)\n        doc9.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name9.upper() + \".docx\")\n        replace_string(doc9, lop_amount_PL, lop_amount)\n        replace_string(doc9, provider_address_PL, provider_address9)\n        replace_string(doc9, provider_email_PL, provider_email9)\n        replace_string(doc9, provider_name_PL, provider_name9)\n\n    if doc_type == \"RR\":\n        doc9 = Document(rr_pl)\n        doc9.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name9.upper() + \".docx\")\n        replace_string(doc9, provider_address_PL, provider_address9)\n        replace_string(doc9, provider_email_PL, provider_email9)\n        replace_string(doc9, provider_name_PL, provider_name9)\n        replace_string(doc9, user_name_pl, user_name)\n        replace_string(doc9, user_fax_pl, user_fax)\n        replace_string(doc9, user_email_pl, user_email)\n        replace_string(doc9, current_balance_pl, current_balance9)\n        replace_string(doc9, requested_balance_pl, requested_balance9)\n\n    replace_string(doc9, client_name_PL, client_name)\n    replace_string(doc9, doa_PL, doa)\n    replace_string(doc9, today_date_PL, today_date)\n    replace_string(doc9, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc9.save('/DocuLegal/LOPs/' + client_name.upper() + ' LOP ' + provider_name9.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc9.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name9.upper() + \".docx\")\n\n\nif provider_int > 9:\n\n    if doc_type == \"LOP\":\n        doc10 = Document(lop_pl)\n        doc10.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name10.upper() + \".docx\")\n        replace_string(doc10, lop_amount_PL, lop_amount)\n        replace_string(doc10, provider_address_PL, provider_address10)\n        replace_string(doc10, provider_email_PL, provider_email10)\n        replace_string(doc10, provider_name_PL, provider_name10)\n\n    if doc_type == \"RR\":\n        doc10 = Document(rr_pl)\n        doc10.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name10.upper() + \".docx\")\n        replace_string(doc10, provider_address_PL, provider_address10)\n        replace_string(doc10, provider_email_PL, provider_email10)\n        replace_string(doc10, provider_name_PL, provider_name10)\n        replace_string(doc10, user_name_pl, user_name)\n        replace_string(doc10, user_fax_pl, user_fax)\n        replace_string(doc10, user_email_pl, user_email)\n        replace_string(doc10, current_balance_pl, current_balance10)\n        replace_string(doc10, requested_balance_pl, requested_balance10)\n\n    replace_string(doc10, client_name_PL, client_name)\n    replace_string(doc10, doa_PL, doa)\n    replace_string(doc10, today_date_PL, today_date)\n    replace_string(doc10, attorney_name_PL, attorney_name)\n\n\n    if doc_type == \"LOP\":\n        doc10.save('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name10.upper() + \".docx\")\n    if doc_type == \"RR\":\n        doc10.save('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name10.upper() + \".docx\")\n\n\n\n#notify user of completion\nif provider_int == 1:\n    print('Document sent to \"DocuLegal\" folder')\nelse:\n    print('Documents sent to \"DocuLegal\" folder')\n\nMakePDF = input(\"Would you also like PDF versions of these files? (Y/N): \")\n\nif MakePDF == \"Y\":\n    print(\"Please Wait...\")\n    if doc_type == \"LOR\":\n        DocxtoPDF('/DocuLegal/LORs/Word/' + client_name.upper() + ' LOR ' + def_insurance.upper() + \".docx\",\n                  '/DocuLegal/LORs/PDF/' + client_name.upper() + ' LOR ' + def_insurance.upper() + \".pdf\")\n\n\n    if doc_type == \"LOP\":\n\n        DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name.upper() + \".docx\",\n                  '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name.upper() + \".pdf\")\n\n        if provider_int > 1:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name2.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name2.upper() + \".pdf\")\n\n        if provider_int > 2:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name3.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name3.upper() + \".pdf\")\n\n        if provider_int > 3:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name4.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name4.upper() + \".pdf\")\n\n        if provider_int > 4:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name5.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name5.upper() + \".pdf\")\n\n        if provider_int > 5:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name6.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name6.upper() + \".pdf\")\n\n        if provider_int > 6:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name7.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name7.upper() + \".pdf\")\n\n        if provider_int > 7:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name8.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name8.upper() + \".pdf\")\n\n        if provider_int > 8:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name9.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name9.upper() + \".pdf\")\n\n        if provider_int > 9:\n            DocxtoPDF('/DocuLegal/LOPs/Word/' + client_name.upper() + ' LOP ' + provider_name10.upper() + \".docx\",\n                      '/DocuLegal/LOPs/PDF/' + client_name.upper() + ' LOP ' + provider_name10.upper() + \".pdf\")\n    if doc_type == \"RR\":\n\n        DocxtoPDF('/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name.upper() + \".docx\",\n                  '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name.upper() + \".pdf\")\n\n        if provider_int > 1:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name2.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name2.upper() + \".pdf\")\n\n        if provider_int > 2:\n                DocxtoPDF(\n                    '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name3.upper() + \".docx\",\n                    '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name3.upper() + \".pdf\")\n\n        if provider_int > 3:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name4.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name4.upper() + \".pdf\")\n\n        if provider_int > 4:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name5.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name5.upper() + \".pdf\")\n\n        if provider_int > 5:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name6.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name6.upper() + \".pdf\")\n\n        if provider_int > 6:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name7.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name7.upper() + \".pdf\")\n\n        if provider_int > 7:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name8.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name8.upper() + \".pdf\")\n\n        if provider_int > 8:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name9.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name9.upper() + \".pdf\")\n\n        if provider_int > 9:\n            DocxtoPDF(\n                '/DocuLegal/Reduction Requests/Word/' + client_name.upper() + ' Reduction Request ' + provider_name10.upper() + \".docx\",\n                '/DocuLegal/Reduction Requests/PDF/' + client_name.upper() + ' Reduction Request ' + provider_name10.upper() + \".pdf\")\n\n    if provider_int == 1:\n        print('Document sent to \"DocuLegal\" folder')\n    else:\n        print('Documents sent to \"DocuLegal\" folder')\n\ntime.sleep(5)","sub_path":"DocuLegal.py","file_name":"DocuLegal.py","file_ext":"py","file_size_in_byte":31937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"153094908","text":"import math\nimport requests\nfrom requests import urllib3\nfrom lxml import etree\nimport json\nfrom multiprocessing import Pool\n\n\ndef get_proxy():\n    return requests.get(\"http://127.0.0.1:5010/get/\").content\n\n\ndef delete_proxy(proxy):\n    requests.get(\"http://127.0.0.1:5010/delete/?proxy={}\".format(proxy))\n\n\ndef get_one_page(url):\n    retry_count = 5\n    proxy = get_proxy()\n    kv = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) '\n                        'Chrome/49.0.2623.221 Safari/537.36 SE 2.X MetaSr 1.0'}\n    while retry_count > 0:\n        try:\n            response = requests.get(url, headers=kv, proxies={\"http\": \"http://{}\".format(proxy)}, verify=False)\n            if response.status_code == 200:\n                return response\n        except Exception:\n            retry_count -= 1\n    delete_proxy(proxy)\n    return None\n\n\ndef scrape(url):\n    print(\"start collect URL\")\n    url2 = 'https://www.nhs.uk' + url\n    content = get_one_page(url2)\n    if content is not None:\n        html = content.text\n        htmlURL = content.url\n        html2 = etree.HTML(html)\n        stringTotal = html2.xpath('//*[@id=\"main-content\"]/div/form[2]/div[1]/ul/li[1]')\n        tempString = stringTotal[0].text.split()\n        totalResult = tempString[-2]\n        pageNumber = int(math.ceil(int(totalResult) / 10))\n        lastpage = int(totalResult) % 10\n        fr = open('result.json')\n        dict = json.load(fr)\n        fr.close()\n        for a in range(1, pageNumber + 1):\n            url3 = htmlURL + '&ResultsOnPageValue=10&isNational=0&totalItems=' + str(\n                totalResult) + '&currentPage=' + str(a)\n            content1 = get_one_page(url3)\n            if content1 is not None:\n                html3 = content1.text\n                html4 = etree.HTML(html3)\n                b = 22\n                if a == pageNumber:\n                    b = 2 + (2 * lastpage)\n                for ii in range(4, b + 2, 2):\n                    name1 = html4.xpath('//*[@id=\"main-content\"]/div/form[2]/table/tbody/tr[' + str(ii) + ']/th/a')\n                    result = html4.xpath(\n                        '//*[@id=\"main-content\"]/div/form[2]/table/tbody/tr[' + str(ii) + ']/th/a/@href')\n                    key = name1[0].text\n                    value = result[0]\n                    dict[key] = value\n            else:\n                with open('error.txt', 'a')as file:\n                    aa = url2 + ':' + url3\n                    file.write(aa)\n                    continue\n        print(\"collect finished, start to write\")\n        jsObj = json.dumps(dict, indent=2)\n        with open('result.json', 'w')as file:\n            file.write(jsObj)\n            file.close()\n    else:\n        with open('error1.txt', 'a')as file1:\n            aaa = url2\n            file1.write(aaa)\n            file1.close()\n\n\nif __name__ == '__main__':\n    file = open('url.json', 'r', encoding='utf-8')\n    s = json.load(file)\n    file.close()\n    url = s.values()\n    pool = Pool()   \n    pool.map(scrape, url)\n","sub_path":"code/useIPconfig.py","file_name":"useIPconfig.py","file_ext":"py","file_size_in_byte":3042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"391668598","text":"#!/usr/bin/env python\nimport rospy\nimport numpy as np\nfrom sensor_msgs.msg import Range\n\ncurrent_distances = []\ndef callback(data):\n    rospy.loginfo(rospy.get_caller_id() + \"I heard %s!\", data)\n    current_distances.append(data.range)\n    print (current_dstances)\n\ndef listener():\n\n    rospy.init_node('home_subscriber', anonymous=True)\n\n    rospy.Subscriber(\"chowder\", Range, callback)\n    # spin() simply keeps python from exiting until this node is stopped\n    rospy.spin()\n\nif __name__ == '__main__':\n    listener()\n","sub_path":"nodes/home_subscriber.py","file_name":"home_subscriber.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"244971301","text":"#coding=utf-8\nimport sys\nimport os\nfrom   os.path import abspath, dirname\nsys.path.append(abspath(dirname(__file__)))\nimport tkinter\nimport tkinter.filedialog\nfrom   tkinter import *\nimport Fun\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\nElementBGArray={}  \nElementBGArray_Resize={} \nElementBGArray_IM={} \ndef Button_3_onCommand(uiName,widgetName):\n    filePath = tkinter.filedialog.askopenfilename(initialdir=os.path.abspath('.'), title='选择文件')\n    Fun.SetText(uiName, \"Entry_4\", filePath)\n    #input = PdfFileReader(open(filePath, \"rb\"))\ndef Button_5_onCommand(uiName,widgetName):\n    filePath = tkinter.filedialog.askopenfilename(initialdir=os.path.abspath('.'), title='选择文件')\n    Fun.SetText(uiName, \"Entry_6\", filePath)\ndef Button_7_onCommand(uiName,widgetName):\n    file1 = Fun.GetText(uiName, \"Entry_4\")\n    file2 = Fun.GetText(uiName, \"Entry_6\")\n    input1 = PdfFileReader(open(file1, \"rb\"))\n    input2 = PdfFileReader(open(file2, \"rb\"))\n    pageCount1 = input1.getNumPages()\n    outPut1 = PdfFileWriter()\n    dirName = os.path.dirname(file1)\n    for iPage in range(input1.getNumPages()):\n        outPut1.addPage(input1.getPage(iPage))\n    for iPage in range(input2.getNumPages()):\n        outPut1.addPage(input2.getPage(iPage))\n    newFileName = os.path.join(dirName,\"merge1.pdf\")\n    outputStream = open(newFileName, \"wb\")\n    outPut1.write(outputStream)\n    outputStream.close()\n    Fun.MessageBox(\"merge finised,new file name is=merge1.pdf\")\ndef Button_10_onCommand(uiName,widgetName):\n    dirPath = tkinter.filedialog.askdirectory(initialdir=os.path.abspath('.'), title='打开目录查找')\n    Fun.SetText(uiName, \"Entry_11\", dirPath)\ndef Button_12_onCommand(uiName,widgetName):\n     file_dir = Fun.GetText(uiName, \"Entry_11\")\n     outfile = \"out.pdf\"\n     MergePDF(file_dir, outfile)\ndef getFileName(filedir):\n    file_list = [os.path.join(root, filespath) \\\n                 for root, dirs, files in os.walk(filedir) \\\n                 for filespath in files \\\n                 if str(filespath).endswith('pdf')\n                 ]\n    return file_list if file_list else []\ndef MergePDF(filepath, outfile):\n    output = PdfFileWriter()\n    outputPages = 0\n    pdf_fileName = getFileName(filepath)\n    if pdf_fileName:\n        for pdf_file in pdf_fileName:\n            print(\"路径：%s\"%pdf_file)\n            # 读取源PDF文件\n            input = PdfFileReader(open(pdf_file, \"rb\"))\n            # 获得源PDF文件中页面总数\n            pageCount = input.getNumPages()\n            outputPages += pageCount\n            print(\"页数：%d\"%pageCount)\n            Fun.SetTKAttrib(\"Project4\", \"Progress_3\", \"value\",pageCount)\n            # 分别将page添加到输出output中\n            for iPage in range(pageCount):\n                output.addPage(input.getPage(iPage))\n        print(\"合并后的总页数:%d.\"%outputPages)\n        # 写入到目标PDF文件\n        outputStream = open(os.path.join(filepath, outfile), \"wb\")\n        output.write(outputStream)\n        outputStream.close()\n        Fun.MessageBox(\"PDF文件合并完成！合并后的文件名为 out.pdf\")\n        print(\"PDF文件合并完成！\")\n    else:\n        print(\"没有可以合并的PDF文件！\")\n","sub_path":"Examples/PDFTool/Merge_cmd.py","file_name":"Merge_cmd.py","file_ext":"py","file_size_in_byte":3223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"437311367","text":"from bs4 import BeautifulSoup\nfrom components.logger import Logger\nimport urllib.request\nfrom time import time\n\n\n\"\"\"\nClass which is responsible for performing operations connected with site status management.\n\"\"\"\nclass ScrapOperator(Logger):\n    START = None\n    END = None\n    URL = None\n\n    def __init__(self, sites):\n        super().__init__()\n        self.log_setup()\n        self.site_status = {web: \"UNKNOWN\" for web in sites}\n\n    # Returns loaded site HTML\n    @staticmethod\n    def load_site(url):\n        ScrapOperator.URL = url\n        ScrapOperator.START = time()\n        data = urllib.request.urlopen(url).read()\n        ScrapOperator.END = time()\n        return data\n\n    # Returns raw data\n    @staticmethod\n    def parse_site(site):\n        ScrapOperator.START = time()\n        data = BeautifulSoup(site, 'html.parser')\n        ScrapOperator.END = time()\n        return data\n\n    # Returns list, if item wasn't found the list would be empty\n    @staticmethod\n    def find_element(tuple_data, info):\n        element, el_type, key = tuple_data\n\n        # Find by text\n        if element == \"text\":\n            return info.body.find_all(text=key)\n        # Find by other parameters e.g. (p, class, text)\n        else:\n            return info.body.find_all(element, {el_type: key})\n\n    # Checks if list was empty (it means element wasn't found) and creates proper info in log\n    def check_condition(self, element):\n        if not element:\n            result = \"Element NOT found!\"\n            status = \"ERROR\"\n        else:\n            result = \"Element found\"\n            status = \"OK\"\n\n        self.status_generator(status)\n        message = self.build_log_message(result)\n        self.log_operator.info(message)\n\n    # Maps site url with its status\n    def status_generator(self, status):\n        self.site_status[ScrapOperator.URL] = status\n\n    @staticmethod\n    # Returns page load time\n    def calculate_time(start, end):\n        return end - start\n\n    # Creates message inserted to log file\n    def build_log_message(self, result, error=False):\n        # Error means that there's a problem with web site so loading time is negligible\n        if error:\n            calculated_time = \"Site error\"\n        # In other case calculate time\n        else:\n            calculated_time = self.calculate_time(ScrapOperator.START, ScrapOperator.END)\n\n        # {site url} {result of checking operation} {time}\n        return \"{} {} {}\".format(\n            ScrapOperator.URL,\n            result,\n            calculated_time)\n","sub_path":"components/scrap_handler.py","file_name":"scrap_handler.py","file_ext":"py","file_size_in_byte":2536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"175614864","text":"import sys\nsys.stdin = open('14425.txt', 'r')\n\nN, M = map(int, input().split())\n\nS = set()\nanswer = 0\n\nfor _ in range(N):\n    S.add(input())\n\nfor _ in range(M):\n    word = input()\n    if word in S:\n        answer += 1\n\nprint(answer)","sub_path":"BOJ/trie/14425_문자열집합_200830.py","file_name":"14425_문자열집합_200830.py","file_ext":"py","file_size_in_byte":232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"368280410","text":"\"\"\"Support for the Airly service.\"\"\"\nfrom homeassistant.components.sensor import (\n    ATTR_STATE_CLASS,\n    STATE_CLASS_MEASUREMENT,\n    SensorEntity,\n)\nfrom homeassistant.const import (\n    ATTR_ATTRIBUTION,\n    ATTR_DEVICE_CLASS,\n    CONCENTRATION_MICROGRAMS_PER_CUBIC_METER,\n    CONF_NAME,\n    DEVICE_CLASS_HUMIDITY,\n    DEVICE_CLASS_PRESSURE,\n    DEVICE_CLASS_TEMPERATURE,\n    PERCENTAGE,\n    PRESSURE_HPA,\n    TEMP_CELSIUS,\n)\nfrom homeassistant.helpers.update_coordinator import CoordinatorEntity\n\nfrom .const import (\n    ATTR_CAQI,\n    ATTR_CAQI_ADVICE,\n    ATTR_CAQI_DESCRIPTION,\n    ATTR_CAQI_LEVEL,\n    DEFAULT_NAME,\n    DOMAIN,\n    MANUFACTURER,\n)\n\nATTR_ICON = \"icon\"\nATTR_LABEL = \"label\"\nATTR_LIMIT = \"limit\"\nATTR_PERCENT = \"percent\"\nATTR_PM1 = \"PM1\"\nATTR_PM10 = \"PM10\"\nATTR_PM10_LIMIT = \"PM10_LIMIT\"\nATTR_PM10_PERCENT = \"PM10_PERCENT\"\nATTR_PM25 = \"PM25\"\nATTR_PM25_LIMIT = \"PM25_LIMIT\"\nATTR_PM25_PERCENT = \"PM25_PERCENT\"\nATTR_HUMIDITY = \"HUMIDITY\"\nATTR_PRESSURE = \"PRESSURE\"\nATTR_TEMPERATURE = \"TEMPERATURE\"\nATTR_UNIT = \"unit\"\n\nATTRIBUTION = {\"en\": \"Data provided by Airly\", \"pl\": \"Dane dostarczone przez Airly\"}\n\nSENSOR_TYPES = {\n    ATTR_CAQI: {\n        ATTR_DEVICE_CLASS: None,\n        ATTR_ICON: None,\n        ATTR_LABEL: ATTR_CAQI,\n        ATTR_UNIT: None,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n    ATTR_CAQI_DESCRIPTION: {\n        ATTR_DEVICE_CLASS: None,\n        ATTR_ICON: \"mdi:card-text-outline\",\n        ATTR_LABEL: ATTR_CAQI_DESCRIPTION.capitalize(),\n        ATTR_UNIT: None,\n        ATTR_STATE_CLASS: None,\n    },\n    ATTR_PM1: {\n        ATTR_DEVICE_CLASS: None,\n        ATTR_ICON: \"mdi:blur\",\n        ATTR_LABEL: ATTR_PM1,\n        ATTR_UNIT: CONCENTRATION_MICROGRAMS_PER_CUBIC_METER,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n    ATTR_PM10: {\n        ATTR_DEVICE_CLASS: None,\n        ATTR_ICON: \"mdi:blur\",\n        ATTR_LABEL: ATTR_PM10,\n        ATTR_UNIT: CONCENTRATION_MICROGRAMS_PER_CUBIC_METER,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n    ATTR_PM25: {\n        ATTR_DEVICE_CLASS: None,\n        ATTR_ICON: \"mdi:blur\",\n        ATTR_LABEL: \"PM2.5\",\n        ATTR_UNIT: CONCENTRATION_MICROGRAMS_PER_CUBIC_METER,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n    ATTR_HUMIDITY: {\n        ATTR_DEVICE_CLASS: DEVICE_CLASS_HUMIDITY,\n        ATTR_ICON: None,\n        ATTR_LABEL: ATTR_HUMIDITY.capitalize(),\n        ATTR_UNIT: PERCENTAGE,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n    ATTR_PRESSURE: {\n        ATTR_DEVICE_CLASS: DEVICE_CLASS_PRESSURE,\n        ATTR_ICON: None,\n        ATTR_LABEL: ATTR_PRESSURE.capitalize(),\n        ATTR_UNIT: PRESSURE_HPA,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n    ATTR_TEMPERATURE: {\n        ATTR_DEVICE_CLASS: DEVICE_CLASS_TEMPERATURE,\n        ATTR_ICON: None,\n        ATTR_LABEL: ATTR_TEMPERATURE.capitalize(),\n        ATTR_UNIT: TEMP_CELSIUS,\n        ATTR_STATE_CLASS: STATE_CLASS_MEASUREMENT,\n    },\n}\n\n\nasync def async_setup_entry(hass, config_entry, async_add_entities):\n    \"\"\"Add a Airly entities from a config_entry.\"\"\"\n    name = config_entry.data[CONF_NAME]\n\n    coordinator = hass.data[DOMAIN][config_entry.entry_id]\n\n    sensors = []\n    for sensor in SENSOR_TYPES:\n        # When we use the nearest method, we are not sure which sensors are available\n        if coordinator.data.get(sensor):\n            sensors.append(AirlySensor(coordinator, name, sensor))\n\n    async_add_entities(sensors)\n\n\nclass AirlySensor(CoordinatorEntity, SensorEntity):\n    \"\"\"Define an Airly sensor.\"\"\"\n\n    def __init__(self, coordinator, name, kind):\n        \"\"\"Initialize.\"\"\"\n        super().__init__(coordinator)\n        self._name = name\n        self.kind = kind\n        self._description = SENSOR_TYPES[self.kind]\n        self._device_class = None\n        self._state = None\n        self._icon = None\n        self._unit_of_measurement = None\n        self._attrs = {ATTR_ATTRIBUTION: ATTRIBUTION[self.coordinator.language]}\n        self._attr_state_class = self._description[ATTR_STATE_CLASS]\n\n    @property\n    def name(self):\n        \"\"\"Return the name.\"\"\"\n        return f\"{self._name} {self._description[ATTR_LABEL]}\"\n\n    @property\n    def device_info(self):\n        \"\"\"Return the device info.\"\"\"\n        return {\n            \"identifiers\": {\n                (DOMAIN, f\"{self.coordinator.latitude}-{self.coordinator.longitude}\")\n            },\n            \"name\": DEFAULT_NAME,\n            \"manufacturer\": MANUFACTURER,\n            \"entry_type\": \"service\",\n        }\n\n    @property\n    def state(self):\n        \"\"\"Return the state.\"\"\"\n        self._state = self.coordinator.data[self.kind]\n        if self.kind in [ATTR_PM1, ATTR_PM25, ATTR_PM10, ATTR_PRESSURE, ATTR_CAQI]:\n            self._state = round(self._state)\n        if self.kind in [ATTR_TEMPERATURE, ATTR_HUMIDITY]:\n            self._state = round(self._state, 1)\n        return self._state\n\n    @property\n    def extra_state_attributes(self):\n        \"\"\"Return the state attributes.\"\"\"\n        if self.kind == ATTR_CAQI_DESCRIPTION:\n            self._attrs[ATTR_CAQI_ADVICE] = self.coordinator.data[ATTR_CAQI_ADVICE]\n        if self.kind == ATTR_CAQI:\n            self._attrs[ATTR_CAQI_LEVEL] = self.coordinator.data[ATTR_CAQI_LEVEL]\n        if self.kind == ATTR_PM25:\n            self._attrs[ATTR_LIMIT] = self.coordinator.data[ATTR_PM25_LIMIT]\n            self._attrs[ATTR_PERCENT] = round(self.coordinator.data[ATTR_PM25_PERCENT])\n        if self.kind == ATTR_PM10:\n            self._attrs[ATTR_LIMIT] = self.coordinator.data[ATTR_PM10_LIMIT]\n            self._attrs[ATTR_PERCENT] = round(self.coordinator.data[ATTR_PM10_PERCENT])\n        return self._attrs\n\n    @property\n    def icon(self):\n        \"\"\"Return the icon.\"\"\"\n        if self.kind == ATTR_CAQI:\n            if isinstance(self._state, int):\n                if self._state <= 25:\n                    self._icon = \"mdi:emoticon-excited\"\n                elif self._state <= 50:\n                    self._icon = \"mdi:emoticon-happy\"\n                elif self._state <= 75:\n                    self._icon = \"mdi:emoticon-neutral\"\n                elif self._state <= 100:\n                    self._icon = \"mdi:emoticon-sad\"\n                elif self._state > 100:\n                    self._icon = \"mdi:emoticon-dead\"\n        else:\n            self._icon = self._description[ATTR_ICON]\n        return self._icon\n\n    @property\n    def device_class(self):\n        \"\"\"Return the device_class.\"\"\"\n        return self._description[ATTR_DEVICE_CLASS]\n\n    @property\n    def unique_id(self):\n        \"\"\"Return a unique_id for this entity.\"\"\"\n        return f\"{self.coordinator.latitude}-{self.coordinator.longitude}-{self.kind.lower()}\"\n\n    @property\n    def unit_of_measurement(self):\n        \"\"\"Return the unit the value is expressed in.\"\"\"\n        return self._description[ATTR_UNIT]\n","sub_path":"custom_components/airly/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":6861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"429863122","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.io.idl import readsav\nfrom astropy.io import fits\nfrom astropy.convolution import convolve, Box1DKernel\nimport astropy.constants as const\n\nplt.figure()\n\n#COSG130M\ninstrument = 'COS_G130M'\ndata = readsav('../COS/TRAPPIST1_G130M_Mm1_NOSCL_10dec2018.sav')\nmask = (data['Wave'] <1207)|(data['Wave'] >1225)&(data['Wave'] <1301)|(data['Wave'] >1307.)&(data['Wave'] <1355)|(data['Wave'] >1356)\nf = convolve(data['flux'][mask],Box1DKernel(5))\ne = convolve(data['err'][mask],Box1DKernel(5))/(5**0.5)\n#plt.step(data['wave'][mask], data['flux'][mask])\nplt.step(data['wave'][mask],f, where='mid', c='C0')\nplt.step(data['wave'][mask],e, where='mid', c='C1')\n\nend_130 = data['wave'][-1]\n\n\n#COSG160M\ninstrument = 'COS_G160M'\ndata = readsav('../COS/TRAPPIST1_G160M_3orb_Mm1_NOSCL_09dec2018.sav')\nmask = (data['wave'] > end_130)\nf = convolve(data['flux'][mask],Box1DKernel(5))\ne = convolve(data['err'][mask],Box1DKernel(5))/(5**0.5)\n#plt.step(data['wave'][mask], data['flux'][mask])\nplt.step(data['wave'][mask],f, where='mid', c='C0')\nplt.step(data['wave'][mask],e, where='mid', c='C1')\n\n#plt.step(data['wave'][mask], data['err'][mask])\nend_160 = data['wave'][mask][-1]\n\ninstrument = 'COS_G230L'\nspecpath = '../../../trappist-1/hst/data/ldlm42010_x1dsum.fits'\ncnw = np.array([], dtype=float)\ncnf = np.array([], dtype=float)\ncne = np.array([], dtype=float)\ncndq = np.array([], dtype=int)\nndata = fits.getdata(specpath,1)[0:2]\nfor dt in ndata:\n    cnw= np.concatenate((cnw, dt['WAVELENGTH']))\n    cnf = np.concatenate((cnf, dt['FLUX']))\n    cne = np.concatenate((cne, dt['ERROR']))\n    cndq = np.concatenate((cndq, dt['DQ']))\nmask = (cnw > end_160)\ncnw, cnf, cne, cndq = cnw[mask], cnf[mask], cne[mask], cndq[mask] \nplt.step(cnw, cnf, where='mid', c='C0')\n\nplt.axhline(0, ls='--', c='k')\n\nlines = [1561, 1657.00751,1657.37863,1657.90661,1486,1666.153,1407.382,1670.787, 1533.45  ,1393.755, 1402.770]\nline_names = ['Ci', 'Ci','Ci','Ci','Oiii','Oiv','Alii','Siii', 'Siiv','Siiv']\n\ndv = -56300 #Bourrier+17a \nc = const.c.value\ndshift = (1.0+(dv/c))\n[plt.axvline(line*dshift, ls='--',c='r') for line in lines]\n#plt.ylim(5e-16,-3e-16)\n\nplt.show()\n\n           ","sub_path":"trappist-1/combined/t1_justcos.py","file_name":"t1_justcos.py","file_ext":"py","file_size_in_byte":2197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"635966166","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\n\n__author__ = 'User'\n\nimport unittest\nimport datetime\n\nfrom MainScript import FileHandler\n\ndef get_day_of_the_week():\n    DayL = ['Mon','Tues','Wednes','Thurs','Fri','Satur','Sun']\n    date = DayL[datetime.date(datetime.datetime.now().year,datetime.datetime.now().month,datetime.datetime.now().day).weekday()] + 'day'\n    return date\n\nclass MyTest(unittest.TestCase):\n    def setUp(self):\n        self.file2 = FileHandler.read_csv('file2.txt')\n        self.file3 = FileHandler.read_csv('file3.txt')\n\n    def tearDown(self):\n        pass\n\n    def get_row_index(self):\n        #Get today's day name like: Saturday\n        today_day = get_day_of_the_week()\n        #Now check for matching value in static list and return the index.\n        for i,val in enumerate(self.static_header):\n            if val == today_day:\n                return i\n\n    def get_current_time_in_seconds(self):\n        current_time_hour = datetime.datetime.now().hour\n        current_time_min = datetime.datetime.now().minute\n        s_float = str(current_time_hour)+'.'+str(current_time_min)\n        current_time = float(s_float)\n        return int(current_time*3600)\n\n    def test_f2_f3(self):\n        f2_row = [79200, 28800, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n        f3_row = [0, 0, 79200, 28800, 79200, 28800, 79200, 28800, 79200, 28800, 79200, 28800, 79200, 28800]\n\n        row_index = 4\n\n        f2_val = f2_row[row_index]\n        f3_val = f3_row[row_index]\n\n        current_time_milis = self.get_current_time_in_seconds()\n\n        if f2_val > current_time_milis or f3_val > current_time_milis:\n            prev_row_index = row_index\n            while True:\n                prev_row_index = prev_row_index - 2\n                if prev_row_index < 0:\n                    break\n                f2_val = f2_row[prev_row_index]\n                f3_val = f3_row[prev_row_index]\n                if f2_val > 0 or f3_val > 0:\n                    break\n\n        self.assertEqual(f2_val,0)\n        self.assertEqual(f3_val,79200)\n\nif __name__ == '__main__':\n    MyTest().main()\n","sub_path":"Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"558735313","text":"# Ep03 - DICIONÁRIOS E ARQUIVOS\r\n\r\ndef perguntar():\r\n    return input(\"<I> Para Inserir um usuário\\n\" +\r\n                 \"<P> Para Pesquisar um usuário\\n\" +\r\n                 \"<E> Para Excluir um usuário\\n\" +\r\n                 \"<L> Para Listar um usuário\\n\" +\r\n                 \"o que deseja realizar? \").upper()\r\n\r\ndef inserir(dicionario):\r\n    dicionario[input(\"Digite o login: \").upper()] = [input(\"Digite o nome: \").upper(),\r\n                                                     input(\"Digite a última data de acesso: \"),\r\n                                                     input(\"Qual a última estação acessada: \").upper()]\r\n    salvar(dicionario)\r\n\r\ndef salvar(dicionario):\r\n    with open(\"bd.txt\", \"a\") as arquivo:\r\n        for chave, valor in dicionario.items():\r\n            arquivo.write(chave + \":\" + str(valor))\r\n","sub_path":"Python/Python/Cap5/Ep03_funcoes.py","file_name":"Ep03_funcoes.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"27495998","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport sys, os, socket, threading, time, datetime, json\nfrom Tkinter import *\n\nBUFFER_SIZE = 1024\nrowCounter = 0\nroot = None\nclient = None\nsender = None\n\nclass ScrollBar(Frame):\n    def __init__(self, root):\n\n        Frame.__init__(self, root)\n        self.canvas = Canvas(root, borderwidth=0, background=\"#ffffff\")\n        self.frame = Frame(self.canvas, background=\"#ffffff\")\n        self.vsb = Scrollbar(root, orient=\"vertical\", command=self.canvas.yview)\n        self.canvas.configure(yscrollcommand=self.vsb.set)\n        self.vsb.pack(side=\"right\", fill=\"y\")\n        self.canvas.pack(side=\"left\", fill=\"both\", expand=True)\n        self.canvas.create_window((4,4), window=self.frame, anchor=\"nw\", tags=\"self.frame\")\n        self.frame.bind(\"<Configure>\", self.OnFrameConfigure)\n    def OnFrameConfigure(self, event):\n        '''Reset the scroll region to encompass the inner frame'''\n        self.canvas.configure(scrollregion=self.canvas.bbox(\"all\"))\n    def onMouseWheel(self, event):\n        self.canvas.yview_scroll(-1*(event.delta/120), \"units\")\n\nclass TCPsender(threading.Thread):\n    def __init__(self, client, root):\n        global rowCounter\n        threading.Thread.__init__(self)\n        self.root = root\n        self.client = client\n        self.v = StringVar()\n        self.entry = Entry(self.root, textvariable=self.v)\n        self.entry.grid(row=rowCounter, column=0)\n        #rowCounter += 1\n        self.button = Button(self.root, text=\"Send\", command=self.send)\n        self.button.grid(row=rowCounter, column=1, columnspan=2)\n        rowCounter += 1\n    def enterPressed(self, event):\n        self.send()\n    def send(self):\n        global rowCounter\n        if self.client.is_connected:\n            message = self.v.get()\n            if message == \"\":\n                return\n            self.client.send(message)\n            messageTime = datetime.datetime.now().strftime(\"%Y:%m:%d:%H:%M:%S.%f\")\n            label = Label(self.root, text=message, font=(\"Helvetica\", 12), fg=\"black\", anchor=W, justify=RIGHT, wraplength=100)\n            label.grid(row=rowCounter, column=1)\n            rowCounter += 1\n            label = Label(self.root, text=messageTime, font=(\"Helvetica\", 6), fg=\"gray\", anchor=W, justify=RIGHT, wraplength=100)\n            label.grid(row=rowCounter, column=1)\n            rowCounter += 1\n            self.v.set(\"\")\n    def run(self):\n        pass\n\nclass TCPclient(threading.Thread):\n    def __init__(self, ip, port, root):\n        threading.Thread.__init__(self)\n        self.root = root\n        self.ip = ip\n        self.port = port\n        self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n        self.is_connected = False\n        self.connect()\n    def connect(self):\n        if self.is_connected:\n            return\n        self.socket.connect((self.ip, self.port))\n        self.is_connected = True\n        self.start()\n    def receive(self, size):\n        if self.is_connected:\n            return self.socket.recv(size)\n        return \"\"\n    def send(self, message):\n        if self.is_connected:\n            self.socket.send(message.encode(\"utf-8\"))\n    def close(self):\n        self.socket.close()\n    def run(self):\n        global rowCounter\n        while True:\n            if self.is_connected:\n                message = \"\"\n                try:\n                    message = self.receive(BUFFER_SIZE)\n                except:\n                    self.is_connected = False\n                if len(message) > 0:\n                    if message == \"exit\":\n                        break\n                    messageDict = json.loads(message, encoding='utf-8')\n                    label = Label(self.root, text=messageDict[\"sender\"], font=(\"Helvetica\", 8), fg=\"blue\", anchor=E, justify=LEFT, wraplength=100)\n                    label.grid(row=rowCounter, column=0)\n                    rowCounter += 1\n                    label = Label(self.root, text=messageDict[\"content\"], font=(\"Helvetica\", 12), fg=\"black\", anchor=E, justify=LEFT, wraplength=100)\n                    label.grid(row=rowCounter, column=0)\n                    rowCounter += 1\n                    label = Label(self.root, text=messageDict[\"time\"], font=(\"Helvetica\", 6), fg=\"gray\", anchor=E, justify=LEFT, wraplength=100)\n                    label.grid(row=rowCounter, column=0)\n                    rowCounter += 1\n                else:\n                    self.is_connected = False\n                    break\n            else:\n                break\n\n\ndef handleExit():\n    global root\n    global client\n    global sender\n    client.send(\"exit\")\n    time.sleep(1)\n    #client.close()\n    root.destroy()\n\nroot = Tk()\nroot.geometry(\"300x500\")\nroot.protocol(\"WM_DELETE_WINDOW\", handleExit)\n\nscrollbar = ScrollBar(root)\nscrollbar.pack(side=\"top\", fill=\"both\", expand=True)\n\n#root.bind_all(\"<MouseWheel>\", scrollbar.onMouseWheel)\n\nTCP_IP = \"localhost\"\nTCP_PORT = 4000\n\nconf_file = \"client.conf\" \n\nif os.path.exists(conf_file):\n    ftr = open(conf_file, \"r\")\n    info = ftr.readline()\n    ftr.close()\n    \n    splitted_info = info.split(\":\")\n\n    TCP_IP = splitted_info[0]\n    TCP_PORT = int(splitted_info[1], 10)\nelse:\n    sys.exit(conf_file + \" does not exist\")\n\nroot.wm_title(\"Client \" + TCP_IP + \":\" + str(TCP_PORT))\nclient = TCPclient(TCP_IP, TCP_PORT, scrollbar.frame)\nsender = TCPsender(client, scrollbar.frame)\nroot.bind(\"<Return>\", sender.enterPressed)\n\nroot.mainloop()\n","sub_path":"messenger/client/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":5519,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"335910631","text":"import pytest\r\nimport os\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.chrome.options import Options\r\n\r\ndef test_opening():\r\n    #opening of portfolio page\r\n    \r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/portfolio')\r\n    \r\n\r\ndef test_resumemore():\r\n    #testing the more button for resume item\r\n\r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/portfolio')\r\n    resume_button= driver.find_element_by_id(\"resumebutton\")\r\n    resume_button.click()\r\n\r\ndef test_navblog():\r\n    #testing the navigation by clicking on blog button on navbar\r\n\r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/portfolio')\r\n    blog_button= driver.find_element_by_id(\"blognav\")\r\n    blog_button.click()\r\n\r\ndef test_navhome():\r\n    #testing the home button on navbar\r\n\r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/portfolio')\r\n    blog_button= driver.find_element_by_id(\"blognav\")\r\n    blog_button.click()\r\n    home_button= driver.find_element_by_id(\"homenav\")\r\n    home_button.click()\r\n\r\ndef test_blogpost():\r\n    #testing the clicking/redirection of blog posts\r\n\r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/portfolio')\r\n    blog_button= driver.find_element_by_id(\"blognav\")\r\n    blog_button.click()\r\n    post_button= driver.find_element_by_id(\"titlepost\")\r\n    post_button.click()\r\n\r\ndef test_comment():\r\n    #test for submitting a comment on blog post\r\n\r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/portfolio')\r\n    blog_button= driver.find_element_by_id(\"blognav\")\r\n    blog_button.click()\r\n    post_button= driver.find_element_by_id(\"titlepost\")\r\n    post_button.click()\r\n    name_field= driver.find_element_by_id(\"id_author\")\r\n    name_field.send_keys(\"Tay Boon Kai\")\r\n    comment_field= driver.find_element_by_id(\"id_body\")\r\n    comment_field.send_keys(\"Hi! Amazing insight and wonderful post. Thanks!\")\r\n    submit_button= driver.find_element_by_id(\"commentsubmit\")\r\n    submit_button.click()\r\n\r\n\r\n@pytest.mark.xfail\r\ndef test_categorylen():\r\n    #test for maxlength in creating category (failing test case)\r\n\r\n    driver=webdriver.Chrome()\r\n    driver.maximize_window()\r\n    driver.get('localhost:8000/admin')\r\n    username_field= driver.find_element_by_id(\"id_username\")\r\n    username_field.send_keys(\"kai\")\r\n    password_field= driver.find_element_by_id(\"id_password\")\r\n    password_field.send_keys(\"tbk@01234\")\r\n    submit_btn= driver.find_element_by_xpath(\"//input[@type='submit']\")\r\n    submit_btn.click()\r\n    category_add= driver.find_element_by_xpath(\"//*[contains(@href, 'category/add')]\")\r\n    category_add.click()\r\n    name_field = driver.find_element_by_class_name(\"vTextField\")\r\n    name_field.send_keys(\"dramamamamamamamamama\")\r\n    assert name_field == \"dramamamamamamamamama\"\r\n    #text is more than 20 characters\r\n    #if assert fails and xfail is activated, it means input validation of <20 characters worked\r\n    \r\n    \r\n    \r\n\r\n\r\n","sub_path":"test_portfolio.py","file_name":"test_portfolio.py","file_ext":"py","file_size_in_byte":3177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"514836384","text":"\"\"\" Core definition of a Thermo Document \"\"\"\nfrom datetime import datetime\nfrom enum import Enum\nfrom typing import ClassVar, Dict, List\n\nfrom pydantic import BaseModel, Field\nfrom pymatgen.analysis.phase_diagram import PhaseDiagram, PhaseDiagramError\nfrom pymatgen.core.periodic_table import Element\n\nfrom emmet.core.material import PropertyDoc\nfrom emmet.core.structure import StructureMetadata\nfrom emmet.stubs import Composition, ComputedEntry\n\n\nclass DecompositionProduct(BaseModel):\n    \"\"\"\n    Entry metadata for a decomposition process\n    \"\"\"\n\n    material_id: str = Field(\n        None, description=\"The material this decomposition points to\"\n    )\n    formula: str = Field(\n        None,\n        description=\"The formula of the decomposed material this material decomposes to\",\n    )\n    amount: float = Field(\n        None,\n        description=\"The amount of the decomposed material by formula units this this material decomposes to\",\n    )\n\n\nclass ThermoDoc(PropertyDoc):\n    \"\"\"\n    A thermo entry document\n    \"\"\"\n\n    property_name: ClassVar[str] = Field(\n        \"thermo\", description=\"The subfield name for this property\"\n    )\n\n    uncorrected_energy_per_atom: float = Field(\n        ..., description=\"The total DFT energy of this material per atom in eV/atom\"\n    )\n\n    energy_per_atom: float = Field(\n        ...,\n        description=\"The total corrected DFT energy of this material per atom in eV/atom\",\n    )\n\n    energy_uncertainy_per_atom: float = Field(\n        None, description=\"\"\n    )  # From correction_uncertainty / N\n\n    formation_energy_per_atom: float = Field(\n        None, description=\"The formation energy per atom in eV/atom\"\n    )\n\n    energy_above_hull: float = Field(\n        ..., description=\"The energy above the hull in eV/Atom\"\n    )\n    is_stable: bool = Field(\n        False,\n        description=\"Flag for whether this material is on the hull and therefore stable\",\n    )\n    equillibrium_reaction_energy_per_atom: float = Field(\n        None,\n        description=\"The reaction energy of a stable entry from the neighboring equilibrium stable materials in eV.\"\n        \" Also known as the inverse distance to hull.\",\n    )\n\n    decomposes_to: List[DecompositionProduct] = Field(\n        None,\n        description=\"List of decomposition data for this material. Only valid for metastable or unstable material.\",\n    )\n\n    energy_type: str = Field(\n        ...,\n        description=\"The type of calculation this energy evaluation comes from. TODO: Convert to enum?\",\n    )\n\n    entry_types: List[str] = Field(\n        description=\"List of available energy types computed for this material\"\n    )\n\n    entries: Dict[str, ComputedEntry] = Field(\n        None,\n        description=\"List of all entries that are valid for this material.\"\n        \" The keys for this dictionary are names of various calculation types\",\n    )\n\n    @classmethod\n    def from_entries(cls, entries: List[ComputedEntry], sandboxes=None):\n\n        pd = PhaseDiagram(entries)\n        sandboxes = sandboxes or [\"core\"]\n\n        docs = []\n\n        for e in entries:\n            (decomp, ehull) = pd.get_decomp_and_e_above_hull(e)\n\n            d = {\n                \"material_id\": e.entry_id,\n                \"uncorrected_energy_per_atom\": e.uncorrected_energy\n                / e.composition.num_atoms,\n                \"energy_per_atom\": e.uncorrected_energy / e.composition.num_atoms,\n                \"formation_energy_per_atom\": pd.get_form_energy_per_atom(e),\n                \"energy_above_hull\": ehull,\n                \"is_stable\": e in pd.stable_entries,\n                \"sandboxes\": sandboxes,\n            }\n\n            if \"last_updated\" in e.data:\n                d[\"last_updated\"] = e.data[\"last_updated\"]\n\n            # Store different info if stable vs decomposes\n            if d[\"is_stable\"]:\n                d[\n                    \"equillibrium_reaction_energy_per_atom\"\n                ] = pd.get_equilibrium_reaction_energy(e)\n            else:\n                d[\"decomposes_to\"] = [\n                    {\n                        \"material_id\": de.entry_id,\n                        \"formula\": de.composition.formula,\n                        \"amount\": amt,\n                    }\n                    for de, amt in decomp.items()\n                ]\n\n            d[\"energy_type\"] = e.parameters.get(\"run_type\", \"Unknown\")\n            d[\"entry_types\"] = [e.parameters.get(\"run_type\", \"Unknown\")]\n            d[\"entries\"] = {e.parameters.get(\"run_type\", \"\"): e}\n\n            for k in [\"last_updated\"]:\n                if k in e.parameters:\n                    d[k] = e.parameters[k]\n                elif k in e.data:\n                    d[k] = e.data[k]\n\n            docs.append(ThermoDoc.from_composition(composition=e.composition, **d))\n\n        return docs\n","sub_path":"emmet-core/emmet/core/thermo.py","file_name":"thermo.py","file_ext":"py","file_size_in_byte":4787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"622273736","text":"#!/usr/bin/python\n\n# just pick one name from name list randomly\n\nimport random\n\nname_list = ['alice', 'bob', 'cathy', 'david', 'eric',\n\t'fred', 'grace', 'helen']\nsize = len(name_list)\n\nfor i in range(10):\n\tidx = random.randint(0, size-1)\t# int(random.random() * size)\n\tprint(name_list[idx], '\\t',)\n","sub_path":"python/ran.py","file_name":"ran.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"35192773","text":"\"\"\"\nProgram: CS 115 Lab 6c\nAuthor: Colin Franceschini\nDescription: This program will read a positive integer and\n express it as the sum of powers of 2.\n\"\"\"\n\n\ndef main():\n    # Read user's input and store it in a variable called i_num.\n    i_num = int(input(\"Enter a number:\"))\n    \n    # while i_num is larger than zero:\n    while i_num > 0:\n        # Initialize n and two_to_n.\n        n = 0\n\n        two_to_n = 1\n        \n\n        # Do all the stuff you were doing before to find n and two_to_n.\n        # Remember that two_to_n is the largest power of 2 less than i_num.\n        while two_to_n < i_num:        \n            n += 1\n            two_to_n = two_to_n * 2\n        \n        if two_to_n > i_num:            \n            n -= 1\n            two_to_n = two_to_n / 2\n\n        i_num = i_num - two_to_n\n\n        # print 2**n.\n            \n        if i_num != 0:\n            \n            print(\"2**\", n, ' + ', end='', sep= '')\n        else:\n            print(\"2**\", n, end='', sep= '')\n            \nmain()\n","sub_path":"CS 115 Programming 1/lab06/lab06c.py","file_name":"lab06c.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"619924312","text":"import xml.etree.ElementTree as ET\nimport os\nimport json\n\ndef load_image_labels(imageset, IMAGE_PATH, LABEL_PATH, class_names=[]):\n    # temp=[]\n    images=[]\n    type=\"instances\"\n    annotations=[]\n    categories = [\n\t\t{\n\t\t\t\"id\" : 1,\n\t\t\t\"name\" : \"Pedestrian\",\n\t\t\t\"supercategory\" : \"object\"\n\t\t},\n        {\n            \"id\": 2,\n            \"name\": \"Cyclist\",\n            \"supercategory\": \"object\"\n        },\n        {\n            \"id\": 3,\n            \"name\": \"Car\",\n            \"supercategory\": \"object\"\n        },\n        {\n            \"id\": 4,\n            \"name\": \"Van\",\n            \"supercategory\": \"object\"\n        },\n        {\n            \"id\": 5,\n            \"name\": \"Truck\",\n            \"supercategory\": \"object\"\n        },\n\t]\n    anno_id=0\n    typeall=[]\n    # load ground-truth from xml annotations\n    label_files_dir = imageset + LABEL_PATH\n    for image_id, label_file_name in enumerate(os.listdir(label_files_dir)):\n        label_file=imageset + LABEL_PATH + label_file_name\n        # image_file =imageset + IMAGE_PATH + label_file_name.split('.')[0]+'.jpg'\n        image_file = ('.').join(label_file_name.split('.')[0:-1]) + '.jpg'\n        tree = ET.parse(label_file)\n        root = tree.getroot()\n        width = float(1624)\n        height = float(1200)\n        label = []\n\n        # for child in root:\n        #     if child.tag=='size':\n        #         width=child.find('width').text\n        #         height=child.find('height').text\n\n\n        images.append({\n            \"file_name\": image_file,\n\t\t\t\"height\": height,\n\t\t\t\"width\": width,\n\t\t\t\"id\": image_id\n\t\t})# id of the image. referenced in the annotation \"image_id\"\n\n        objs = root.find('OBJECTS')\n        for obj in objs.iter('OBJECT'):\n            if obj.find('TYPE').text=='DontCare':\n                continue\n            for subobj in obj:\n                if subobj.tag == 'TYPE':\n                    cls_name = subobj.text\n                    typeall.append(cls_name)\n                    cls_id = class_names.index(cls_name)\n                elif subobj.tag == 'RECT':\n                    xmin = float(subobj.attrib['X'])\n                    ymin = float(subobj.attrib['Y'])\n                    xlen = float(subobj.attrib['W'])\n                    ylen = float(subobj.attrib['H'])\n                    xmax = xlen+xmin\n                    ymax = ylen+ymin\n            annotations.append({\"id\": anno_id,\n                                \"image_id\" : image_id,\n                                \"category_id\" : cls_id,\n                                \"segmentation\" : [[xmin, ymin, xmin, ymax, xmax, ymax, xmax, ymin],],\n                                \"area\" : xlen*ylen,\n                                \"bbox\" : [xmin, ymin, xlen, ylen],\n                                \"iscrowd\" : 0})\n            anno_id=anno_id+1\n            # label.append([image_file,image_id, cls_id, xmin, ymin, xlen, ylen])\n            print([image_file, image_id, cls_id, xmin, ymin, xlen, ylen])\n    settype=set(typeall)\n        # temp.append(np.array(label))\n    return {\"images\":images,\"type\":type,\"annotations\":annotations,\"categories\":categories}\n\nROOT_PATH = '/media/D/qiyan6000/'\nIMAGE_PATH ='Images/'\nNEW_PATH = 'Annotations/'\n# imageset=ROOT_PATH+['train','test']\n\nclasses=['background','Pedestrian','Cyclist','Car','Van','Truck']\n\n# for dirlist in ['train',]:\n#     # decode_xml(subpath, XML_PATH, NEW_PATH)\n#     subpath =ROOT_PATH+dirlist\n#     label_dict = load_image_labels(subpath,IMAGE_PATH,NEW_PATH,classes)\n#     with open(ROOT_PATH+'coco_annotations/'+dirlist+'.json','w') as json_file:\n#         json_file.write(json.dumps(label_dict, ensure_ascii=False))\n#         json_file.close()\n\nlabel_dict = load_image_labels(ROOT_PATH,IMAGE_PATH,NEW_PATH,classes)\nwith open(ROOT_PATH+'coco_annotations/'+'train'+'.json','w') as json_file:\n    json_file.write(json.dumps(label_dict, ensure_ascii=False))\n    json_file.close()\n\n\n\n\n\n","sub_path":"xml2coco.py","file_name":"xml2coco.py","file_ext":"py","file_size_in_byte":3892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"647496071","text":"from dicttoxml import dicttoxml\r\nfrom json import dump\r\nfrom sys import argv\r\n\r\n\r\n# Setup and get file\r\n\r\nwith open(argv[1]) as f:\r\n    avs_file = f.read()\r\n\r\noutput = argv[2]\r\nfiletype = argv[3]\r\n\r\n# Parse file into chunks\r\n\r\nraw_values = avs_file.split(\">\\n<\")\r\n\r\n# Remove extra brackets\r\n\r\nraw_values[0] = raw_values[0].replace(\"<\", \"\", 1)\r\nraw_values[-1] = raw_values[-1].replace(\">\", \"\", 1)\r\n\r\n# Populate dictionary with values\r\n\r\nvalues = {}\r\nfor item in raw_values:\r\n    values[item.split(\":\")[0]] = item.split(\":\")[1]\r\n    \r\n# Fix data types for all variables\r\n\r\nfor key in values.keys():\r\n\r\n    # Check for numerical values\r\n\r\n    try:\r\n        values[key] = float(values[key])\r\n    except:\r\n        pass\r\n    \r\n    try:\r\n\r\n        # Make sure Python can't round the float to int\r\n        \r\n        if values[key] == int(values[key]):\r\n            values[key] = int(values[key])\r\n        continue\r\n    except:\r\n        pass\r\n\r\n    # Check for booleans\r\n\r\n    if values[key] == \"True\":\r\n        values[key] = True\r\n        continue\r\n\r\n    elif values[key] == \"False\":\r\n        values[key] = False\r\n        continue\r\n\r\n    # Fix extra quotes in strings\r\n\r\n    else:\r\n        if values[key].startswith('\"') and values[key].endswith('\"'):\r\n            values[key] = values[key][1:-1]\r\n\r\n# Output to requested file\r\n\r\nif filetype == \"json\":\r\n    dump(values, open(output, \"w\"))\r\n\r\nelif filetype == \"xml\":\r\n    with open(output, \"w\") as f:\r\n        tf = str(dicttoxml(values, attr_type=False))\r\n        tf = tf.replace(\"b'\", \"\")\r\n        tf = tf.replace(tf[-1], \"\")\r\n        f.write(tf)\r\n","sub_path":"AVS File Interpreter/AVS File Interpreter/avs.py","file_name":"avs.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"286939354","text":"import os\n\nfrom arkos import logger\nfrom arkos.utilities import shell\n\n\ndef install(*mods, **kwargs):\n    # Installs a set of NPM packages.\n    cwd = os.getcwd()\n    if \"install_path\" in kwargs:\n        os.chdir(kwargs[\"install_path\"])\n    s = shell(\"npm install %s%s\" % (\" \".join(x for x in mods), (\" --\"+\" --\".join(x for x in kwargs[\"opts\"]) if kwargs.has_key(\"opts\") else \"\")))\n    os.chdir(cwd)\n    if s[\"code\"] != 0:\n        logger.error(\"NPM install of %s failed; log output follows:\\n%s\"%(\" \".join(x for x in mods),s[\"stderr\"]))\n        raise Exception(\"NPM install failed, check logs for info\")\n\ndef remove(*mods):\n    # Remove an installed NPM package.\n    s = shell(\"npm uninstall %s\" % \" \".join(x for x in mods), stderr=True)\n    if s[\"code\"] != 0:\n        logger.error(\"Failed to remove %s via npm; log output follows:\\n%s\"%(\" \".join(x for x in mods),s[\"stderr\"]))\n        raise Exception(\"Failed to remove %s via npm, check logs for info\"%\" \".join(x for x in mods))\n\ndef install_from_package(path, stat=\"production\", opts={}):\n    # Installs a set of NPM package dependencies from an NPM package.json.\n    cwd = os.getcwd()\n    os.chdir(path)\n    s = shell(\"npm install %s%s\" % (\" --\"+stat if stat else \"\", \" --\"+\" --\".join(x+\"=\"+opts[x] for x in opts) if opts else \"\"))\n    os.chdir(cwd)\n    if s[\"code\"] != 0:\n        logger.error(\"NPM install of %s failed; log output follows:\\n%s\"%(path,s[\"stderr\"]))\n        raise Exception(\"NPM install failed, check logs for info\")\n","sub_path":"arkos/languages/nodejs.py","file_name":"nodejs.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"328109071","text":"#!/usr/bin/env python3\n\"\"\"\n    Handles local storage\n\"\"\"\n\nimport os, re, sys\nimport tarfile, zipfile, bz2\nimport random, string\nimport json\nimport pathlib\n\nfrom pprint import pprint\n\nREGISTRY_FILES=(\"ALL\", \"NEW\", \"MD5\", \"SHA1\", \"SHA256\")\n\nclass LocalMirrorRegistry( object ):\n\n    def __init__( self, path, **opt ):\n        self._debug = False\n        self._path = pathlib.Path( path )\n\n        self._options = opt\n\n    def create_structure( self ):\n        if not self._path.exists():\n            self._path.mkdir( mode=0o755, parents=True )\n\n        for f in REGISTRY_FILES:\n            p = pathlib.Path( \"%s/%s\" % ( self._path, f ) )\n            pprint( p )\n\n\nclass LocalMirrorRepo( object ):\n\n    def __init__( self, path, **opt ):\n        pass\n","sub_path":"aptmirror/mirror/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"408219969","text":"# encoding: utf8\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Profile',\n            fields=[\n                ('id', models.AutoField(serialize=False, auto_created=True, primary_key=True, verbose_name='ID')),\n                ('user', models.OneToOneField(to_field='id', to=settings.AUTH_USER_MODEL)),\n                ('name', models.CharField(max_length=200, blank=True, verbose_name='profile name')),\n                ('date_of_birth', models.DateField(null=True, verbose_name='birthday', blank=True)),\n                ('show_age', models.BooleanField(default=True, verbose_name='show my age')),\n                ('description', models.TextField(blank=True, verbose_name='about yourself')),\n                ('relationship_status', models.CharField(max_length=100, choices=[('single', 'Single'), ('relationship', 'In a relationship'), ('casual', 'Open relationship'), ('married', 'Married'), ('divorced', 'Divorced'), ('widowed', 'Widowed')], blank=True)),\n                ('looking_for', models.CharField(max_length=100, choices=[('chats', 'People to chat with'), ('friends', 'New friends'), ('dating', 'Dating'), ('life_partner', 'A life partner'), ('casual', 'Casual relationships')], blank=True)),\n            ],\n            options={\n                'verbose_name_plural': 'Profiles',\n                'verbose_name': 'Profile',\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","sub_path":"apps/profiles/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":1632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"17928808","text":"#coding=utf-8\nimport os\nimport cv2\nimport numpy as np\nimport multiprocessing\nfrom contextlib import contextmanager\nimport random\nimport sys\nfrom queue import Queue\nimport ujson as json\n\nEPS = 1e-9\n\n@contextmanager\ndef poolContext(*args, **kwargs):\n    pool = multiprocessing.Pool(*args, **kwargs)\n    yield pool\n    pool.terminate()\n\ndef read_images(image_dir, image_size=None, normalize=True, limit=-1):\n    file_names = os.listdir(image_dir)\n    random.shuffle(file_names)\n    file_paths = []\n    for file_name in file_names:\n        file_path = image_dir + \"/\" + file_name\n        file_paths.append(file_path)\n\n    if limit != -1:\n        random.shuffle(file_paths)\n        file_paths = file_paths[:limit]\n\n    images = []\n    for file_path in file_paths:\n        #print(file_path)\n        image = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)\n        if image_size is not None:\n            image = cv2.resize(image, image_size[::-1])\n            assert image_size == image.shape\n        if normalize is True:\n            image = image.astype(float) - np.mean(image)\n            std = np.std(image)\n            if std != 0:\n                image /= std\n        images.append(image)\n    return images\n\ndef read_data(image_dir, image_size=None, normalize=True, limit=-1, additional=True):\n    with open(\"./hist\", \"r\") as fin:\n        hist_dict = json.loads(fin.read())\n\n    file_names = os.listdir(image_dir)\n    random.shuffle(file_names)\n    file_paths = []\n    for file_name in file_names:\n        file_path = image_dir + \"/\" + file_name\n        file_paths.append(file_path)\n\n    if limit != -1:\n        random.shuffle(file_paths)\n        file_paths = file_paths[:limit]\n\n    data = []\n    for file_path in file_paths:\n        #print(file_path)\n        image = cv2.imread(file_path, cv2.IMREAD_GRAYSCALE)\n        additional_features = get_additional_features(image, file_path, hist_dict) if additional else None\n        if image_size is not None:\n            image = cv2.resize(image, image_size[::-1])\n            assert image_size == image.shape\n        if normalize is True:\n            image = image.astype(float) - np.mean(image)\n            std = np.std(image)\n            if std != 0:\n                image /= std\n        data.append({\"image\": image, \"additional_features\": additional_features})\n    return data\n\ndef get_additional_features(image, file_path, hist_dict): # 计算图片的附加信息\n    #print(file_path)\n    path_split = file_path.split(\"/\")[-1].split(\".\")[0].split(\"_\")[1:]\n    assert len(path_split) == 6\n    additional_features = []\n    pos = (int(path_split[0]), int(path_split[1]))\n    size = (int(path_split[2]), int(path_split[3]))\n    page_size = (int(path_split[4]), int(path_split[5]))\n    all_mean = image.mean()\n    additional_features += [pos[0], pos[1], size[0], size[1], all_mean]\n    additional_features += [page_size[0] - pos[0] - size[0], page_size[1] - pos[1] - size[1]]\n    unit_x, unit_y = int(size[0] / 4), int(size[1] / 8)\n    for x_idx in range(4):\n        x_s = x_idx * unit_x\n        x_e = (x_idx + 1) * unit_x if x_idx < 3 else size[0]\n        for y_idx in range(8):\n            y_s = y_idx * unit_y\n            y_e = (y_idx + 1) * unit_y if y_idx < 7 else size[1]\n            subwin = image[x_s: x_e, y_s: y_e]\n            additional_features.append(subwin.mean())\n            additional_features.append(all_mean - subwin.mean())\n            additional_features.append(subwin.max())\n            additional_features.append(subwin.min())\n    #additional_features += cv2.resize(image, (48, 16)).reshape((-1,)).tolist()\n    #print(type(additional_features), len(additional_features))\n    #print(additional_features)\n    #hog_features = HOG_features(image)\n    #print(hog_features.shape)\n    hist = get_hist(image).tolist()\n    additional_features += hist\n    for f in [correlation, chi_square, intersection, bhattacharyya]:\n        for mean_hist in hist_dict.values():\n            additional_features.append(f(hist, mean_hist))\n    #print(len(additional_features))\n    return additional_features\n\ndef correlation(hist1, hist2):\n    hist1 = np.array(hist1)\n    hist2 = np.array(hist2)\n    hist1_mean = hist1.mean()\n    hist2_mean = hist2.mean()\n\n    d = np.sum((hist1 - hist1_mean) * (hist2 - hist2_mean)) / \\\n            (np.sqrt(np.sum((hist1 - hist1_mean) * (hist1 - hist1_mean)) * np.sum((hist2 - hist2_mean) * (hist2 - hist2_mean))) + EPS)\n    return d\n\ndef chi_square(hist1, hist2):\n    hist1 = np.array(hist1)\n    hist2 = np.array(hist2)\n    d = np.sum((hist1 - hist2) * (hist1 - hist2) / (hist1 + hist2 + EPS))\n    return d\n\ndef intersection(hist1, hist2):\n    d = 0.0\n    for idx in range(len(hist1)):\n        d += min(hist1[idx], hist2[idx])\n    return d\n\ndef bhattacharyya(hist1, hist2):\n    hist1 = np.array(hist1)\n    hist2 = np.array(hist2)\n    a = np.sum(np.sqrt(hist1 * hist2))\n    b = np.sqrt(hist1.mean() * hist2.mean()) * 64 + EPS\n    d = np.sqrt(1 - a / b)\n    return d\n\ndef get_hist(image):\n    hist = np.zeros(64)\n    for x in image.reshape((-1,)):\n        hist[int(x / 4)] += 1\n    #hist /= (image.shape[0] * image.shape[1])\n    return hist\n\ndef get_image_path(data_path, pre_fix=[]):\n    ret = []\n    dir_queue = Queue()\n    dir_queue.put(data_path)\n    while not dir_queue.empty():\n        now_path = dir_queue.get()\n        if os.path.isdir(now_path):\n            dir_list = os.listdir(now_path)\n            for son_path in dir_list:\n                dir_queue.put((\"%s/%s\" % (now_path, son_path)))\n        else:\n            file_name = now_path.split(\"/\")[-1]\n            for fix in pre_fix:\n                if file_name.startswith(fix):\n                    ret.append(now_path)\n                    break\n            if len(pre_fix) == 0: # 默认读入所有非background elements\n                if not file_name.startswith(\"background\"):\n                    ret.append(now_path)\n\n    return ret\n\ndef gen_mean_hist():\n    #read_data(image_dir=\"./data/noiseful_positive\", image_size=(16, 48), normalize=True,limit=100)\n    #pass\n    mean_hist = {}\n    images = read_images(\"./data/neg_data\", normalize=False, limit=10000)\n    hist = np.zeros(64)\n    for image in images:\n        hist += get_hist(image)\n    hist /= hist.sum()\n    mean_hist[\"false_hist\"] = hist\n    images = read_images(\"./data/noiseful_pos_data\", normalize=False)\n    hist = np.zeros(64)\n    for image in images:\n        hist += get_hist(image)\n    hist /= hist.sum()\n    mean_hist[\"noise_p_hist\"] = hist\n    images = read_images(\"./data/positive_data_for_java_screenhot\", normalize=False)\n    hist = np.zeros(64)\n    for image in images:\n        hist += get_hist(image)\n    hist /= hist.sum()\n    mean_hist[\"p_hist\"] = hist\n    with open(\"./hist\", \"w\") as fout:\n        fout.write(json.dumps(mean_hist))\n\ndef main():\n    gen_mean_hist()\n    #read_data(image_dir=\"./data/noiseful_positive\", image_size=(16, 48), normalize=True,limit=100)\n    pass\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6931,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"109084737","text":"VIS_SERVICE_SOLR_PATH = 'http://api.adsabs.harvard.edu/v1/search/query'\n\nVIS_SERVICE_TVRH_PATH = 'http://0.0.0.0:9000/solr/tvrh/'\n\n#This section configures this application to act as a client, for example to query solr via adsws\nVIS_SERVICE_API_TOKEN = 'This should be changed'\n\n#word cloud config\nVIS_SERVICE_WC_MAX_RECORDS = 500\nVIS_SERVICE_WC_START = 0\n\n#threshold that a word stem has to pass before being included\nVIS_SERVICE_WC_MIN_PERCENT_WORD = 3\nVIS_SERVICE_WC_MIN_OCCURRENCES_WORD = 2\n\n#author network config\nVIS_SERVICE_AN_MAX_RECORDS = 1000\nVIS_SERVICE_AN_START = 0\n\n#configuration for augmented graph data\nVIS_SERVICE_AN_MAX_GROUPS = 8\n\n#paper network config\n#paper network calculation is kind of slow, so limiting the number of records for now.\nVIS_SERVICE_PN_MAX_RECORDS = 1000\nVIS_SERVICE_PN_START = 0\n\n#configuration for augmented graph data\nVIS_SERVICE_PN_MAX_GROUPS = 10\n\n\n","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"56043527","text":"from flask import Flask, jsonify, request\nimport pickle\nimport numpy as np\n\n\napp = Flask(__name__)\n# reading the model created\nmodel = pickle.load(open('model.pkl', 'rb'))\n\n\n# post req with body as [40,42,12] and response as predicted disease\n@app.route('/v1/predict', methods=['POST'])\ndef predict_disease():\n    data = request.get_json()\n    print(data['symptoms'])\n    # generating the list to prepare input for model\n    symptoms = np.zeros(131)\n    for index in data['symptoms']:\n        symptoms[index] = 1\n    prediction = model.predict([symptoms])\n    print(prediction[0])\n    # sending the predicted disease with status code\n    return jsonify({\"result\": str(prediction[0])}), 200\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, port=8000)\n","sub_path":"app_backend/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"617179494","text":"# Import required libraries\nimport os.path\nfrom os import path\n\n# This comment was added for fun\n# os.getcwd()\n# print(os.listdir())\n# Test name functions\n# input_name = input(\"Write your input name here: \")\n\n# Create required working directory if needed\ndef folder_manip(parent_path, input_name):\n    print(parent_path)\n    os.chdir(parent_path + '//data_process')\n    ### Debug: Print the parent folder name\n    # print(path_parent)\n\n    # Define folder_make command:\n    def folder_make(folder_name):\n        if not path.exists(folder_name):\n            os.mkdir(folder_name)\n            print(folder_name + ' created!')\n        else:\n            print(folder_name + ' existed!')\n        return\n\n    # Create folder with name\n    folder_make(input_name)\n    os.chdir(\"./{}\".format(input_name))\n    # Create sub-directories\n    folder_make(\"calib_image\")\n    folder_make(\"data\")\n    folder_make(\"park_lot_info\")\n    folder_make(\"ref_image\")\n    folder_make(\"calib_image_cut\")\n    folder_make(\"image_take_from_camera\")\n\n    # Return the folder pointer to main folder\n    os.chdir(parent_path)\n    ### Debug: Print current working directory\n    # print(os.getcwd())\n\n    # print(os.listdir)\n\n# Create required working files\ndef file_manip(parent_path, input_name):  # Making log files for future access\n    os.chdir(parent_path + '//data_process//{}'.format(input_name))\n    ### Debug: Print working directory\n    # print(parent_path)\n\n    def file_make(filename):\n        if not path.isfile(filename):\n            f = open(filename, \"w\")\n            f.close()\n            print(filename + ' created!')\n        else:\n            print(filename + ' existed!')\n\n    # Create files to store debug data\n    file_make(\"debug.txt\")\n    file_make(\"runTime.txt\")\n\n    # Create files to store parking lot data\n    os.chdir(\".\\\\park_lot_info\")\n    file_make(\"slot.txt\")\n    file_make(\"landmark.txt\")\n\n    os.chdir(parent_path)\n\n# Open available parking lot file, append positions to a list\ndef file_open_avail_parklot(parent_path):\n    # Initiate values\n    file_flag = 0\n    avail_parklot = []\n\n    # Open file contains defined parking lot name\n    # Avoid using static addresses\n    f_avail_parklot = open(parent_path + \"\\\\data_process\\\\avail_parklot.txt\", 'r+')\n    if os.stat(parent_path + \"\\\\data_process\\\\avail_parklot.txt\").st_size == 0:\n        # Indicates if the parking lot is defined or not. If not, halt the processing program\n        file_flag = 1                               # Not defined value\n        avail_parklot = None\n        return avail_parklot, file_flag\n    else:\n        string_x = \"\"\n        # Initiate new list, get the value from the list\n        lis = [line.split() for line in f_avail_parklot]\n        file_length = len(lis)\n\n        for ii in range(file_length):\n            for val in lis[ii]:\n                string_x = val\n            avail_parklot.append(string_x)\n    f_avail_parklot.close()\n\n    return avail_parklot, file_flag\n\n# Add new parking lot to the monitoring file\ndef file_append_avail_parklot(parent_path, new_parklot_name):\n    # Initiate values:\n    # Avoid using static addresses\n    f_avail_parklot = open(parent_path + \"\\\\data_process\\\\avail_parklot.txt\", 'a+')\n    f_avail_parklot.write(\"\\n\")\n    f_avail_parklot.write(new_parklot_name)\n    f_avail_parklot.close()\n\n    return 0\n\n# Rewrite the parking lot's parking slots' positions\ndef file_slot_write(parent_path, park_lot_name, slot_x, slot_y, num_of_slot):\n    # Initiate values:\n    f_slot = open(parent_path + \"\\\\data_process\\\\{}\\\\park_lot_info\\\\slot.txt\".format(park_lot_name), 'w+')\n    # Write slot coordinates to the file\n    for slot_index in range(1, num_of_slot+1):\n        f_slot.write('%d ' % slot_index)\n        f_slot.write('%d ' % slot_x[slot_index])\n        f_slot.write('%d\\n' % slot_y[slot_index])\n\n    f_slot.close()\n\n    return 0\n\n# Rewrite the landmarks' positions\ndef file_landmark_write(parent_path, ref_xx, ref_yy, park_lot_name):\n    # Initiate values:\n    # Avoid using static addresses\n    f_landmark = open(parent_path + \"\\\\data_process\\\\{}\\\\park_lot_info\\\\landmark.txt\".format(park_lot_name), 'w+')\n    f_runt = open(parent_path + \"\\\\data_process\\\\{}\\\\runTime.txt\".format(park_lot_name), 'w+')\n    # Write value to the file\n    f_runt.write('1')\n    # Write landmark coordinates to the file\n    for landmark_index in range(1, 5):\n        f_landmark.write('%d ' % landmark_index)\n        f_landmark.write('%d ' % ref_xx[landmark_index])\n        f_landmark.write('%d\\n' % ref_yy[landmark_index])\n\n    f_landmark.close()\n    f_runt.close()\n\n    return 0\n\n# Remove all folder content before an another run, to avoid image show errors\n# A better approach to file management is recommended. Deleting files is not recommended.\ndef remove_folder_content(address):\n    for file_name in os.listdir(address):\n        if os.path.exists(os.path.join(address, file_name)):\n            os.remove(os.path.join(address, file_name))","sub_path":"folderFileManip.py","file_name":"folderFileManip.py","file_ext":"py","file_size_in_byte":4942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"327898766","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Copyright 2011-2014, Nigel Small\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\n\"\"\"\nThe `calendar` module provides standardised date management functionality\nbased on a calendar subgraph::\n\n    from py2neo import neo4j\n    from py2neo.calendar import GregorianCalendar\n\n    graph_db = neo4j.GraphDatabaseService()\n    time_index = graph_db.get_or_create_index(neo4j.Node, \"TIME\")\n    calendar = GregorianCalendar(time_index)\n\n    graph_db.create(\n        {\"name\": \"Alice\"},\n        (0, \"BORN\", calendar.day(1800, 1, 1)),\n        (0, \"DIED\", calendar.day(1900, 12, 31)),\n    )\n\nThe root calendar node is held within a dedicated node index which needs\nto be supplied to the calendar constructor.\n\nAll dates managed by the :py:class:`GregorianCalendar` class adhere to a\nhierarchy such as::\n\n    (CALENDAR)-[:YEAR]->(2000)-[:MONTH]->(12)-[:DAY]->(25)\n\n\"\"\"\n\n\nfrom __future__ import unicode_literals\n\nfrom datetime import date as _date\n\nfrom .neo4j import CypherQuery\n\n\nclass GregorianCalendar(object):\n\n    class Date(object):\n\n        def __init__(self, year, month=None, day=None):\n            if year and month and day:\n                self._date = _date(year, month, day)\n                self._resolution = 3\n                self.year = year\n                self.month = month\n                self.day = day\n            elif year and month:\n                self._date = _date(year, month, 1)\n                self._resolution = 2\n                self.year = year\n                self.month = month\n                self.day = None\n            elif year:\n                self._date = _date(year, 1, 1)\n                self._resolution = 1\n                self.year = year\n                self.month = None\n                self.day = None\n\n        def __str__(self):\n            if self.year and self.month and self.day:\n                return \"{y:04d}-{m:02d}-{d:02d}\".format(\n                    y=self.year, m=self.month, d=self.day\n                )\n            elif self.year and self.month:\n                return \"{y:04d}-{m:02d}\".format(\n                    y=self.year, m=self.month\n                )\n            elif self.year:\n                return \"{y:04d}\".format(\n                    y=self.year\n                )\n\n        def get_node(self, calendar):\n            if self.year and self.month and self.day:\n                return calendar.day(self.year, self.month, self.day)\n            elif self.year and self.month:\n                return calendar.month(self.year, self.month)\n            elif self.year:\n                return calendar.year(self.year)\n            else:\n                raise ValueError()\n\n    class DateRange(object):\n\n        def __init__(self, start_date=None, end_date=None):\n            if start_date and end_date:\n                self.start_date = GregorianCalendar.Date(*start_date)\n                self.end_date = GregorianCalendar.Date(*end_date)\n                # check both dates are compatible\n                if self.start_date._resolution != self.end_date._resolution:\n                    raise ValueError(\"Range start and end dates are specified \"\n                                     \"at different resolutions\")\n                # ensure dates are correctly ordered\n                if self.start_date._date > self.end_date._date:\n                    self.start_date, self.end_date = self.end_date, self.start_date\n            elif start_date:\n                self.start_date = GregorianCalendar.Date(*start_date)\n                self.end_date = None\n            elif end_date:\n                self.start_date = None\n                self.end_date = GregorianCalendar.Date(*end_date)\n            else:\n                raise ValueError(\"Either start date or end date must \"\n                                 \"be specified for a date range\")\n\n    def __init__(self, index):\n        \"\"\" Create a new calendar instance pointed to by the index provided.\n        \"\"\"\n        self._index = index\n        self._graph_db = self._index.service_root.graph_db\n        self._calendar = self._index.get_or_create(\"calendar\", \"Gregorian\", {})\n\n    def calendar(self):\n        return self._calendar\n\n    def day(self, year, month, day):\n        \"\"\" Fetch the calendar node representing the day specified by `year`,\n        `month` and `day`.\n        \"\"\"\n        d = GregorianCalendar.Date(year, month, day)\n        date_path = self._calendar.get_or_create_path(\n            \"YEAR\",  {\"year\": d.year},\n            \"MONTH\", {\"year\": d.year, \"month\": d.month},\n            \"DAY\",   {\"year\": d.year, \"month\": d.month, \"day\": d.day},\n        )\n        return date_path.nodes[-1]\n\n    def month(self, year, month):\n        \"\"\" Fetch the calendar node representing the month specified by `year`\n        and `month`.\n        \"\"\"\n        d = GregorianCalendar.Date(year, month)\n        date_path = self._calendar.get_or_create_path(\n            \"YEAR\",  {\"year\": d.year},\n            \"MONTH\", {\"year\": d.year, \"month\": d.month},\n        )\n        return date_path.nodes[-1]\n\n    def year(self, year):\n        \"\"\" Fetch the calendar node representing the year specified by `year`.\n        \"\"\"\n        d = GregorianCalendar.Date(year)\n        date_path = self._calendar.get_or_create_path(\n            \"YEAR\",  {\"year\": d.year},\n        )\n        return date_path.nodes[-1]\n\n    def date(self, date):\n        return GregorianCalendar.Date(*date).get_node(self)\n\n    def date_range(self, start_date=None, end_date=None):\n        \"\"\" Fetch the calendar node representing the date range defined by\n        `start_date` and `end_date`. If either are unspecified, this defines an\n        open-ended range. Either `start_date` or `end_date` must be specified.\n        \"\"\"\n        #                         (CAL)\n        #                           |\n        #                       [:RANGE]\n        #                           |\n        #                           v\n        # (START)<-[:START_DATE]-(RANGE)-[:END_DATE]->(END)\n        range_ = GregorianCalendar.DateRange(start_date, end_date)\n        start, end = range_.start_date, range_.end_date\n        if start and end:\n            # if start and end are equal, return the day node instead\n            if (start.year, start.month, start.day) == (end.year, end.month, end.day):\n                return start.get_node(self)\n            if (start.year, start.month) == (end.year, end.month):\n                root = self.month(start.year, start.month)\n            elif start.year == end.year:\n                root = self.year(start.year)\n            else:\n                root = self._calendar\n            query = \"\"\"\\\n                START z=node({z}), s=node({s}), e=node({e})\n                CREATE UNIQUE (s)<-[:START_DATE]-(r {r})-[:END_DATE]->(e),\n                              (z)-[:DATE_RANGE]->(r {r})\n                RETURN r\n            \"\"\"\n            params = {\n                \"z\": root._id,\n                \"s\": start.get_node(self)._id,\n                \"e\": end.get_node(self)._id,\n                \"r\": {\n                    \"start_date\": str(start),\n                    \"end_date\": str(end),\n                },\n            }\n        elif start:\n            query = \"\"\"\\\n                START z=node({z}), s=node({s})\n                CREATE UNIQUE (s)<-[:START_DATE]-(r {r}),\n                              (z)-[:DATE_RANGE]->(r {r})\n                RETURN r\n            \"\"\"\n            params = {\n                \"z\": self._calendar._id,\n                \"s\": start.get_node(self)._id,\n                \"r\": {\n                    \"start_date\": str(start),\n                },\n            }\n        elif end:\n            query = \"\"\"\\\n                START z=node({z}), e=node({e})\n                CREATE UNIQUE (r {r})-[:END_DATE]->(e),\n                              (z)-[:DATE_RANGE]->(r {r})\n                RETURN r\n            \"\"\"\n            params = {\n                \"z\": self._calendar._id,\n                \"e\": end.get_node(self)._id,\n                \"r\": {\n                    \"end_date\": str(end),\n                },\n            }\n        else:\n            raise ValueError(\"Either start or end date must be supplied \"\n                             \"for a date range\")\n        return CypherQuery(self._graph_db, query).execute_one(**params)\n\n    def quarter(self, year, quarter):\n        if quarter == 1:\n            return self.date_range((year, 1, 1), (year, 3, 31))\n        elif quarter == 2:\n            return self.date_range((year, 4, 1), (year, 6, 30))\n        elif quarter == 3:\n            return self.date_range((year, 7, 1), (year, 9, 30))\n        elif quarter == 4:\n            return self.date_range((year, 10, 1), (year, 12, 31))\n        else:\n            raise ValueError(\"quarter must be in 1..4\")\n","sub_path":"mac_env/lib/python2.7/site-packages/py2neo/calendar.py","file_name":"calendar.py","file_ext":"py","file_size_in_byte":9247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"439376861","text":"\"\"\"\nwhat we need to do here is for all the possible row place 1 queen just that it doesnt violate\nattack rule of queen\n\nhow i am going to solve is for all row place queen in first column and see if it violate if it doesnt\nthan increment the column and check if it violates ..if it does increment the column and check again\n\ndont forget to return from functions\n\nmost important thing learned to do deepcopy when list has objects since this is list of list we did deepcopy other wise appending to list would\nhave suffice\nlist is by reference\nway to copy list by value\ncopytolist = copyfromlist[:}\ncopytolist = copy.deepcopy(copyfromlist) -- copyfromlist has objects like list of list or list of dict etc..\n\nin order to assign index of list you need to intialize it\n\n\"\"\"\n\nimport sys\nimport os\nimport copy\n\ndef find_all_arrangements(n):\n    #print(n)\n    board = [[ '-' for j in range(n)] for i in range(n)]\n    sol =[]\n    helper(n,0,board,sol)\n    print(len(sol))\n    strsol=[ [ [ ['-']] for row in range(len(sol[k]))] for k in range(len(sol))]\n    for k in range(len(sol)):\n        for row in range(len(sol[k])):\n            string=\"\"\n            for col in range(len(sol[k][row])):\n                string+=sol[k][row][col]\n            strsol[k][row]=string\n\n    return strsol\n\ndef helper(n,col,board,sol):\n\n    if col == n:\n        sol.append(copy.deepcopy(board))\n\n    for row in range(n):\n        if validmove(n,row,col,board):\n            board[row][col] = 'q'\n            if helper(n,col+1,board,sol):\n                return True # dont return to get all possible solutions\n            else:\n                board[row][col] = '-' #backtrack\n\n    return False\n\n\ndef validmove(n,row,col,board):\n\n    if row < 0 or row > n or col < 0 or col > n:\n        return False\n\n    for i in range(col):\n        if board[row][i] == 'q':\n            return False\n\n    r = row\n    c = col\n\n    while r >= 0 and c >= 0:\n        if board[r][c] == 'q':\n            return False\n        r -= 1\n        c -= 1\n\n    r = row\n    c = col\n    while r < n and c >= 0:\n        if board[r][c] == 'q':\n            return False\n        r += 1\n        c -= 1\n\n    return True\n\n\nif __name__ == \"__main__\":\n    #n = int(input())\n\n    res = find_all_arrangements(4)\n\n    for res_x in res:\n        for res_y in res_x:\n            print(str(res_y) + \" \")\n        print(\"\\n\")\n\n\n\n\n","sub_path":"Python/IK/Recursion/nQueens.py","file_name":"nQueens.py","file_ext":"py","file_size_in_byte":2347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"365672846","text":"# coding: utf-8\n# Author：Brent\n# Date ：2020/6/21 7:46 AM\n# Tool ：PyCharm\n# Describe ：给定一个二叉搜索树, 找到该树中两个指定节点的最近公共祖先。\n#\n# 百度百科中最近公共祖先的定义为：“对于有根树 T 的两个结点 p、q，最近公共祖先表示为一个结点 x，满足 x 是 p、q 的祖先且 x 的深度尽可能大（一个节点也可以是它自己的祖先）。”\n#\n# 例如，给定如下二叉搜索树:  root = [6,2,8,0,4,7,9,null,null,3,5]\n#\n#\n#\n#  \n#\n# 示例 1:\n#\n# 输入: root = [6,2,8,0,4,7,9,null,null,3,5], p = 2, q = 8\n# 输出: 6\n# 解释: 节点 2 和节点 8 的最近公共祖先是 6。\n# 示例 2:\n#\n# 输入: root = [6,2,8,0,4,7,9,null,null,3,5], p = 2, q = 4\n# 输出: 2\n# 解释: 节点 2 和节点 4 的最近公共祖先是 2, 因为根据定义最近公共祖先节点可以为节点本身。\n#  \n#\n# 说明:\n#\n# 所有节点的值都是唯一的。\n# p、q 为不同节点且均存在于给定的二叉搜索树中。\n#\n# 来源：力扣（LeetCode）\n# 链接：https://leetcode-cn.com/problems/lowest-common-ancestor-of-a-binary-search-tree\nclass Solution(object):\n    def lowestCommonAncestor(self, root, p, q):\n        root_value, p_value, q_value = root.val, p.val, q.val\n        if p_value < root_value and q_value < root_value:\n            \"\"\"节点都在左子树\"\"\"\n            return self.lowestCommonAncestor(root.left, p, q)\n        elif p_value > root_value and q_value > root_value:\n            \"\"\"节点都在右子树\"\"\"\n            return self.lowestCommonAncestor(root.right, p, q)\n        else:\n            \"\"\"节点在root节点两边\"\"\"\n            return root","sub_path":"geekAlgorithm010/Week03/lowest-common-ancestor-of-a-binary-search-tree.py","file_name":"lowest-common-ancestor-of-a-binary-search-tree.py","file_ext":"py","file_size_in_byte":1677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"371695391","text":"# Implementation of the \"Generalised Product of GP Experts\"\n# by Cao & Fleet, 2014\n#\n# Pablo Moreno-Munoz (pmoreno@tsc.uc3m.es)\n# Universidad Carlos III de Madrid\n# May 2020\n\nimport torch\n\nclass GenPoeGP(torch.nn.Module):\n    \"\"\"\n    -- Generalised Product of GP Experts --\n    \"\"\"\n    def __init__(self, models, input_dim=1.0):\n        super(GenPoeGP, self).__init__()\n\n        self.input_dim = int(input_dim)  # dimension of x\n\n        # Adjacent Local GP Models\n        self.models = models  # is a list\n\n    def forward(self):\n        return 1.0\n\n    def predictive(self, x, y, x_new):\n        # x is a list of x_k (distributed)\n        # y is a list of y_k (distributed)\n\n        K = len(self.models)\n        beta_k = 1/K\n\n        gp_m = torch.zeros(x_new.size())\n        gp_v = torch.zeros(x_new.size())\n\n        for k, model_k in enumerate(self.models):\n            m_k, v_k = model_k.predictive(x[k], y[k], x_new)\n\n            gp_m += (beta_k*m_k)/v_k\n            gp_v += beta_k/v_k\n\n        gp_v = 1.0/gp_v\n        gp_m = gp_v*gp_m\n\n        return gp_m, gp_v\n\n\n    def rmse(self, x, y, x_new, f_new):\n        f_gp,_ = self.predictive(x, y, x_new)\n        rmse = torch.sqrt(torch.mean((f_new - f_gp)**2.0)).detach().numpy()\n        return rmse\n\n    def mae(self, x, y, x_new, f_new):\n        f_gp,_ = self.predictive(x, y, x_new)\n        mae = torch.mean(torch.abs(f_new - f_gp)).detach().numpy()\n        return mae\n\n    def nlpd(self, x, y, x_new, y_new):\n        f_gp, v_gp = self.predictive(x, y, x_new)\n        nlpd = - torch.mean(self.models[0].likelihood.log_predictive(y_new, f_gp, v_gp)).detach().numpy()\n        return nlpd\n\n    # FOR HIERARCHICAL SETTINGS\n\n    def predictive_layer(self, gps_m, gps_v, x_new):\n        # gps_m is a list of gp_m (distributed)\n        # gps_v is a list of gp_v (distributed)\n\n        K = len(gps_m)\n        beta_k = 1/K\n\n        gp_m = torch.zeros(x_new.size())\n        gp_v = torch.zeros(x_new.size())\n\n        for k, m_k in enumerate(gps_m):\n            v_k = gps_v[k]\n\n            gp_m += (beta_k*m_k)/v_k\n            gp_v += beta_k/v_k\n\n        gp_v = 1.0/gp_v\n        gp_m = gp_v*gp_m\n\n        return gp_m, gp_v\n\n    def rmse_layer(self, gps_m, gps_v, x_new, f_new):\n        f_gp,_ = self.predictive_layer(gps_m, gps_v, x_new)\n        rmse = torch.sqrt(torch.mean((f_new - f_gp)**2.0)).detach().numpy()\n        return rmse\n\n    def mae_layer(self, gps_m, gps_v, x_new, f_new):\n        f_gp,_ = self.predictive_layer(gps_m, gps_v, x_new)\n        mae = torch.mean(torch.abs(f_new - f_gp)).detach().numpy()\n        return mae\n\n    def nlpd_layer(self, gps_m, gps_v, x_new, y_new):\n        f_gp, v_gp = self.predictive_layer(gps_m, gps_v, x_new)\n        nlpd = - torch.mean(self.models[0].likelihood.log_predictive(y_new, f_gp, v_gp)).detach().numpy()\n        return nlpd\n","sub_path":"baselines/gpoegp.py","file_name":"gpoegp.py","file_ext":"py","file_size_in_byte":2822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"639997946","text":"from memo.models import *\nfrom datetime import datetime, timedelta\nfrom django.shortcuts import render, redirect\n# owner contents information class\nclass Owner:\n    def __init__(self, owner_id):    \n        self.owner = Users.objects.get(user_id=owner_id)\n        self.ownerMemos = Memos.objects.filter(made_by=self.owner).exclude(tab_name=\"TRASH\")\n        self.ownerPublicMemos = Memos.objects.filter(made_by=self.owner, disclose=\"public\").exclude(tab_name=\"TRASH\")\n\n    def overTimePublicMemos(self):\n        timePeriod = datetime.now() - timedelta(days=15)\n        overMonthPublicMemos = Memos.objects.filter(made_by=self.owner,created_at__lt=timePeriod, disclose=\"public\").exclude(tab_name=\"TRASH\")\n        return overMonthPublicMemos\n    \n    def overTimeMemos(self):\n        timePeriod = datetime.now() - timedelta(days=15)\n        overMonthPublicMemos = Memos.objects.filter(made_by=self.owner, created_at__lt=timePeriod)\n        return overMonthPublicMemos\n\n    def PublicCrumbs(self):\n        allPublicCrumbs = []\n        for memo in self.ownerPublicMemos:\n            allPublicCrumbs.append(memo.keyword)\n        sortedPublicCrumbs = list(set(allPublicCrumbs))\n        return sortedPublicCrumbs\n\n    def Crumbs(self):\n        allCrumbs = []\n        for memo in self.ownerMemos:\n            allCrumbs.append(memo.keyword)\n        sortedCrumbs = list(set(allCrumbs))\n        return sortedCrumbs\n\n    def YouTubeId(self, YouTubeUrl):\n        video_id = \"\"\n        if YouTubeUrl:\n            for i in range(len(YouTubeUrl)):               \n                if YouTubeUrl[i] == \"=\":\n                    for s in range(i+1, len(YouTubeUrl)):\n                        video_id = video_id + YouTubeUrl[s]                    \n        return video_id","sub_path":"memo/ownerInfo.py","file_name":"ownerInfo.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"53081040","text":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nfrom main import * # import the main python file with model from the example\nimport time\nimport tensorlayer as tl\n\nload_weights = tl.files.load_npz(name='saved/model.npz')\ntl.files.assign_weights(load_weights, model_)\n\ntop_n = 3\n\ndef respond(input):\n    sentence = inference(input, top_n)\n    response=' '.join(sentence)\n    return response\n\nwhile True:\n    userInput = input(\"Query > \")\n    for i in range(top_n):\n        print(\"bot# \", respond(userInput))\n\n","sub_path":"infer.py","file_name":"infer.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"575203476","text":"\"\"\"\nhttps://www.hackerrank.com/challenges/the-minion-game/problem\n\"\"\"\n\n\ndef minion_game(s):\n    v = 'AEIOU'\n    stuart, kevin, ln = 0, 0, len(s)\n\n    for i in range(ln):\n        if s[i] in v:\n            kevin += (ln - i)\n        else:\n            stuart += (ln - i)\n\n    if stuart > kevin:\n        print('Stuart', stuart)\n    elif stuart == kevin:\n        print('Draw')\n    else:\n        print('Kevin', kevin)\n\n\nif __name__ == '__main__':\n    s = input(\"Enter a string to play the minion game \").upper()\n    minion_game(s)","sub_path":"python/minionGame.py","file_name":"minionGame.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"616871745","text":"count = 0\r\ncountv = 0\r\n\r\nwhile True:\r\n    p = input(\"Digite uma palavra: \")\r\n    count += 1 #contador de palavras\r\n    \r\n    #verificar se começa com vogal\r\n    ini = p[0]\r\n    if ini == 'a' or ini == 'e' or ini == 'i' or ini == 'o' or ini == 'u':\r\n        countv += 1 #contador de vogal\r\n   \r\n    tam = len(p)\r\n    print(\"A palavra {} tem {} letras\".format(p, tam))\r\n    for x in range (tam-1):\r\n        print(p[x], '-', end='') #escreve letras separadas por hifen\r\n   \r\n    vogais = 0\r\n    for y in range (tam-1): #contador de vogais na palavra digitada\r\n        if p[y] == 'a' or p[y] == 'e' or p[y] == 'i' or p[y] == 'o' or p[y] == 'u':\r\n            vogais += 1\r\n    \r\n    #sem tratamento para letra maiuscula e acentos.\r\n    print(p[-1]) # pega a ultima letra\r\n    print(p, \"ao contrário é: \", p[::-1])\r\n    print(\"\\n{} possui {} vogais\".format(p, vogais+1)) \r\n    \r\n    resp = str(input(\"Deseja digitar outra palavra? (S/N): \"))\r\n    if resp.upper() != \"S\":\r\n        break\r\n   \r\n    print(\"\\nTotal de palavras: {}\".format(count))\r\n    print(\"Palavras iniciadas por vogal: {}\".format(countv))","sub_path":"ex009 - Contador de silabas e letras.py","file_name":"ex009 - Contador de silabas e letras.py","file_ext":"py","file_size_in_byte":1100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"18236679","text":"from survey import AnonymousSurvey\nquestion = \"What language did you first learn to speak?\"\nmy_survey = AnonymousSurvey(question)\nmy_survey.show_question()\nprint(\"Enter q to quit \\n\")\nwhile True:\n\tre  = input(\"Enter language \")\n\tif re == 'q':\n\t\tbreak\n\tmy_survey.reponse1(re)\nprint(\"\\nThank you to everyone who participated in the survey!\")\nmy_survey.show_result()","sub_path":"ch_11/language_survey.py","file_name":"language_survey.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"10172687","text":"from __future__ import print_function\nfrom functools import partial\nimport maya.cmds as cmds\n\nclass ButtonWin(object):\n    def __init__(self):\n        self.win = cmds.window()\n        self.layout = cmds.columnLayout(parent=self.win)\n        for x in range(10):\n            cmds.button(label=\"Click Here {}\".foramt(x), parent=self.layout,\n                    command=partial(self.report, x))\n            cmds.showWindow()\n    \n    def report(self, buttonIndex, value):\n        print(\"button {} got {}\".format(buttonIndex, value))\n\nf = ButtonWin()\n","sub_path":"playground/guip.py","file_name":"guip.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"198244492","text":"'''\nModule containing the flask app routes for weblapse\n'''\n\nimport threading\nfrom flask import request, render_template, Response, redirect, flash, Markup, url_for, send_from_directory\n\nfrom weblapse import app, LAPSE_DIR\nfrom weblapse.download import get_batches\nfrom weblapse.camera import start_timelapse_session, shutdown\nfrom weblapse.templates.home import Home\nfrom weblapse.templates.config import Config\nfrom weblapse.templates.download import Download\nfrom werkzeug.exceptions import BadRequestKeyError\n\n@app.route('/')\ndef index():\n    return Home()()\n\n@app.route('/config')\ndef config():\n    return Config()()\n\n@app.route('/download')\ndef download():\n    return Download(get_batches())()\n\n@app.route('/zipped/<batch_name>')\ndef zipped(batch_name):\n    batch = [batch for batch in get_batches() if batch.name == batch_name][0]\n    if batch.zipped:\n        return send_from_directory(LAPSE_DIR, '{}.zip'.format(batch.name))\n    else:\n        batch.to_zip()\n        return redirect(url_for('download'))\n\n\n@app.route('/delete/<batch_name>')\ndef deleter(batch_name):\n    batch = [batch for batch in get_batches() if batch.name == batch_name][0]\n    batch.delete()\n    return redirect(url_for('download'))\n\n@app.route('/start_sequence', methods=['POST'])\ndef start_sequence():\n    try:\n        name = request.form['name']\n        interval = int(request.form['interval'])\n        length = int(request.form['length'])\n        power_mode = True if request.form['power_mode'] == 'True' else False\n        power_off = True if request.form['power_off'] == 'True' else False\n    except BadRequestKeyError:\n        return redirect(url_for('config'))\n    except ValueError:\n        return redirect(url_for('config'))\n         \n    camera = threading.Thread(target=start_timelapse_session,\n                               args=(name, interval, length, power_mode, power_off))\n    try:\n        camera.start()\n    except OSError:\n        return redirect(url_for('config'))\n    \n    return 'Starting timelapse sequence!'\n\n@app.route('/power_off', methods=['post'])\ndef power_off():\n    shutdown()\n","sub_path":"weblapse/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":2089,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"407568955","text":"import os\nimport pandas as pd\nimport re\nimport numpy as np\nfrom input import test\nimport json\n\n\ndef parse_test():\n    dataset = pd.DataFrame(columns=['no', 'author', 'content'])\n\n    for item in test.input:\n        line_data = {\n            'no': item[\"no\"],\n            'author': item['author'],\n            'content': re.sub(r\"\\s+\", \" \", item['content'])\n        }\n\n        dataset = dataset.append(line_data, ignore_index=True)\n\n    return dataset\n\n\ndef parse_federalist_papers(file_path):\n    csv_file_path = os.path.join(os.path.dirname(file_path), \"federalist_papers.csv\")\n    if os.path.exists(csv_file_path):\n        dataset = pd.read_csv(csv_file_path, index_col=0, dtype=np.str)\n        return dataset\n\n    with open(file_path) as f:\n        file_content = f.read()\n\n    dataset = pd.DataFrame(columns=['no', 'author', 'content'])\n\n    regex_paper_start = 'federalist?.\\sno?.\\s\\d{1,2}'\n    regex_paper_end = '\\n\\n\\n\\n\\n|\\*There\\sare\\stwo\\sslightly'\n    regex_paper_author = 'HAMILTON\\sAND\\sMADISON|HAMILTON\\sOR\\sMADISON|HAMILTON|JAY|MADISON'\n\n    paper_start_matches = list(re.finditer(regex_paper_start, file_content, re.IGNORECASE))\n    for paper_start_match in paper_start_matches:\n        # paper\n        paper_start = paper_start_match.span()[0]\n\n        paper_end_match = re.search(regex_paper_end, file_content[paper_start:], re.IGNORECASE)\n        paper_end = paper_start + paper_end_match.span()[0]\n\n        # paper no\n        paper_no_start = paper_start\n        paper_no_end = paper_start_match.span()[1]\n\n        # author\n        paper_author_match = re.search(regex_paper_author, file_content[paper_start:])\n        paper_author_start = paper_start + paper_author_match.span()[0]\n        paper_author_end = paper_start + paper_author_match.span()[1]\n\n        # content\n        paper_content_start = paper_author_end\n        paper_content_end = paper_end\n\n        no = file_content[paper_no_start:paper_no_end]\n        author = file_content[paper_author_start:paper_author_end]\n        content = re.sub(r\"\\s+\", \" \", file_content[paper_content_start:paper_content_end])\n\n        paper_data = {\n            'no': no,\n            'author': author,\n            'content': content\n        }\n\n        dataset = dataset.append(paper_data, ignore_index=True)\n\n    dataset.to_csv(csv_file_path)\n    return dataset\n\n\ndef parse_pan_datasets(path):\n    datasets = {}\n    dirs = [os.path.join(path, x) for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))]\n    for dir_path in dirs:\n        dir_name = os.path.basename(dir_path)\n        csv_file_path = os.path.join(path, dir_name + \".csv\")\n        if os.path.exists(csv_file_path):\n            dataset = pd.read_csv(csv_file_path, index_col=0, dtype=np.str)\n            datasets[dir_name] = dataset\n            continue\n\n        dataset = parse_pan_dataset(dir_path)\n        dataset.to_csv(csv_file_path)\n        datasets[dir_name] = dataset\n\n    return datasets\n\n\ndef parse_pan_dataset(path):\n    dataset = pd.DataFrame(columns=['no', 'author', 'content', 'true-author'])\n    with open(os.path.join(path, \"ground-truth.json\"), encoding=\"utf8\") as f:\n        ground_truth = json.load(f)[\"ground-truth\"]\n\n    dirs = [os.path.join(path, x) for x in os.listdir(path) if os.path.isdir(os.path.join(path, x))]\n    for dir_path in dirs:\n        files = os.listdir(dir_path)\n        for file_name in files:\n            no = re.search('\\d+', file_name).group()\n            author = os.path.basename(dir_path)\n\n            with open(os.path.join(dir_path, file_name), encoding=\"utf8\") as f:\n                content = f.read()\n                content = re.sub(r\"<NAME/>\", \"\", content)\n                content = re.sub(r\"\\s+\", \" \", content)\n\n            if file_name.startswith(\"unknown\"):\n                true_author = list(filter(lambda x: x[\"unknown-text\"] == file_name, ground_truth))[0][\"true-author\"]\n                author_code = int(re.search(\"\\d+\", true_author).group())\n\n                # if code is 0, skip it\n                if author_code == 0:\n                    continue\n            else:\n                true_author = author\n\n            data = {\n                'no': no,\n                'author': author,\n                'content': content,\n                'true-author': true_author\n            }\n\n            dataset = dataset.append(data, ignore_index=True)\n\n    return dataset\n","sub_path":"parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"431095192","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nMAIN CODE FOR RESULTS ANALYSIS\n\nCreated on Sun Apr  7 19:07:49 2019\n\n@author: Luiz Felipe Manke\n\nImprovements to Be Done:\n    -\n\"\"\"\n\n# System Libraries\n\n# Own Libraries\nfrom retrieve_results_simple import simple_retrieve_results\n\n\ndef main(path):\n    \"\"\"Main function to analyze the stock market.\"\"\"\n    simple_retrieve_results(path=path)\n    print('Done!')\n\n\nif __name__ == '__main__':\n    PATH = ('/Users/luizmanke/Google Drive/Stock Market Analysis/Project' +\n            '/results')\n    main(path=PATH)\n","sub_path":"dev/src/main_retrieve_results.py","file_name":"main_retrieve_results.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"96430377","text":"# import the necessary packages\nfrom scipy.spatial import distance as dist\nfrom imutils.video import VideoStream\nfrom imutils import face_utils\nfrom threading import Thread\nimport numpy as np\nimport playsound\nimport argparse\nimport imutils\nimport time\nimport dlib\nimport cv2\nimport numpy as np\nimport os\nimport six.moves.urllib as urllib\nimport sys\nimport tarfile\nimport tensorflow as tf\nimport zipfile\nfrom collections import defaultdict\nfrom io import StringIO\nfrom matplotlib import pyplot as plt\nfrom PIL import Image\nsys.path.append(\"..\")\nfrom utils import label_map_util\nfrom utils import visualization_utils as vis_util\nimport requests\nimport json\n\nMODEL_NAME = 'ssd_mobilenet_v1_coco_2017_11_17'\nMODEL_FILE = MODEL_NAME + '.tar.gz'\nDOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'\n \nPATH_TO_CKPT = MODEL_NAME + '/frozen_inference_graph.pb'\n \nPATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')\n \nNUM_CLASSES = 90\n\nopener = urllib.request.URLopener()\nopener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)\ntar_file = tarfile.open(MODEL_FILE)\nfor file in tar_file.getmembers():\n    file_name = os.path.basename(file.name)\n    if 'frozen_inference_graph.pb' in file_name:\n        tar_file.extract(file, os.getcwd())\n \ndetection_graph = tf.Graph()\nwith detection_graph.as_default():\n    od_graph_def = tf.GraphDef()\n    with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n        serialized_graph = fid.read()\n        od_graph_def.ParseFromString(serialized_graph)\n        tf.import_graph_def(od_graph_def, name='')\n\nlabel_map = label_map_util.load_labelmap(PATH_TO_LABELS)\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\ncategory_index = label_map_util.create_category_index(categories)\n\ngpu_options = tf.GPUOptions(per_process_gpu_memory_fraction = 0.4)\n\n\n\n\ndef sound_alarm(path):\n\t# play an alarm sound\n\tplaysound.playsound(path)\n\ndef eye_aspect_ratio(eye):\n\t# compute the euclidean distances between the two sets of\n\t# vertical eye landmarks (x, y)-coordinates\n\tA = dist.euclidean(eye[1], eye[5])\n\tB = dist.euclidean(eye[2], eye[4])\n\n\t# compute the euclidean distance between the horizontal\n\t# eye landmark (x, y)-coordinates\n\tC = dist.euclidean(eye[0], eye[3])\n\n\t# compute the eye aspect ratio\n\tear = (A + B) / (2.0 * C)\n\n\t# return the eye aspect ratio\n\treturn ear\n \n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\nap.add_argument(\"-a\", \"--alarm\", type=str, default=\"alert.wav\",help=\"path alarm .WAV file\")\nargs = vars(ap.parse_args())\n \n# define two constants, one for the eye aspect ratio to indicate\n# blink and then a second constant for the number of consecutive\n# frames the eye must be below the threshold for to set off the\n# alarm\nEYE_AR_THRESH = 0.3\nEYE_AR_CONSEC_FRAMES = 48\n\n# initialize the frame counter as well as a boolean used to\n# indicate if the alarm is going off\nCOUNTER = 0\nALARM_ON = False\n\n# initialize dlib's face detector (HOG-based) and then create\n# the facial landmark predictor\nprint(\"[INFO] loading facial landmark predictor...\")\ndetector = dlib.get_frontal_face_detector()\npredictor = dlib.shape_predictor('shape_predictor_68_face_landmarks.dat')\n\n# grab the indexes of the facial landmarks for the left and\n# right eye, respectively\n(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"left_eye\"]\n(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS[\"right_eye\"]\n\n# start the video stream thread\nprint(\"[INFO] starting video stream thread...\")\nvideo_capture = cv2.VideoCapture(0)\ntime.sleep(2.0)\n\nwith detection_graph.as_default():\n    with tf.Session(graph=detection_graph, config = tf.ConfigProto(gpu_options = gpu_options)) as sess:\n        # loop over frames from the video stream\n        while True:\n            # grab the frame from the threaded video file stream, resize\n            # it, and convert it to grayscale\n            # channels)\n            ret, frame = video_capture.read()\n            frame = cv2.flip(frame,1)\n            gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n            \n            \n            #Object detection\n            # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n            image_np_expanded = np.expand_dims(frame, axis=0)\n            image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n            # Each box represents a part of the image where a particular object was detected.\n            boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n            # Each score represent how level of confidence for each of the objects.\n            # Score is shown on the result image, together with the class label.\n            scores = detection_graph.get_tensor_by_name('detection_scores:0')\n            classes = detection_graph.get_tensor_by_name('detection_classes:0')\n            num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n            # Actual detection.\n            (boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections],feed_dict={image_tensor: image_np_expanded})\n            # Visualization of the results of a detection.\n            errorTypes = []\n            vis_util.visualize_boxes_and_labels_on_image_array(\n                frame,\n                np.squeeze(boxes),\n                np.squeeze(classes).astype(np.int32),\n                np.squeeze(scores),\n                category_index,\n                use_normalized_coordinates=True,\n                line_thickness=8)\n            for c in classes[0]:\n                object = category_index.get(c)\n                objectName = object.get('name')\n                if objectName == 'cell phone':\n                    cv2.putText(frame, \"DANGEROUS PARCTICE!\", (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n                    errorTypes = ['red']\n                    if not ALARM_ON:\n                            ALARM_ON = True\n\n                            # check to see if an alarm file was supplied,\n                            # and if so, start a thread to have the alarm\n                            # sound played in the background\n                            if args[\"alarm\"] != \"\":\n                                t = Thread(target=sound_alarm,\n                                    args=(args[\"alarm\"],))\n                                t.deamon = True\n                                t.start()\n                else:\n                    ALARM_ON = False\n                    errorTypes = ['green']\n                \n            # detect faces in the grayscale frame\n            rects = detector(gray, 0)\n\n            # loop over the face detections\n            for rect in rects:\n                # determine the facial landmarks for the face region, then\n                # convert the facial landmark (x, y)-coordinates to a NumPy\n                # array\n                shape = predictor(gray, rect)\n                shape = face_utils.shape_to_np(shape)\n\n                # extract the left and right eye coordinates, then use the\n                # coordinates to compute the eye aspect ratio for both eyes\n                leftEye = shape[lStart:lEnd]\n                rightEye = shape[rStart:rEnd]\n                leftEAR = eye_aspect_ratio(leftEye)\n                rightEAR = eye_aspect_ratio(rightEye)\n\n                # average the eye aspect ratio together for both eyes\n                ear = (leftEAR + rightEAR) / 2.0\n\n                # compute the convex hull for the left and right eye, then\n                # visualize each of the eyes\n                leftEyeHull = cv2.convexHull(leftEye)\n                rightEyeHull = cv2.convexHull(rightEye)\n                cv2.drawContours(frame, [leftEyeHull], -1, (0, 255, 0), 1)\n                cv2.drawContours(frame, [rightEyeHull], -1, (0, 255, 0), 1)\n\n                # check to see if the eye aspect ratio is below the blink\n                # threshold, and if so, increment the blink frame counter\n                if ear < EYE_AR_THRESH:\n                    COUNTER += 1\n\n                    # if the eyes were closed for a sufficient number of\n                    # then sound the alarm\n                    if COUNTER >= EYE_AR_CONSEC_FRAMES:\n                        # if the alarm is not on, turn it on\n                        if not ALARM_ON:\n                            ALARM_ON = True\n\n                            # check to see if an alarm file was supplied,\n                            # and if so, start a thread to have the alarm\n                            # sound played in the background\n                            if args[\"alarm\"] != \"\":\n                                t = Thread(target=sound_alarm,\n                                    args=(args[\"alarm\"],))\n                                t.deamon = True\n                                t.start()\n\n                        # draw an alarm on the frame\n                        cv2.putText(frame, \"DROWSINESS ALERT!\", (10, 30),\n                            cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n                        errorTypes = ['red']\n\n                # otherwise, the eye aspect ratio is not below the blink\n                # threshold, so reset the counter and alarm\n                else:\n                    COUNTER = 0\n                    ALARM_ON = False\n                    errorTypes = ['green']\n\n                # draw the computed eye aspect ratio on the frame to help\n                # with debugging and setting the correct eye aspect ratio\n                # thresholds and frame counters\n                cv2.putText(frame, \"Eye Aspect Ratio: {:.2f}\".format(ear), (300, 30),\n                    cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2)\n\n            url = 'http://localhost:3000/get-prediction-data'\n            payload = {'errorTypes': errorTypes}\n            headers = {'content-type': 'application/json'}\n\n            response = requests.post(url, data=json.dumps(payload), headers=headers)\n            try:\n                response_data = response.raise_for_status()\n            except:\n                pass\n\n            # show the frame\n            cv2.imshow(\"Frame\", frame)\n            key = cv2.waitKey(1) & 0xFF\n         \n            # if the `q` key was pressed, break from the loop\n            if key == ord(\"q\"):\n                cv2.destroyAllWindows()\n                break\n\n# do a bit of cleanup\ncv2.destroyAllWindows()\n\n","sub_path":"combined.py","file_name":"combined.py","file_ext":"py","file_size_in_byte":10421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"508076888","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 31 15:20:24 2019\n\n@author: aleks\n\"\"\"\n\nimport lightgbm as lgb\nfrom joblib import dump, load\nfrom lstm_model import DataLoader, Model\nimport json\nfrom attr import dataclass\nfrom sklearn import preprocessing\nimport numpy as np\nfrom sklearn.metrics import mean_absolute_error\nimport matplotlib.pyplot as plt\nimport utils\n\n\n#X = load(\"X_train_1500.joblib\")\n#y = load(\"y_train_1500.joblib\")\nX, y = utils.read_prepared_csv(\"data/train_features_1500_X.csv\", \"data/train_features_1500_Y.csv\")\nX = X.loc[0:50000,:] # <- load a chunk and repeat until its over\ny = y.loc[0:50000,:]\n\nprint(\"Len X: {}\".format(len(X)))\nprint(\"Len y: {}\".format(len(y)))\n#### Load models\nmodels_path = \"classifiers/\"\n\n\n@dataclass\nclass DataSet:\n    X: np.ndarray\n    y: np.ndarray\n    \n\ndef scale(X):\n    \n    # normalize the dataset\n    #scaler = preprocessing.MinMaxScaler(feature_range=(0, 1))\n    scaler = load(\"scaler_lstm.joblib\")\n    X = scaler.transform(X)\n  \n    return X\n\ndef predict_lstm(X_test, y_test, predictions):\n    \n    predictions = predictions.flatten().clip(0,1)\n    \n    mae = round(mean_absolute_error(y_test, predictions), 6)\n    print('\\n{} VAL MAE: {}'.format(\"LSTM\", mae))\n    \n    return predictions\n\nconfigs = json.load(open('config.json', 'r'))\nlstm_path = \"good_lstm.h5\"\nmodel_lstm = Model(configs)\nmodel_lstm.load_h5_model('{}{}'.format(models_path, lstm_path))\n\n# A bit ridicolous but whatever\nX = scale(X)\ntrain = DataSet(X=X, y=y)\ndata = DataLoader(None, train)\n\nY=y\n#del X, y, train\n\nX, y = data.get_test_data(\n        seq_len=configs['data']['sequence_length'],\n        normalise=configs['data']['normalise']\n    )\n\npredictions_lstm = model_lstm.predict_point_by_point(X)\npredictions = 4000*(predictions_lstm-0.4) # MAYBE NOT, plot and see\n\n# save predictions and TARGE dataframe([predictions, Y]) < MAKE SURE THEY ARE FINE\n\n","sub_path":"lstm_prediction.py","file_name":"lstm_prediction.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"155506768","text":"import pynbody\nimport matplotlib.pylab as plt\n\n# load the snapshot and set to physical units\ns = pynbody.load('snapshot_104')\n\n# load the halos\nh = s.halos()\n\n# center on the largest halo and align the disk\n#pynbody.analysis.angmom.faceon(h[1])\n\n# convert all units to something reasonable (kpc, Msol etc)\ns.physical_units()\n\n# create a profile object for the stars (by default this is a 2D profile)\np = pynbody.analysis.profile.Profile(h[1].d,min=.01,max=50,ndim=3)\n\n# make the figure and sub plots\nf, axs = plt.subplots(1,2,figsize=(14,6))\n\naxs[0].plot(p['rbins'],p['density'], 'k')\n#axs[0].semilogy()\naxs[0].set_xlabel('R [kpc]')\naxs[0].set_ylabel(r'$\\rho_{DM}$ [M$_{\\odot}$ kpc$^{-3}$]')\nplt.show()","sub_path":"radial_density_profile.py","file_name":"radial_density_profile.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"211195530","text":"#quicksort.py\r\ndef quick_sort(array):\r\n\t\"\"\"进行快速排序\"\"\"\r\n\tif len(array) < 2:\r\n\t\treturn array\r\n\telse:\r\n\t\tmid = len(array) // 2\r\n\t\tpivot = array[mid]\r\n\t\t#找出小于基准值的数\r\n\t\tless = [i for i in array[:mid] + array[mid+1:] if i <= pivot]\r\n\t\t#找出大于基准值的数\r\n\t\tgreater = [i for i in array[:mid] + array[mid+1:] if i > pivot]\r\n\r\n\t\treturn quick_sort(less) + [pivot] + quick_sort(greater)\r\n\r\nprint(quick_sort([2, 5, 10, 10, 0, 100, 49, 20]))","sub_path":"quicksort.py","file_name":"quicksort.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"610575350","text":"# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import, division, print_function\n\nfrom marshmallow import fields, validate\n\nfrom polyaxon_schemas.base import BaseConfig, BaseSchema\nfrom polyaxon_schemas.ops.build import BuildSchema\nfrom polyaxon_schemas.ops.environments.base import EnvironmentSchema\nfrom polyaxon_schemas.ops.logging import LoggingSchema\n\n\nclass BaseRunSchema(BaseSchema):\n    version = fields.Int(allow_none=None)\n    kind = fields.Str(allow_none=None, validate=validate.Equal('Implement'))\n    logging = fields.Nested(LoggingSchema, allow_none=None)\n    tags = fields.List(fields.Str(), allow_none=None)\n    environment = fields.Nested(EnvironmentSchema, allow_none=True)\n    build = fields.Nested(BuildSchema)\n\n    @staticmethod\n    def schema_config():\n        return BaseRunConfig\n\n\nclass BaseRunConfig(BaseConfig):\n    \"\"\"\n    Build config.\n\n    Args:\n        image: str. The name of the image to use during the build step.\n        build_steps: list(str). The build steps to apply to your docker image.\n            (translate to multiple RUN ...)\n        env_vars: list((str, str)) The environment variable to set on you docker image.\n        nocache: `bool`. To not use cache when building the image.\n        ref: `str`. The commit/branch/treeish to use.\n\n    \"\"\"\n    SCHEMA = BaseRunSchema\n    IDENTIFIER = 'run'\n    REDUCED_ATTRIBUTES = [\n        'kind',\n        'version',\n        'logging',\n        'tags',\n        'environment',\n    ]\n\n    def __init__(self,\n                 kind=None,\n                 version=None,\n                 logging=None,\n                 tags=None,\n                 environment=None,\n                 build=None,\n                 ):\n        self.kind = kind\n        self.version = version\n        self.logging = logging\n        self.tags = tags\n        self.environment = environment\n        self.build = build\n","sub_path":"polyaxon_schemas/ops/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"228849128","text":"#!/home/edilson/anaconda3/bin/python3.8\n\nimport re\nimport sys\nfrom project.dualsimplex import dual_simplex\n\n\nobjective_function = sys.argv[1]\nconstraints = sys.argv[2:]\n\n\ndef get_variables_and_coefficients(eq):\n\n    variables = re.findall(r\"[a-z]+\", eq)\n    coefficients = re.split(r\"[a-z]+\", eq)\n    integer_coefficients = list(map(lambda v: int(v), coefficients[0:-1]))\n    equal = re.split(r\"=|<|>|(<&=)|(>&=)\", eq)\n    if len(equal) > 1:\n        return variables, integer_coefficients, float(equal[-1])\n    return variables, coefficients[0:-1]\n\n\ndef main():\n\n    try:\n        values_objective_function = get_variables_and_coefficients(objective_function)\n        coefficients_objective_function = values_objective_function[1]\n        constraints_coefficients = []\n        constraints_values = []\n        for c in constraints:\n            _, coefficients, result = get_variables_and_coefficients(c)\n            constraints_coefficients.append(coefficients)\n            constraints_values.append(result)\n    except Exception as e:\n        print(\"Revisar las funciones de entrada\")\n        return\n    finally:\n        result = dual_simplex(\n            coefficients_objective_function,\n            constraints_coefficients,\n            constraints_values,\n        )\n        print(result)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"610409515","text":"#\n# Licensed to the Apache Software Foundation (ASF) under one\n# or more contributor license agreements.  See the NOTICE file\n# distributed with this work for additional information\n# regarding copyright ownership.  The ASF licenses this file\n# to you under the Apache License, Version 2.0 (the\n# \"License\"); you may not use this file except in compliance\n# with the License.  You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing,\n# software distributed under the License is distributed on an\n# \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY\n# KIND, either express or implied.  See the License for the\n# specific language governing permissions and limitations\n# under the License.\n#\n\nimport numpy as np\nimport os\nimport base64\nimport pickle\nfrom time import time\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom keras import backend as K\nfrom keras.models import Model\nfrom keras.layers import Dense, GlobalAveragePooling2D\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.optimizers import Adam, SGD\nfrom keras.utils.np_utils import to_categorical\n\nfrom rafiki.model import BaseModel, FixedKnob, FloatKnob, CategoricalKnob, utils\nfrom rafiki.constants import ModelDependency\nfrom rafiki.model.dev import test_model_class\n\n\nclass ResNet(BaseModel):\n    '''\n    Implements ResNet to train image classification model on Cifa10 dataset\n    '''\n    @staticmethod\n    def get_knob_config():\n        return {\n            'epochs': FixedKnob(15),\n            'batch_size': CategoricalKnob([32, 64, 128]),\n            'learning_rate': FloatKnob(0.0001, 0.001, is_exp=True),\n            'momentum': FloatKnob(0.3, 0.6, 0.9),\n            'max_image_size': FixedKnob(224),\n            }\n    def __init__(self, **knobs):\n        super().__init__(**knobs)\n        self._knobs = knobs\n        self.__dict__.update(knobs)\n        self._model = self._build_classifier(self.learning_rate, self.momentum)\n\n  \n    def train(self, dataset_path, **kwargs):\n        ep = self._knobs.get('epochs')\n        bs = self._knobs.get('batch_size')\n        max_image_size = self._knobs.get('max_image_size')\n        \n        dataset = utils.dataset.load_dataset_of_image_files(dataset_path, min_image_size=32, \n                                                            max_image_size=max_image_size, mode='RGB')\n        self._image_size = dataset.image_size\n        (images, classes) = zip(*[(image, image_class) for (image, image_class) in dataset])\n        \n        images = self._prepare_X(images)\n        classes = keras.utils.to_categorical(classes, num_classes=10)\n        \n        self._model.fit(\n                    images, \n                    classes, \n                    epochs=ep, \n                    batch_size=bs\n                )\n\n        # Compute train accuracy\n        (loss, accuracy) = self._model.evaluate(images, classes)\n\n        utils.logger.log('Train loss: {}'.format(loss))\n        utils.logger.log('Train accuracy: {}'.format(accuracy))\n\n        \n    def evaluate (self, dataset_path):\n        max_image_size = self._knobs.get('max_image_size')\n        dataset = utils.dataset.load_dataset_of_image_files(dataset_path, min_image_size=32, \n                                                            max_image_size=max_image_size, mode='RGB')\n        self._image_size = dataset.image_size\n        (images, classes) = zip(*[(image, image_class) for (image, image_class) in dataset])\n        \n        images = self._prepare_X(images)\n        classes = keras.utils.to_categorical(classes, num_classes=10)      \n        \n        # Compute test accuracy\n        (test_loss, test_acc) = self._model.evaluate(images, classes, verbose=1)\n        return test_acc\n\n\n    def predict(self, queries):\n        queries = utils.dataset.transform_images(queries, image_size=self._image_size, mode='RGB')\n        X = self._prepare_X(queries)\n        probs = self._model.predict(X)\n        return probs.tolist()\n\n\n    def destroy(self):\n        pass\n\n\n    def dump_parameters(self):\n        params = {}\n        # Put model parameters\n        model_bytes = pickle.dumps(self._model)\n        model_base64 = base64.b64encode(model_bytes).decode('utf-8')\n        params['model_base64'] = model_base64\n\n        # Put image size\n        params['image_size'] = self._image_size\n\n        return params\n\n        \n    def load_parameters(self, params):\n        # Load model parameters\n        model_base64 = params['model_base64']\n        model_bytes = base64.b64decode(model_base64.encode('utf-8'))\n        self._model = pickle.loads(model_bytes)\n\n        # Load image size\n        self._image_size = params['image_size']\n        \n\n    def _prepare_X(self, images):\n        images = np.asarray(images)\n        images = images/255\n        mean = np.mean(images, axis=0)\n        images -= mean        \n        return images\n\n    \n    def _build_classifier(self, learning_rate, momentum):\n        lr=self._knobs.get('learning_rate')\n        momentum=self._knobs.get('momentum')\n        \n        base_model = ResNet50(weights='imagenet', include_top=False)\n        x = base_model.output\n        x = GlobalAveragePooling2D()(x)\n        x = Dense(1024, activation='relu')(x)     \n        predictions = Dense(10, activation='softmax')(x)\n\n        model = Model(inputs=base_model.input, outputs=predictions)\n\n        for layer in base_model.layers:\n            layer.trainable = False\n\n        model.compile(loss='categorical_crossentropy', metrics=['accuracy'], optimizer='rmsprop')\n\n        for layer in model.layers[:249]:\n            layer.trainable = False\n        for layer in model.layers[249:]:\n            layer.trainable = True\n\n        model.compile(optimizer=SGD(lr=lr, momentum=momentum), metrics=['accuracy'], loss='categorical_crossentropy')\n                       \n        return model\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--train_path', type=str, default='data/cifar10_train.zip', help='Path to train dataset')\n    parser.add_argument('--val_path', type=str, default='data/cifar10_val.zip', help='Path to validation dataset')\n    parser.add_argument('--test_path', type=str, default='data/cifar10_test.zip', help='Path to test dataset')\n    parser.add_argument('--query_path', type=str, default='examples/data/image_classification/cifar10_test_1.png', \n                        help='Path(s) to query image(s), delimited by commas')\n    (args, _) = parser.parse_known_args()\n\n    queries = utils.dataset.load_images(args.query_path.split(',')).tolist()\n    test_model_class(\n        model_file_path=__file__,\n        model_class='ResNet',\n        task='IMAGE_CLASSIFICATION',\n        dependencies={\n                    ModelDependency.KERAS: '2.2.4'        },\n        train_dataset_path=args.train_path,\n        val_dataset_path=args.val_path,\n        test_dataset_path=args.test_path,\n        queries=queries\n    )\n","sub_path":"examples/models/image_classification/ResNet.py","file_name":"ResNet.py","file_ext":"py","file_size_in_byte":6979,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"492515226","text":"\"\"\"Discrete event simulation of queue of tasks for N number of machines.\nBase classes and simple test.\n\"\"\"\n\nfrom __future__ import unicode_literals, print_function, absolute_import\n\nimport random\nimport heapq\n\n# Python Imaging Library (PIL) http://www.pythonware.com/products/pil/\nimport Image, ImageDraw\n\nSEED = 27182\nrandom.seed(SEED)\n\n\nclass Machine(object):\n    \"Machine with capacity to handle one task. To be put into priority queue.\"\n\n    def __init__(self):\n        self.finished = 0.0\n\n    def __cmp__(self, other):\n        return cmp(self.finished, other.finished)\n\n\nclass Task(object):\n    \"One task to be performed. To be put into priority queue.\"\n\n    def __init__(self, arrived, duration):\n        self.t = arrived\n        self.arrived = arrived\n        self.duration = duration\n        self.started = None\n\n    def __cmp__(self, other):\n        return cmp(self.t, other.t)\n\n    def __str__(self):\n        if self.started is None:\n            return \"Task {:.2f}\".format(self.arrived)\n        else:\n            return str(self.started - self.arrived)\n\n    def queue(self, machines):\n        \"Add this task to the queue; assign to machine, compute when finished.\"\n        assert self.started is None\n        machine = heapq.heappop(machines)\n        # The machine is idle; start immediately\n        if machine.finished < self.arrived:\n            self.started = self.arrived\n        # The machine is busy; start when it is done\n        else:\n            self.started = machine.finished\n        self.t = machine.finished = self.started + self.duration\n        heapq.heappush(machines, machine)        \n\n\nclass Simulation(object):\n    \"Perform and record simulation.\"\n\n    def __init__(self, n_machines, utilization):\n        self.n_machines = n_machines\n        self.utilization = utilization\n\n    def run(self, n_tasks, duration, verbose=False):\n        self.n_tasks = n_tasks\n        self.duration = float(duration)\n        self.records = []\n        self.delays = []\n        machines = [Machine() for n in range(self.n_machines)]\n        heapq.heapify(machines)\n        # Create all tasks with arrival times independent of each other\n        capacity = self.utilization * self.n_machines\n        self.tmax = self.n_tasks * self.duration / capacity\n        tasks = [Task(random.uniform(0.0, self.tmax), duration=self.duration)\n                 for i in xrange(self.n_tasks)]\n        heapq.heapify(tasks)\n        while tasks:\n            task = heapq.heappop(tasks)\n            if task.started is None:\n                task.queue(machines)\n                heapq.heappush(tasks, task)\n            else:\n                finished = task.started + task.duration\n                self.tmax = max(self.tmax, finished)\n                self.records.append((task.arrived, task.started, finished))\n                self.delays.append(task.started - task.arrived)\n                if verbose:\n                    print(task)\n        self.delays.sort()\n\n    def write_image(self, filename, ymax=400):\n        YSTART = 20\n        YOFFSET = 15\n        YSTEP = 4\n        img = Image.new('RGB', (int(self.tmax)+1, YSTART+ymax), 'gray')\n        draw = ImageDraw.Draw(img)\n        msg = \"{} machines, {}% utilization\".format(self.n_machines,\n                                                    int(100*self.utilization))\n        draw.text((4, 2), msg)\n        y = YSTART + ymax - YOFFSET\n        draw.text((4, y), 'time')\n        tx, ty = draw.textsize('time')\n        x = 4 + tx + 4\n        y += ty / 2\n        draw.line((x, y, x+30, y, x+30-10, y-5, x+30-10, y+5, x+30, y), 'white')\n        y = YSTART\n        for arrived, started, finished in self.records:\n            y += YSTEP\n            if y >= ymax:\n                y = YSTART + YSTEP\n            # High-light the delay in red\n            if arrived != started:\n                draw.rectangle((arrived, y, started, y+YSTEP), 'red')\n            # Task processing is black\n            draw.rectangle((started, y, finished, y+YSTEP), 'black')\n            # Task arrival time in a separate lane, to show randomness\n            draw.line((arrived, ymax-4, arrived, ymax+YSTEP-4), 'white')\n        img.save(filename, 'PNG')\n\n    def get_mean_delay(self):\n        return sum(self.delays)/len(self.delays)\n    \n    def get_mean_positive_delay(self):\n        positive_delays = [d for d in self.delays if d > 0.0]\n        return sum(positive_delays)/len(positive_delays)\n    \n    def get_median_delay(self):\n        return self.delays[len(self.delays)/2]\n\n    def get_number_delayed(self):\n        return len([d for d in self.delays if d > 0.0])\n\n\nif __name__ == '__main__':\n    N_MACHINES = 1\n    N_TASKS = 100\n    DURATION = 5.0\n    UTILIZATION = 0.5\n    simulation = Simulation(n_machines=N_MACHINES, utilization=UTILIZATION)\n    simulation.run(n_tasks=N_TASKS, duration=DURATION)\n    simulation.write_image(\"queue_{}_{}.png\".format(N_MACHINES,\n                                                    int(100*UTILIZATION)))\n    print('tasks:', simulation.n_tasks)\n    print('duration:', simulation.duration)\n    print('tasks delayed:', simulation.get_number_delayed())\n    print('mean delay:', simulation.get_mean_delay())\n    print('median delay:', simulation.get_median_delay())\n    print('tmax:', simulation.tmax)\n","sub_path":"base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":5256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"39714785","text":"import twisted.internet\n\n\nclass ProxyClientProtocol(twisted.internet.protocol.Protocol):\n\n  def __init__(self, factory):\n    self.factory = factory\n\n  def connectionMade(self):\n    logging.info(\"Client: connected to peer\")\n    self.forward_queue = self.factory.forward_queue\n    self.forward_queue.get().addCallback(self.serverDataReceived)\n\n  def serverDataReceived(self, data):\n    if data is False:\n      self.forward_queue = None\n      logging.warning(\"Client: disconnecting from peer\")\n      self.factory.continueTrying = False\n      self.transport.loseConnection()\n    elif self.forward_queue:\n      logging.info(\"Client: writing %d bytes to peer\" % len(data))\n      self.transport.write(data)\n      self.forward_queue.get().addCallback(self.serverDataReceived)\n    else:\n      self.factory.forward_queue.put(data)\n\n  def dataReceived(self, data):\n    logging.info(\"Client: %d bytes received from peer\" % len(data))\n    self.factory.backward_queue.put(data)\n\n  def connectionLost(self, why):\n    if self.forward_queue:\n      self.forward_queue = None\n      logging.warning(\"Client: peer disconnected unexpectedly\")\n\n\nclass ClientFactory(twisted.internet.protocol.ReconnectingClientFactory):\n\n  maxDelay = 10\n  continueTrying = True\n\n  def __init__(self, backward_queue, forward_queue):\n    self.backward_queue = backward_queue\n    self.forward_queue  = forward_queue\n\n  def buildProtocol(self, address):\n    return ProxyClientProtocol(self)\n\n","sub_path":"ocsdaemon/proxy/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"321876580","text":"\nimport re\nimport requests\n\nnothing = '63579'\nwhile True:\n    response = requests.get(f'http://www.pythonchallenge.com/pc/def/linkedlist.php?nothing={nothing}')\n    print(response.text)\n    text = re.findall(r'and the next nothing is (\\d+)', response.text)[0]\n    if text is None:\n        print('ERrro')\n        break\n    else:\n        nothing = text\n    print(nothing)\n\n","sub_path":"level-4.py","file_name":"level-4.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"527714619","text":"# -*- coding: utf-8 -*-\nfrom openerp.osv import osv,fields\nfrom pytils import numeral\n\nclass res_partner(osv.Model):\n    _inherit = \"res.partner\"\n    def _get_default_bank_id(self, cr, uid, ids, field_name, arg, context=None):\n        res = {}\n        for row in self.browse(cr, uid, ids, context):\n            res[row.id] = row.bank_ids and row.bank_ids[0].id or None\n        return res\n\n    _columns = {\n        'represented_by':fields.char('Represented by', size=256, help='String for contracts'),\n        'default_bank_id':fields.function(_get_default_bank_id, type='many2one', obj='res.partner.bank')\n        }\n\ndef _get_amount_in_words(self, cr, uid, ids, field_name, arg, context=None):\n    res = {}\n\n    for row in self.browse(cr, uid, ids, context):\n        rubles = numeral.rubles(int(row.amount_total))\n        copek_num = round(row.amount_total - int(row.amount_total))\n        copek = numeral.choose_plural(int(copek_num), (u\"копейка\", u\"копейки\", u\"копеек\"))\n        res[row.id] = (\"%s %02d %s\")%(rubles, copek_num, copek)\n\n    return res\n\nclass sale_order(osv.Model):\n    _inherit = 'sale.order'\n\n    _columns = {\n        'date_acceptance': fields.date('Acceptance act date', required=False, readonly=False),\n        'amount_total_in_words': fields.function(_get_amount_in_words, string='Amount in words', type='char'),\n\n        }\n    def _prepare_invoice(self, cr, uid, order, lines, context=None):\n        invoice_vals = super(sale_order, self)._prepare_invoice(cr, uid, order, lines, context)\n        invoice_vals['date_origin'] = order.date_order\n        return invoice_vals\n\nclass account_invoice(osv.Model):\n    _inherit = 'account.invoice'\n\n    def _get_partner_bank_id(self, cr, uid, context=None):\n        company_id = self.pool.get('res.company')._company_default_get(cr, uid, 'account.invoice', context=context)\n        company_obj = self.pool.get('res.company').browse(cr, uid, company_id, context=context)\n        if not company_obj:\n            return None\n        return company_obj.partner_id.default_bank_id.id\n\n    _columns = {\n        'amount_total_in_words': fields.function(_get_amount_in_words, string='Amount in words', type='char'),\n        'date_origin': fields.date('Origin date', required=False, readonly=True),\n        }\n    _defaults = {\n        'partner_bank_id': _get_partner_bank_id,\n        }\n\n#class account_invoice_line(osv.Model):\n#    _inherit = 'account.invoice.line'\n#    def _get_synonym(self, cr, uid, ids, field_name, arg, context):\n#        res = {}\n#        for row in self.browse(cr, uid, ids, context):\n#            res[row.id] = {}\n#            res[row.id]['product_uom_qty'] = row.quantity\n#            res[row.id]['product_uom'] = row.uos_id\n#        return res\n#\n#    _columns = {\n#        'product_uom_qty':fields.function(_get_synonym, multi='synonym', type='float'),\n#        'product_uom':fields.function(_get_synonym, multi='synonym', type='many2one'),\n#        }\n","sub_path":"sale_report_ru/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"462071959","text":"import requests\nimport os\nimport bs4\nimport re\nimport json\nimport sys\nfrom lxml import html\n\nuser_agent = {\n    'User-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.61 Safari/537.36'}\nurl = 'https://www.amazon.com.au/FX505DT-IPS-Type-R5-3550H-GeForce-FX505DT-AH51/dp/B07VBK4SYS'\n\n# Download the Amazon product page\nprint('Downloading page %s...' % url)\npage = requests.get(url, headers=user_agent)\n\ntree = html.fromstring(page.content)\n# Create images folder based on page title\nt = tree.find(\".//title\").text\nos.makedirs('images/'+t, exist_ok=True)\n\nhiRes = str(tree.xpath('//script[contains(., \"ImageBlockATF\")]/text()'))\nif hiRes == []:\n    print('Could not find script data containing hires image urls')\nelse:\n    hiResImages = re.findall('\\\"hiRes\\\":\\\"(https.*?\\.jpg)\\\"', hiRes)\n\nfor imgUrl in hiResImages:\n    # Download the image.\n    print('Downloading image %s...' % (imgUrl))\n    res = requests.get(imgUrl)\n    res.raise_for_status()\n\n    # Save the image to ./product-images\n    imageFile = open(os.path.join('images/'+t,\n                                  os.path.basename(imgUrl)), 'wb')\n\n    for chunk in res.iter_content(100000):\n        imageFile.write(chunk)\n    imageFile.close()\n\nprint('Done.')\n","sub_path":"scrape-amazon-images.py","file_name":"scrape-amazon-images.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"374117332","text":"# -*- encoding: UTF-8 -*-\n\n__author__ = 'faide'\nfrom openerp.osv import fields\nfrom openerp.osv import osv\nimport datetime\nfrom openerp.tools.misc import DEFAULT_SERVER_DATETIME_FORMAT\n\n\ndef default_dt(*args, **kwargs):\n    return datetime.datetime.strftime(\n        datetime.datetime.utcnow(), DEFAULT_SERVER_DATETIME_FORMAT\n    )\n\n\nclass LoginLogs(osv.Model):\n    _name = \"login_audit\"\n    _description = \"Login audit trail\"\n    _rec_name = \"login\"\n\n    _order = \"login_dt desc\"\n\n    _columns = {\n        \"login\": fields.char(\n            \"login\", size=64, required=True,\n            help=\"The login name that performed a login operation\"\n        ),\n        \"login_dt\": fields.datetime(\n            \"login_dt\", help=\"Datetime of login operation\"\n        ),\n    }\n\n    _defaults = {\n        \"login_dt\": default_dt\n    }\n","sub_path":"addons/base_res_users_streamline/loginaudit.py","file_name":"loginaudit.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"379079843","text":"import pandas as pd\nimport numpy as np\nimport sys\n\n#includes itself as a child\ndef is_child_present(grid, phecode, indiv_pres, key, anc_ch):\n    children = list(anc_ch.loc[anc_ch.phecode_ancestor==phecode, 'phecode_child'])\n    for c in children:\n        if indiv_pres[grid][key[c]]:\n            return True\n    return False\n\ndef rollup(phe_path, child_anc_path):\n    phecodes = pd.read_csv(phe_path, dtype=str)\n    child_anc = pd.read_csv(child_anc_path, dtype=str)\n\n    prev_dict = dict()\n    phe_key = dict()\n    individual_presence = dict()\n    #each individual has a list of counts, corresponding in order to the unique_codes list\n    #increase counts according to whether a child is present\n    unique_codes = list(phecodes.PHECODE.unique())\n    for i in range(len(unique_codes)):\n        phe_key[unique_codes[i]]=i\n    for indv in phecodes.GRID.unique():\n        #intialize len(unique_codes) list containing false for all GRIDs\n        individual_presence[indv] = [False]*len(unique_codes)\n    for i in range(phecodes.shape[0]):\n        #check if child is present for given phecode and individual\n        #if it is not, set it to True for that individual and increment the count in the prev_dict\n        #also increment all the parents of this code in the prev dict\n        if not is_child_present(phecodes.iloc[i].GRID, phecodes.iloc[i].PHECODE, individual_presence, phe_key, child_anc):\n            individual_presence[phecodes.iloc[i].GRID][phe_key[phecodes.iloc[i].PHECODE]]=True\n            #for all parents of the phecode, increase count by 1\n            parents = list(child_anc.loc[child_anc.phecode_child==phecodes.iloc[i].PHECODE, 'phecode_ancestor'])\n            for code in parents:\n                if code in prev_dict:\n                    prev_dict[code]+=1\n                else:\n                    prev_dict[code] = 1\n        else:\n            #It is, so this individual has already been counted for this code\n            #This is because any time a child is found, each of it's parents are incremented\n            #Therefore this individual has already been counted in the prevalence for this code, since a child of this code has been counted\n            individual_presence[phecodes.iloc[i].GRID][phe_key[phecodes.iloc[i].PHECODE]] = True\n    return prev_dict, phecodes.GRID.unique().shape[0]\n\ndef rollup_file(phe_path, child_anc_path):\n    phe_file = open(phe_path, 'r')\n    phedf = pd.read_Csv(phe_path, dtype=str)\n    child_anc = pd.read_csv(child_anc_path, dtype=str)\n\n    prev_dict = dict()\n    phe_key = dict()\n    individual_presence = dict()\n    unique_codes = list(phedf.PHECODE.unique())\n    print('initialized dicts')\n    for i in range(len(unique_codes)):\n        phe_key[unique_codes[i]]=i\n    print('initialized key')\n    for indv in phedf.GRID.unique():\n        individual_presence[indv] = [False]*len(unique_codes)\n    print('initialized indv presence')\n    for line in phe_file:\n        #split line \n        grid, phecode, _ = line.strip('\\n').split(',')\n        if not is_child_present(grid, phecode, individual_presence, phe_key, child_anc):\n            individual_presence[grid][phe_key[phecode]]=True\n            parents = list(child_anc.loc[child_anc.phecode_child==phecode, 'phecode_ancestor'])\n            for code in parents:\n                if code in prev_dict:\n                    prev_dict[code] += 1\n                else:\n                    prev_dict[code] = 1\n        else:\n            individual_presence[grid][phe_key[phecode]] = True\n    print('returning prevalence results')\n    return prev_dict, phedf.GRID.unique().shape[0]\n\ndef create_weights(prev_dict, total_pop):\n    prev = pd.DataFrame()\n    prev['codes'] = prev_dict.keys()\n    prev['counts'] = prev_dict.values()\n    prev['inv_prevalence'] = total_pop/prev['counts']\n    #np.log is ln\n    prev['ln'] = np.log(prev['inv_prevalence'])\n    return prev\n\ndef run_rollup_file():\n    prev_dict, total_pop = rollup_file(sys.argv[1], sys.argv[2])\n    prev_df = create_weights(prev_dict, total_pop)\n    prev_df.to_csv(sys.argv[3])\n\n'''\nargs:\nsys.argv[1] = path to phecodes (frequent visitors)\nsys.argv[2] = path to ancestor to child key for phecodes\nsys.argv[3] = path to output\n'''\nif __name__=='__main__':\n    prev_dict, total_pop = rollup(sys.argv[1], sys.argv[2])\n    prev_df = create_weights(prev_dict, total_pop)\n    prev_df.to_csv(sys.argv[3])\n","sub_path":"scripts/full_pipeline/roll_up_prevalences.py","file_name":"roll_up_prevalences.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"405612210","text":"'''\nBuilds a model (TO BE DETERMINED).\n\nAuthors:\nPaul Hendricks\n\nDate:\n2015-12-15\n\ninputs:\ntrain.h5\ntest.h5\nsample_submission.csv\n\noutputs:\n01-model.h5\n'''\n\n# Load libraries\nimport pandas as pd\n\n# Load data\nprepped_path = './data/prepped/'\ntrain = pd.read_hdf(prepped_path + 'train.h5', 'table')\ntest = pd.read_hdf(prepped_path + 'test.h5', 'table')\nsubmission = pd.read_csv('./data/submission/sample_submission.csv')\n\n# Write data out\npd.to_csv('./data/models/01-model.h5')\n","sub_path":"munge/04-model/03-tier/01-model-weighting.py","file_name":"01-model-weighting.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"415107625","text":"import pkg_resources\nfrom pkg_resources import DistributionNotFound, VersionConflict\nfrom platform import python_version\n\ndef check_python_version():\n    print('Your python version is ', python_version())\n    assert python_version()[:3] == '3.7', 'Make sure you use python version >=3.7'\n\ndef check_env_setup():\n    dependencies = open(\"requirements.txt\").readlines()\n    try:\n        pkg_resources.require(dependencies)\n        print(\"✅ ALL GOOD\")\n    except DistributionNotFound as e:\n        print(\"⚠️ Library is missing\")\n        print(e)\n    except VersionConflict as e:\n        print(\"⚠️ Library version conflict\")\n        print(e)\n    except Exception as e:\n        print(\"⚠️ Something went wrong\")\n        print(e)\n\nif __name__ == '__main__':\n    check_python_version()\n    check_env_setup()","sub_path":"Chap8_Tree/helpers/verify_config.py","file_name":"verify_config.py","file_ext":"py","file_size_in_byte":813,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"551057256","text":"\n# Used code from philk111 to help with using BeautifulSoup\n# Link below:\n# https://github.com/philk111/Nasdaq-options-scraper-with-Python/blob/master/options_scrape.py\n\nimport json\nimport requests\nfrom bs4 import BeautifulSoup as soup\nimport pandas as pd\nimport time\n\n\n# Create empyt data frame to hold all the data\n\n\ncolumns = ['date', 'Symbol', 'Price', 'Strike', 'last', 'gain', 'bid', 'bidSize', 'ask', 'askSize', 'volume', 'interest', 'state', 'StrikePercent']\n\ndf = pd.DataFrame(columns=columns)\n\n\n# Create a list of symbols we want to get data on\n# List is mostly based of the 2016 most traded options by volume from CBOE in their\n# '2016 Market Statistics' report\n\n\nsymbols = [\"AAPL\",\"BAC\",\"FB\",\"NFLX\",\"TWTR\",\"AMZN\",\"MSFT\",\"TSLA\",\"BABA\",\"C\",\"GE\",\"FCX\",\"PG\",\"MU\",\"INTC\",\"JPM\",\"XOM\",\"WFC\",\"T\",\"CHK\",\"X\",\"CSCO\",\"GILD\",\"F\",\"PFE\",\"DIS\",\"CVX\",\"GOLD\",\"AAL\",\"VZ\",\"NVDA\",\"SBUX\",\"GM\",\"AMD\",\"DAL\",\"WMT\",\"BMY\",\"CAT\",\"GPRO\",\"AA\",\"ET\",\"JCP\",\"RIG\",\"IBM\",\"NKE\",\"GOOGL\",\"GS\",\"PBR\",\"HD\",\"WYNN\",\"KMI\",\"QCOM\",\"CRM\",\"MCD\",\"COP\",\"V\",\"DB\",\"BIDU\",\"KO\",\"ORCL\",\"GOOG\",\"SRPT\",\"FIT\",\"CLF\",\"WLL\",\"CELG\",\"JNJ\",\"BA\",\"MS\",\"HAL\",\"VALE\",\"MRK\",\"LVS\",\"CMG\",\"BAX\",\"MRO\",\"TEVA\",\"SUN\",\"JD\",\"MDLZ\",\"ATVI\",\"SLB\",\"NEM\",\"UPS\",\"AGN\",\"ABBV\",\"MGM\", \"RAD\",\"TGT\",\"GG\",\"PYPL\",\"M\",\"EBAY\",\"BX\",\"BP\", \"SQ\", \"SHOP\"]\n\n\n# Loop through each Symbol and add date to dataframe\n\n\nfor s in range(0, len(symbols)):\n    \n    print(\"Starting \" + symbols[s])\n    print(s)\n    url = 'https://www.nasdaq.com/symbol/' + symbols[s] + '/option-chain?callput=call'\n    response = requests.get(url)\n\n    page_soup = soup(response.content, \"html.parser\")\n\n\n    quote = page_soup.find(\"div\", {\"id\": \"qwidget_lastsale\"})\n\n    quote = quote.text.strip('$')\n\n    containers = page_soup.findAll(\"div\", {\"class\": \"OptionsChain-chart borderAll thin\"})\n\n    for container in containers:\n        table_container = container.findAll(\"tr\")\n\n    table_container = container.findAll(\"tr\")\n\n    rows = []\n    for row in table_container:\n\n        rows.append(row.findAll(\"td\"))\n\n\n    for i in range(1, len(rows)):\n\n        date = rows[i][0].text\n        symbol = rows[i][1].text\n        price = float(quote)\n        strike = float(rows[i][2].text)\n        \n        if rows[i][3].text == '':\n            last = .01\n        else:\n            last = float(rows[i][3].text)\n        \n        if rows[i][5].text == '':\n            bid = .01\n        else:\n            bid = float(rows[i][5].text)\n        bidSize = int(rows[i][6].text)\n\n        if rows[i][7].text == '':\n            ask = .01\n        else:\n            ask = float(rows[i][7].text)\n\n        askSize = int(rows[i][8].text)\n        volume = int(rows[i][9].text)\n\n        if rows[i][10].text == '':\n            interest = 0\n        else:\n            interest = int(rows[i][10].text)\n\n        if price < strike:\n            state = \"OTM\"\n            gain = bid / price * 100\n        elif price == strike:\n            state = \"ATM\"\n            gain = bid / price *100\n        else:\n            state = \"ITM\"\n            gain =  (bid - (price - strike)) / price * 100\n            \n        StrikePercent = price / strike * 100\n\n        df.at[len(df)] = [date, symbol, price, strike, last, gain, bid, bidSize, ask, askSize, volume, interest, state, StrikePercent]\n        \n    time.sleep(2)  # Using 2 second sleep as to not make constant requests against the website\n    \n    print(\"Finished \" + symbols[s])\n    \n\n\n# Write dataframe to file using the time as part of file name\n\n\nfileName = 'Options-' + time.ctime().replace('  ', ' ').replace(' ', '-') + '.csv'\ndf.to_csv(fileName)\n\n","sub_path":"options.py","file_name":"options.py","file_ext":"py","file_size_in_byte":3556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"188522269","text":"import automateichon\r\nimport automataEsq\r\nimport nfaToDfa\r\nimport utils\r\nfrom graphviz import Digraph\r\n\r\nVALIDOS = list(\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890\")\r\nOPERADORES = [\"*\", \"|\", \"?\", \"^\", \")\"]\r\nOPERADORESUNI = [\"*\", \"?\"]\r\nCOUNT = [10]\r\nEPSILON = \"ε\"\r\n\r\n\r\nclass Nodo:\r\n    def __init__(self, data):\r\n        self.right = None\r\n        self.left = None\r\n        self.position = None\r\n        self.data = data\r\n\r\n\r\ndef print2DUtil(root, space):\r\n  \r\n    # Base case  \r\n    if (root == None) : \r\n        return\r\n  \r\n    # Increase distance between levels  \r\n    space += COUNT[0] \r\n  \r\n    # Process right child first  \r\n    print2DUtil(root.right, space)  \r\n  \r\n    # Print current node after space  \r\n    # count  \r\n    print()  \r\n    for _ in range(COUNT[0], space): \r\n        print(end = \" \")\r\n    print(root.data)  \r\n  \r\n    # Process left child  \r\n    print2DUtil(root.left, space)  \r\n  \r\n# Wrapper over print2DUtil()  \r\ndef print2D(root) : \r\n      \r\n    # space=[0] \r\n    # Pass initial space count as 0  \r\n    print2DUtil(root, 0)\r\n\r\ndef arbolillo(expresion):\r\n    vals = []\r\n    ops = []\r\n    i = 0\r\n\r\n    while i < len(expresion):\r\n        if expresion[i] == ' ':\r\n            i += 1\r\n            continue\r\n        if expresion[i] == '(':\r\n            ops.append(expresion[i])  \r\n        elif expresion[i] not in OPERADORES:\r\n            val = \"\"\r\n            while(i<len(expresion) and expresion[i] not in OPERADORES):\r\n                val += expresion[i]\r\n                i += 1\r\n            i -= 1\r\n            val = Nodo(val)\r\n            vals.append(val)\r\n\r\n        elif expresion[i] == ')':\r\n            while len(ops) != 0 and ops[-1] != '(':\r\n                val2 = vals.pop()\r\n                val1 = vals.pop()\r\n                op = ops.pop()\r\n                nodito = Nodo(op)\r\n                nodito.left = val1\r\n                nodito.right = val2\r\n                vals.append(nodito)\r\n            ops.pop()\r\n\r\n        else:\r\n            if(expresion[i] in OPERADORESUNI):\r\n                ops.append(expresion[i])\r\n                op = ops.pop()\r\n                val = vals.pop()\r\n                nodito = Nodo(op)\r\n                nodito.right = None\r\n                nodito.left = val\r\n                vals.append(nodito)\r\n            else:\r\n                while (len(ops) != 0 and ops[-1] != '('):\r\n                    op = ops.pop()\r\n                    val2 = vals.pop()\r\n                    val1 = vals.pop()\r\n                    nodito = Nodo(op)\r\n                    nodito.left = val1\r\n                    nodito.right = val2\r\n                    vals.append(nodito)\r\n                ops.append(expresion[i])\r\n\r\n        i += 1\r\n    while len(ops) != 0:\r\n        val2 = vals.pop()\r\n        val1 = vals.pop()\r\n        op = ops.pop()\r\n        nodito = Nodo(op)\r\n        nodito.left = val1\r\n        nodito.right = val2\r\n        vals.append(nodito)\r\n        if (len(vals) == 1):\r\n            return vals[-1]\r\n    return vals[-1]\r\n\r\ndef create_automataRepresentation(arbolito, expresion):\r\n    auto = automataEsq.Machine(expresion)\r\n    print2D(arbolito)\r\n    automaton = automateichon.automataBuilder(arbolito, auto)\r\n    auto.states[-1].accept = True\r\n    dfatext = open(\"nfa.txt\",\"w\")\r\n    for state in auto.states:\r\n        identidad = \"estado: \" + str(state.id) + \"\\n\"\r\n        aceptacion = \"Aceptacion: \"+str(state.accept) + \"\\n\" \r\n        dfatext.write(identidad)\r\n        dfatext.write(aceptacion)\r\n        for trap in state.transitions:\r\n            if trap.simbolo == EPSILON:\r\n                z = \"E\"\r\n            else:\r\n                z = trap.simbolo\r\n            texto = \"Transicion: \" + str(trap.destino) + \" \" + \"Con: \" + z + \"\\n\"\r\n            dfatext.write(texto)\r\n    dfatext.close\r\n  \r\n    dot = Digraph()\r\n    for state in auto.states:\r\n        if state.accept == True:\r\n            dot.node(str(state.id), str(state.id), shape = \"doublecircle\")\r\n        for t in state.transitions:\r\n            dot.edge(str(state.id), str(t.destino), str(t.simbolo))\r\n    print(dot.source)\r\n    dot.render('test-output/round-table.pdf', view=True)\r\n    return automaton\r\n\r\n\r\ndef matchingNfa(arbolito, expresion, textillo):\r\n    auto = automataEsq.Machine(expresion)\r\n    automateichon.automataBuilder(arbolito, auto)\r\n    aceptados = []\r\n    for r in expresion:\r\n        if r not in OPERADORES:\r\n            if r != '(':\r\n                aceptados.append(r)\r\n    for x in textillo:\r\n        if x not in aceptados:\r\n            print(\"no aceptado\")\r\n            return 0\r\n    opciones = list(textillo)\r\n    estaditos = [0]\r\n    estaditos = utils.cerraduraEpsilon(auto, estaditos)\r\n    auto.states[-1].accept = True\r\n    i = 0\r\n    while True:\r\n        tempstates = []\r\n        for estado in estaditos:\r\n            for trans in auto.states[estado].transitions:\r\n                if trans.simbolo == opciones[i] and trans.destino not in tempstates:\r\n                    tempstates.append(trans.destino)\r\n        i += 1\r\n        tempstates = utils.cerraduraEpsilon(auto, tempstates)\r\n        if not tempstates and expresion == EPSILON:\r\n            break\r\n        estaditos = tempstates.copy()\r\n        if i > len(opciones)-1:\r\n            break\r\n    for x in estaditos:\r\n        if auto.states[x].accept:\r\n            return print(\"Aceptado\")\r\n    return print(\"No Aceptado\")\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    print(\"Ingrese la expresion Regular con el simbolo '^' para la concatenacion: \")\r\n    exp = input()\r\n    print(\"Ingrese la cadena a probar:\")\r\n    cadena = input()\r\n    ans = arbolillo(exp)\r\n    create_automataRepresentation(ans, exp)\r\n    nfaToDfa.nfaToDfa(ans, exp)","sub_path":"Proyecto1/Proyecto1/nfa.py","file_name":"nfa.py","file_ext":"py","file_size_in_byte":5640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"290981472","text":"# coding:utf8\nfrom util.config_utils import *\n\n\ndef warn(**kwargs):\n    return {'success': False, 'message': '方法不存在', 'info': 'warn'}\n\n\ndef sync_module_list(**kwargs):\n    user = kwargs.get('user', None)\n    result = []\n    res = {'success': False, 'message': '操作失败', 'user': user, 'data': result}\n    try:\n        result.extend(file_module_list('web', user))\n        result.extend(file_module_list('app', user))\n        res.update({'success': True, 'message': '操作成功', 'user': user, 'data': result})\n    except Exception as e:\n        res.update({'success': False, 'message': '操作失败', 'data': result, 'info': str(e)})\n    return res\n","sub_path":"util/control_operation.py","file_name":"control_operation.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"421172353","text":"from django.shortcuts import render\n\nfrom rest_framework import generics, permissions\nfrom rest_framework.response import Response\n\nfrom auth.serializers import AuthSerializer, UserSerializer\n\n\nclass RegisterApi(generics.GenericAPIView):\n    permission_classes = [permissions.AllowAny]\n    serializer_class = AuthSerializer\n    \n    def post(self, request, *args,  **kwargs):\n        serializer = self.get_serializer(data=request.data)\n        serializer.is_valid(raise_exception=True)\n        user = serializer.save()\n        return Response({\n            \"user\": UserSerializer(\n                user,\n                context=self.get_serializer_context()\n            ).data,\n            \"message\": \"User Created Successfully.  Now perform Login to get your token\",\n        })","sub_path":"medium/auth/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"431484047","text":"from django.core.management.commands.makemessages import Command as MakeMessagesCommand\n\nfrom django_mako_plus.util import get_dmp_instance, get_dmp_app_configs\n\nimport os\nimport os.path\n\n\nclass Command(MakeMessagesCommand):\n    help = \"Makes messages for Mako templates.  The native makemessages in Django doesn't understand Mako files, so this command compiles all your templates so it can find the compiled .py versions of the templates.\"\n\n\n    def add_arguments(self, parser):\n        # must call super because not directly subclassing BaseCommand\n        super(Command, self).add_arguments(parser)\n        # add our additional command\n        parser.add_argument('--extra-gettext-option', default=[], dest='extra_gettext_option', action='append', help=\"Add an additional option to be passed to gettext. Ex: --extra-gettext-option='--keyword=mytrans'\")\n\n\n    def handle(self, *args, **options):\n        # go through each dmp_enabled app and compile its mako templates\n        for app_config in get_dmp_app_configs():\n            self.compile_mako_files(app_config)\n\n        # add any extra xgettext_options (the regular makemessages doesn't do this, and I need to include other aliases like _(), _z(), etc.\n        for opt in options.get('extra_gettext_option', []):\n            self.xgettext_options.append(opt)\n\n        # call the superclass command\n        return super(Command, self).handle(*args, **options)\n\n\n    def compile_mako_files(self, app_config):\n        '''Compiles the Mako templates within the apps of this system'''\n        # go through the files in the templates, scripts, and styles directories\n        moddir = os.path.dirname(app_config.module.__file__)\n        for subdir_name in ('templates', 'scripts', 'styles'):\n            subdir = os.path.join(moddir, subdir_name)\n            if os.path.exists(subdir):\n                for filename in os.listdir(subdir):\n                    fname, ext = os.path.splitext(filename)\n                    if ext.lower() in ( '.htm', '.html', '.jsm', '.cssm' ):\n                        # create the template object, which creates the compiled .py file\n                        renderer = get_dmp_instance().get_template_loader(app_config.name, subdir_name)\n                        renderer.get_template(filename)\n","sub_path":"django_mako_plus/management/commands/dmp_makemessages.py","file_name":"dmp_makemessages.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"153927822","text":"import socket\n\nHOST='127.0.0.1'\nPORT=3333\ns=socket.socket(socket.AF_INET, socket.SOCK_STREAM)\ns.connect((HOST,PORT))\nwhile True:\n    data=input(\"Enter your input:\")\n    s.sendall(data.encode())\n    data=s.recv(1024)\n    print(data.decode())\ns.close()\n","sub_path":"14301083/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"592317012","text":"# This program helps to get the intensity distribution by displaying a\n# rectangle on the DMD, optionally with a grating, moves it over the entire\n# surface of the DMD, and records the intensity that is\n# registered by the camera.\n\nimport pyglet\nimport numpy as np\nimport Config\nimport CommFun\nfrom os import listdir\n#from os.path import isfile\n#from time import sleep\nimport matplotlib.pyplot as plt\n\n# get the configuration parameters from the Config file:\nsquareSizeX = Config.squareSizeX\nsquareSizeY = Config.squareSizeY\nsquareStepX = Config.squareStepX\nsquareStepY = Config.squareStepY\nkList = Config.kList_calib_Intensity\ndelay = Config.delay_calib_Intensity\ntestDisplay = Config.testDisplay_calib_Intensity\nwinSizeX, winSizeY = Config.winSizeX, Config.winSizeY\ndirname = Config.dirname_calib_Intensity\nDoCalibration = Config.DoCalibration\nDoPlotting = Config.DoPlotting\n\n\n# number of times the square is displaced along the x, and along the y-axis\nsquareNumX = (winSizeX - squareSizeX) // squareStepX + 1\nsquareNumY = (winSizeY - squareSizeY) // squareStepY + 1\nrefX, refY = ((winSizeX - squareSizeX) // 2, (winSizeY - squareSizeY) // 2)\n\nif DoCalibration:\n    \n    #Initializing Pyglet session\n    platform = pyglet.window.get_platform()\n    display = platform.get_default_display()\n    screens = display.get_screens()             #List of available screens\n    \n    # depending on the value of testDisplay the patterns will be displayed in a normal window, or on a separate screen (the DMD):\n    if testDisplay :\n        window = pyglet.window.Window(Config.winSizeX, Config.winSizeY, fullscreen = False, screen = screens[0], vsync = True)\n    else :\n        window = pyglet.window.Window(fullscreen = True, screen = screens[1], vsync = True)\n        \n    pyglet.gl.glClearColor(0, 0, 0, 255)           #Setting background color\n    \n\n    \n    \n    @window.event\n    def on_close() :\n        ser.close()\n    \n    @window.event\n    def on_draw() :\n        global probeX, probeY\n        window.clear()\n        CommFun.drawRegion(probeX, probeY, squareSizeX, squareSizeY, screenPic)\n    \n    def display() :\n        pyglet.clock.tick()\n        window.switch_to()\n        window.dispatch_events()\n        window.dispatch_event('on_draw')\n        window.flip()\n    \n    \n    display()\n    print(\"time to start recording\")\n    ser = CommFun.serialInit('COM3')\n    \n    #generation of the grating to be used\n    kx, ky = kList[0]\n    if (0, 0) == (kx, ky) : # completely white picture\n        screenPic = pyglet.image.create(winSizeX, winSizeY, pyglet.image.SolidColorImagePattern((255,255,255,255)))\n    else : # grating with grating vectors kx and ky\n        y, x = np.mgrid[0:winSizeY, 0:winSizeX]\n        array = (lambda x, y : (kx*x/winSizeX - ky*y/winSizeY) %1 < 1/2)(x,y)\n        screenPic = CommFun.numpyToPyglet(array, winSizeX, winSizeY)\n    \n    counter = 0\n    # loop through the whole surface of the DMD screen:\n    for m in range(0, squareNumX) :\n    #for m in range(0, 10) :\n        print(\"displaying column \", m, \" out of \", squareNumX)\n        for n in range(0, squareNumY) :\n    #    for n in range(0, 10) :\n            #we change the position of the probe\n            probeX, probeY = CommFun.squarePosition(m, n, squareStepX, squareStepY)\n            #display the image on the DMD\n            display()\n            #and trigg the camera\n            CommFun.trigg(ser, delay)\n            counter += 1\n    \n    pyglet.app.exit()\n    print(\"finished taking the images\")\n\nif DoPlotting:\n    imname = (listdir(dirname)[0])[0:-8]\n    ResultsIntensity = []\n    for m in range(0, squareNumX*squareNumY):\n        # open the image that was taken:\n        image = CommFun.openImage(m, dirname, imname)\n        ResultsIntensity.append(np.sum(image))\n    ResultsIntensity = np.resize(ResultsIntensity, (squareNumX, squareNumY))\n    plt.imshow(ResultsIntensity, interpolation = 'nearest')\n    plt.colorbar()\n    plt.show()   \n\n\npyglet.app.exit()","sub_path":"calib_Intensity.py","file_name":"calib_Intensity.py","file_ext":"py","file_size_in_byte":3953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"466305351","text":"# Crie um programa que leia uma frase qualquer e difa se ela é um palindromo,\n# Desconsiderando os espaços.\n# ex: apos a sopa\n# a torre da derrota\n\nfrase = str(input('Digite uma frase: ')).strip().upper()\npalavras = frase.split() # separa a frase em palavras\njunto = ''.join(palavras) # junta todas as palvras tirando os atributos\ninverso = ''\nfor letra in range(len(junto) - 1, - 1, - 1):\n    inverso += junto[letra]\nprint('O inverso de {} é {}'.format(junto, inverso))\nif inverso == junto:\n    print('temos um palíndromo!')\nelse:\n    print('A frase digitada não é um palindromo')\n\n\n\n","sub_path":"ex053.py","file_name":"ex053.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"610525414","text":"f=open(\"A-large.in\",\"r\")\ng=open(\"phone.out\",\"w\")\n\nT = int(f.readline())\ndigits = [['Z','ZERO',0],['W','TWO',2],['X','SIX',6],['G','EIGHT',8],['S','SEVEN',7],['V','FIVE',5],['I','NINE',9],['F','FOUR',4],['O','ONE',1],['E','THREE',3]]\n\ndef strtolist(s):\n  L = [x for x in s]\n  return L\n\ndef listtostr(L):\n  s = ''\n  for x in L:\n    s = s+str(x)\n  return s\n\nfor i in range(0,T):\n\n  s = f.readline()[:-1]\n  L = strtolist(s)\n  k = 0\n  numbers = []\n  while len(L) > 0:\n    if digits[k][0] in L:\n      for ch in digits[k][1]:\n        L.remove(ch)\n      numbers.append(digits[k][2])\n    else:\n      k+=1\n  numbers.sort()\n  ans = listtostr(numbers)\n\n  g.write(\"Case #\"+str(i+1)+\": \"+ans+\"\\n\")\n\n  \n  \n  \n","sub_path":"codes/CodeJamCrawler/16_2_1_neat/16_2_1_maxicampo_A.py","file_name":"16_2_1_maxicampo_A.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"118590251","text":"# coding=utf-8\n\"\"\"Tool for generating wordfinder databases.\"\"\"\n\nimport shelve\nimport dbm\nimport _dbm\nfrom argparse import ArgumentParser, FileType, ArgumentTypeError\n\n\ndef shelve_type(string):\n    \"\"\"Factory for creating shelve object types.\"\"\"\n    try:\n        return shelve.open(string, \"n\")\n    except (dbm.error, _dbm.error) as e:\n        raise ArgumentTypeError(\"can't open '{}': {}\".format(string, e))\n\n\n# Parse command line arguments.\narg_parser = ArgumentParser(description=\"Generate prefix and word databases.\")\narg_parser.add_argument(\n    \"-i\", \"--input-file\",\n    type=FileType(\"r\"),\n    metavar=\"file\",\n    required=True,\n    help=\"read words (1 word per line) from given file\"\n)\narg_parser.add_argument(\n    \"-p\", \"--prefix-db\",\n    type=shelve_type,\n    metavar=\"filename\",\n    required=True,\n    help=\"create prefix database using given filename\"\n)\narg_parser.add_argument(\n    \"-w\", \"--word-db\",\n    type=shelve_type,\n    metavar=\"filename\",\n    required=True,\n    help=\"create word database using given filename\"\n)\narg_parser.add_argument(\n    \"-q\", \"--quiet\",\n    action=\"store_true\",\n    help=\"suppress output\"\n)\nargs = arg_parser.parse_args()\n\n\n# Generate the two databases.\ncount = 0\nfor line in args.input_file:\n    word = line.strip()\n    for i in range(2, len(word)):\n        # Generate prefixes of length at least 2.\n        prefix = word[:i]\n        args.prefix_db[prefix] = None\n    args.word_db[word] = None\n    count += 1\n    if count % 10000 == 0:\n        if not args.quiet:\n            print(\"{} words processed.\".format(count))\nif not args.quiet:\n    print(\"All ({}) words processed.\".format(count))\n\n\n# Clean up.\nargs.input_file.close()\nargs.prefix_db.close()\nargs.word_db.close()\n","sub_path":"tools/makedict.py","file_name":"makedict.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"87299237","text":"#\n# 二叉树的实现\n#\n\"\"\"二叉树，就是每个节点都至多有二个子节点的树。\n满二叉树，就是除了叶子节点外，每个节点都有左右两个子节点，这种二叉树叫做满二叉树。\n完全二叉树，就是叶子节点都在最底下两层，最后一层叶子节都靠左排列，并且除了最后一层，其他层的节点个数都要达到最大，\n这种二叉树叫做完全二叉树。\n\"\"\"\n\nclass Node(object):\n    def __init__(self, item):\n        self.item = item # 对应元素\n        self.left = None # 对应左节点\n        self.right = None # 对应右节点\n\n    def __str__(self):  # 打印信息\n        \"\"\"本身数组在pop时，时弹出了Node类，\n        Node类在打印的时候，就可调用本函数，替换输出信息        \n        \"\"\"\n        return str(self.item) # 打印元素\n\n\nclass Tree(object):\n    \"\"\"树的实现\n\n    这个树，只是实现了树，并没有排序功能\n\n    Attribute:\n        root:根节点\n    Function:\n        is_empty(self): 判断是否为空\n        add(self, item): 添加一个元素\n        get_parent(self): 找到父类节点\n    \"\"\"\n\n    def __init__(self):\n        self.root = None # 根节点\n\n\n    def is_empty(self):\n        return not bool(self.root)\n\n\n    def add(self, item):\n        # a = [self.root]\n        if self.root:\n            self.root = Node(item)\n        else:\n            # 这里将根节点压入压入数组\n            # 第一次循环只有root节点，当循环第一次结束时，如果2个节点都不为空\n            # 将这两个节点依次加入数组，可进行向下节点循环\n            # 直到找到不为空的节点返回\n            #\n            #   q是节点集合，这个节点集合并不是一开始就有所有的节点，而是在运行中\n            #   慢慢添加，最终所有节点都会添加进来\n            #\n            # 核心-树的添加元素\n\n            q = [self.root]\n            while True:\n                temp = q.pop() # 从节点集合中弹出一个节点\n                if not temp.left:\n                    temp.left = Node(item)\n                    return 1 # 找到节点-返回终止循环\n                elif not temp.right:\n                    temp.right = Node(item)\n                    return 1 # 找到节点-返回终止循环\n                else:\n                    # 将新找到的Node节点添加进 q 可进行下次查找\n                    q.append(temp.left)  # 重要\n                    q.append(temp.right) # 重要\n                    \n\n    def get_parent(self, element):\n\n        if not self.root:\n            print(\"root is Null\")\n\n        p = [self.root]\n        while True:\n            temp = p.pop()\n            if temp.left.item == element or temp.right.item == element:\n                return temp\n\n            if not temp.left: \n                p.append(temp.left)\n\n            if not temp.right:\n                p.append(temp.right)\n\n\n    def delete(self):\n        pass\n\nif __name__ == \"__main__\":\n    a = Tree()\n    a.add()","sub_path":"2-AlgorithmDataStructure-P/P-00-Structure/06-Tree.py","file_name":"06-Tree.py","file_ext":"py","file_size_in_byte":3065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"544077279","text":"# -*- coding: UTF-8 -*-\nimport os\n\nfrom bert_cause.base.bert import tokenization\nfrom bert_cause.base.model.bert import modeling\nfrom bert_cause.base.helper.log_helper import get_logger\nfrom bert_cause.base.model import model_feature\nfrom bert_cause.base.model import model_builder as builder\nimport tensorflow as tf\n\nlogger = get_logger()\n\n\ndef _get_bert_config(config_file):\n    \"\"\"\n    获取bert模型的配置信息\n    :param config_file: json配置文件路径名\n    :return: 配置信息字典\n    \"\"\"\n    return modeling.BertConfig.from_json_file(config_file)\n\n\ndef _get_run_config(model_dir, save_checkpoints_steps):\n    session_config = tf.ConfigProto(\n        log_device_placement=False,\n        inter_op_parallelism_threads=0,\n        intra_op_parallelism_threads=0,\n        allow_soft_placement=True\n    )\n\n    run_config = tf.estimator.RunConfig(\n        model_dir=model_dir,\n        save_summary_steps=save_checkpoints_steps,\n        save_checkpoints_steps=save_checkpoints_steps,\n        log_step_count_steps=save_checkpoints_steps,\n        session_config=session_config\n    )\n\n    return run_config\n\n\nclass ModelHandler(object):\n    def __init__(self, args, label_list):\n        self.args = args\n        self.label_list = label_list\n        self.num_labels = len(self.label_list) + 1  # 这里为什么要加1，因为0索引被保留了；\n        self.tokenizer = tokenization.FullTokenizer(\n            vocab_file=self.args.vocab_file,\n            do_lower_case=self.args.do_lower_case)\n\n    def get_estimator(self, num_train_steps=None, num_warmup_steps=None):\n        \"\"\"\n        :param num_train_steps: 需要训练的步数，默认为None，即可以不进行训练\n        :param num_warmup_steps: 需要热身的步数，默认为None，即可以不进行热身\n        :return: 返回的Estimator可以控制模型的训练，预测，评估工作等。\n        \"\"\"\n        bert_config = _get_bert_config(self.args.bert_config_file)\n        run_config = _get_run_config(self.args.output_dir, self.args.save_checkpoints_steps)\n        # 检查最大长度是否超长\n        if self.args.max_seq_length > bert_config.max_position_embeddings:\n            raise ValueError(\n                \"Cannot use sequence length %d because the BERT model \"\n                \"was only trained up to sequence length %d\" %\n                (self.args.max_seq_length, bert_config.max_position_embeddings))\n\n        # model_fn 是一个函数，其定义了模型，训练，评测方法，tf 新的架构方法，通过定义model_fn 函数，定义模型，\n        # 然后通过EstimatorAPI进行模型的其他工作，Estimator就可以控制模型的训练，预测，评估工作等。\n        model_fn = builder.model_fn_builder(\n            bert_config=bert_config,\n            num_labels=self.num_labels,  # 要保证label2id.pkl文件中最大索引能够取得到\n            init_checkpoint=self.args.init_checkpoint,\n            learning_rate=self.args.learning_rate,\n            num_train_steps=num_train_steps,\n            num_warmup_steps=num_warmup_steps,\n            args=self.args)\n\n        params = {'batch_size': self.args.batch_size}\n\n        return tf.estimator.Estimator(model_fn, params=params, config=run_config)\n\n    def train(self, train_examples, eval_examples):\n        num_train_steps = int(len(train_examples) * 1.0 / self.args.batch_size *\n                              self.args.num_train_epochs)\n        num_warmup_steps = int(num_train_steps * self.args.warmup_proportion)\n        num_eval_steps = int(len(eval_examples) * 1.0 / self.args.batch_size)\n\n        logger.info(\"***** Running training *****\")\n        logger.info(\"  Num examples = %d\", len(train_examples))\n        logger.info(\"  Batch size = %d\", self.args.batch_size)\n        logger.info(\"  Num steps = %d\", num_train_steps)\n\n        logger.info(\"***** Running evaluation *****\")\n        logger.info(\"  Num examples = %d\", len(eval_examples))\n        logger.info(\"  Batch size = %d\", self.args.batch_size)\n        logger.info(\"  Num steps = %d\", num_eval_steps)\n\n        estimator = self.get_estimator(num_train_steps, num_warmup_steps)\n        # 1. 将数据转化为features\n        train_file = os.path.join(self.args.output_dir, \"train.tf_record\")\n        if not os.path.exists(train_file):\n            model_feature.write_features(\n                train_examples,\n                self.label_list,\n                self.args.max_seq_length,\n                self.tokenizer,\n                train_file,\n                self.args.output_dir)\n\n        eval_file = os.path.join(self.args.output_dir, \"eval.tf_record\")\n        if not os.path.exists(eval_file):\n            model_feature.write_features(\n                eval_examples,\n                self.label_list,\n                self.args.max_seq_length,\n                self.tokenizer,\n                eval_file,\n                self.args.output_dir)\n\n        # 2. 构建input_fn\n        train_input_fn = builder.file_based_input_fn(\n            input_file=train_file,\n            seq_length=self.args.max_seq_length,\n            is_training=True,\n            drop_remainder=True,\n            num_parallel_cells=self.args.num_parallel_cells\n        )\n\n        eval_input_fn = builder.file_based_input_fn(\n            input_file=eval_file,\n            seq_length=self.args.max_seq_length,\n            is_training=False,\n            drop_remainder=False,\n            num_parallel_cells=self.args.num_parallel_cells,\n        )\n        # 3. 构建钩子函数\n        early_stopping_hook = builder.build_decrease_hook(\n            estimator=estimator,\n            num_train_steps=num_train_steps,\n            save_checkpoints_steps=self.args.save_checkpoints_steps)\n\n        # 4. 开始训练\n        train_spec = tf.estimator.TrainSpec(\n            input_fn=train_input_fn,\n            max_steps=num_train_steps,\n            hooks=[early_stopping_hook])\n        eval_spec = tf.estimator.EvalSpec(input_fn=eval_input_fn)\n        tf.estimator.train_and_evaluate(estimator, train_spec, eval_spec)\n\n    def predict(self, predict_examples):\n        \"\"\" 预测并返回标签列表 [num_lines, max_seq_length]\"\"\"\n        logger.info(\"***** Running prediction*****\")\n        logger.info(\"  Num examples = %d\", len(predict_examples))\n        logger.info(\"  Batch size = %d\", self.args.batch_size)\n\n        predict_file = os.path.join(self.args.output_dir, \"predict.tf_record\")\n        model_feature.write_features(\n            predict_examples,\n            self.label_list,\n            self.args.max_seq_length,\n            self.tokenizer,\n            predict_file,\n            self.args.output_dir)\n        predict_input_fn = builder.file_based_input_fn(\n            input_file=predict_file,\n            seq_length=self.args.max_seq_length,\n            is_training=False,\n            drop_remainder=False,\n            num_parallel_cells=self.args.num_parallel_cells\n        )\n        estimator = self.get_estimator()\n        result = estimator.predict(input_fn=predict_input_fn)\n        pred_ids = []\n        for prediction in result:\n            pred_ids.append(prediction[\"pred_ids\"])\n        return pred_ids\n","sub_path":"bert_cause/base/model/model_handler.py","file_name":"model_handler.py","file_ext":"py","file_size_in_byte":7164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"47914197","text":" \nfrom sockets import Server\nimport os\n\n\nclass Game:\n    def __init__(self) -> None:\n        self.server = Server()\n        self.players = {}\n    \n    def update(self):\n        while True:\n            temp_data = self.server.get_data()\n            self.players[temp_data.addr] = temp_data\n\n\nif __name__ == \"__main__\":\n    game = Game()","sub_path":"Game.py","file_name":"Game.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"481217151","text":"# -*- coding: utf-8 -*-\n\"\"\"\niDBSCAN: Iterative Density-Based Spatial Clustering of Applications with Noise\n\"\"\"\n\nimport numpy as np\nfrom sklearn.cluster import DBSCAN\n\ndef idbscan(X, iterative = 4, vector_eps = [2.26, 3.5, 2.8, 6], vector_min_samples = [2, 30, 6, 2], cuts = [900, 150], flag_noise = True, flag_plot_noise = 0):\n    \"\"\"\n    Parameters\n    ----------\n    \n    X : array or sparse (CSR) matrix of shape (n_samples, n_features), or \\\n                array of shape (n_samples, n_samples)\n            A feature array, or array of distances between samples if\n            ``metric='precomputed'``.\n            \n    iterative : int, optional\n        How many time the DBSCAN will run.\n        0 - to run naive DBSCAN using the first parameter on eps and min_samples array\n        1 - to look for and save only the 'long' tracks\n        2 - to look for and save only the 'medium' tracks\n        3 - to look for and save only the 'small' tracks\n        4 - to look for and save all the three types\n        12 - to look for and save the 'long' and the 'medium' tracks\n    \n    vector_eps : float array(1x4), optional\n        The maximum distance between two samples for them to be considered\n        as in the same neighborhood.\n        \n    vector_min_samples : int array (1x4), optional\n        The number of samples (or total weight) in a neighborhood for a point\n        to be considered as a core point. This includes the point itself.\n        \n    cuts : int array (1x2), optional\n        The min number of sampels that the clusters need to be considerer \n        'long', 'medium' or 'small'.\n    \n    flag_noise : Boolean\n        If it is TRUE the noise removing loop is done, when it is FALSE \n        no noise removing is made.\n    \n    Returns\n    -------\n    core_samples : array [n_core_samples]\n        Indices of core samples.\n    labels : array [n_samples]\n        Cluster labels for each point.  Noisy samples are given the label -1.\n    tag : array [n_samples]\n        tag for each point.\n            - Noisy samples are given the label '-1'\n            - 1st iteration tracks are given the label '1'\n            - 2nd iteration tracks are given the label '2'\n            - 3rd iteration tracks are given the label '3'\n\n    \"\"\"\n\n    ## - - - - -\n    Index              = np.arange(0,np.shape(X)[0],dtype=int)\n    Fcluster           = (-1)+np.zeros(np.shape(X)[0],dtype=int)\n    Flabel             = np.empty(np.shape(X)[0],dtype=int)\n    Flabel[:]          = -1\n    auxClu             = -1\n    # - - - - - -\n    #vector_eps         = [2.26, 3.5, 2.8, 6]\n    #vector_min_samples = [2, 30, 6, 2]\n    auxIti             = - 1\n    ## - - - - -\n    indgood = np.ones(np.shape(X)[0],dtype=bool)\n    \n    if (iterative >= 0) & (flag_noise == True) :\n\n        auxIti += 1\n        db      = DBSCAN(eps=vector_eps[auxIti], min_samples=vector_min_samples[auxIti]).fit(X)\n        labels  = db.labels_\n        indgood = db.labels_ != -1\n\n        \n        if flag_plot_noise == 1:\n            import matplotlib.pyplot as plt\n            f,ax = plt.subplots(1,2,figsize=(40,20))\n            ax[0].scatter(X[:, 1], X[:, 0], alpha = 0.5, s = 10, linewidths = 0)\n            ax[0].set_title('Edges after pedestal substraction')\n            ax[1].scatter(X[indgood, 1], X[indgood, 0], alpha = 0.5, s = 10, linewidths = 0)\n            ax[1].set_title('Edges after removing \"noise\"')\n\n        ## ----- Salve the clusters and labels\n        Fcluster[db.labels_ == -1] = -1\n        Flabel[db.labels_   == -1] = -1 # 'n' = noise points\n\n        if iterative == 0:\n            ## ----- Salve the clusters and labels\n            Fcluster        = labels\n            Flabel[indgood] = 3 # 'n' = noise points\n    else:\n        auxIti += 1\n\n    if iterative >= 1:\n        #print('indgood: ', sum(indgood))\n\n        Xnew      = X[indgood,:]\n        #print('Xnew: ', Xnew)\n        indicenew = np.where(indgood == True)[0]\n\n        auxIti    += 1\n        db         = DBSCAN(eps=vector_eps[auxIti], min_samples=vector_min_samples[auxIti]).fit(Xnew)\n        labels     = db.labels_\n        n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\n        # Find the Long clusters\n\n        clusters = [Xnew[labels == i] for i in range(n_clusters_)]\n\n        lenClu = np.zeros(n_clusters_,)\n        for i in range(0,n_clusters_):\n            lenClu[i] = np.size(clusters[i])\n        clusterI = (np.where(lenClu > cuts[0]))[0]\n\n        if iterative == 1 or iterative == 4 or iterative == 12: # To salve ONLY the Long Clusters or 4 to all\n            ## ----- Salve the clusters and labels\n            for i in clusterI:\n                auxClu+=1\n                indice = Index[indicenew[labels == i]]\n                Fcluster[indice] = auxClu\n                Flabel[indice] = 1 # 'l' = Long tracks\n\n    if iterative >= 2:\n\n        indgood2 = ~np.in1d(db.labels_, clusterI)\n        Xnew2 = Xnew[indgood2,:]\n        if np.size(Xnew2) > 1:\n            indicenew2 = np.where(indgood2 == True)[0]\n\n            auxIti+=1\n            db = DBSCAN(eps=vector_eps[auxIti], min_samples=vector_min_samples[auxIti]).fit(Xnew2)\n            labels = db.labels_\n            n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\n            clusters = [Xnew2[labels == i] for i in range(n_clusters_)]\n\n            lenClu = np.zeros(n_clusters_,)\n            for i in range(0,n_clusters_):\n                lenClu[i] = np.size(clusters[i])\n            clusterI = (np.where(lenClu > cuts[1]))[0]\n\n            if iterative == 2 or iterative == 4 or iterative == 12: # To salve ONLY the Medium Clusters or 4 to all\n                ## ----- Salve the clusters and labels\n                for i in clusterI:\n                    auxClu+=1\n                    indice = Index[indicenew[indicenew2[labels == i]]]\n                    Fcluster[indice] = auxClu\n                    Flabel[indice] = 2 # 'c' = Curly tracks\n\n\n    if iterative >= 3:\n\n        indgood3 = ~np.in1d(db.labels_, clusterI)\n        Xnew3 = Xnew2[indgood3,:]\n        if np.size(Xnew3) > 1:\n            indicenew3 = np.where(indgood3 == True)[0]\n\n            auxIti+=1\n            db = DBSCAN(eps=vector_eps[auxIti], min_samples=vector_min_samples[auxIti]).fit(Xnew3)\n            labels = db.labels_\n            n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n\n            clusters = [Xnew3[labels == i] for i in range(n_clusters_)]\n\n            if iterative == 3 or iterative == 4: # To salve ONLY the Small Clusters or 4 to all\n                ## ----- Salve the clusters and labels\n                for j in range(0,n_clusters_):\n                    auxClu+=1\n                    indice = Index[indicenew[indicenew2[indicenew3[labels == j]]]]\n                    Fcluster[indice] = auxClu\n                    Flabel[indice] = 3 # 'c' = others tracks\n\n\n    return Fcluster, np.where(Fcluster != -1)[0], Flabel\n\n\nclass iDBSCAN:\n    \n    def __init__(self, iterative = 4, vector_eps = [2.26, 3.5, 2.8, 6], vector_min_samples = [2, 30, 6, 2], cuts = [900, 150], flag_noise = True, flag_plot_noise = 0):\n        self.iterative = iterative\n        self.vector_eps = vector_eps\n        self.vector_min_samples = vector_min_samples\n        self.cuts = cuts\n        self.flag_noise = flag_noise\n        self.flag_plot_noise = flag_plot_noise\n\n    def fit(self, X):\n        \n        clust = idbscan(X, self.iterative, self.vector_eps, self.vector_min_samples, self.cuts, self.flag_noise, self.flag_plot_noise)\n        self.labels_, self.core_sample_indices_, self.tag_  = clust\n        \n        return self\n","sub_path":"iDBSCAN.py","file_name":"iDBSCAN.py","file_ext":"py","file_size_in_byte":7549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"471652044","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#python 3.6.4\n\nimport json\nimport classes\n\ndef loadWords():\n\twith open('wordsLen.ini','r') as file:\n\t\tallWords = json.loads(file.read())\n\treturn allWords\n\ndef loadPlayer():\n\ttry:\n\t\twith open('player.ini','r') as file:\n\t\t\tplayerData = json.loads(file.read())\n\n\t\tprint([x for x in playerData.keys()],sep=' ',end='\\n')\n\t\tplayerName = 'Sid'#input(\"who are you?\\n\")\n\t\ttry:\n\t\t\tplayer = classes.PlayerCharacter(playerDict=playerData[playerName])\n\t\texcept KeyError:\n\t\t\tprint(\"player doesn't exist\")\n\t\t\tplayerName = input(\"what's your name?\\n\")\n\t\t\tplayerClass = input(\"what's your class?\\n\")\n\t\t\tplayer = classes.PlayerCharacter(playerName=playerName,playerClass=playerClass)\n\texcept FileNotFoundError:\n\t\tplayerData = {}\n\t\tplayerName = input(\"what's your name?\\n\")\n\t\tplayer = classes.PlayerCharacter(playerName=playerName)\n\treturn playerData,player\n\ndef savePlayer(playerData,player):\n\tplayerData[player.name] = player.toJSON()\n\n\twith open('player.ini','w') as file:\n\t\tjson.dump(playerData,file)\n\treturn","sub_path":"backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"637539421","text":"#!/usr/bin/env python\n\nimport csv\nimport requests\nfrom urllib.parse import urljoin\n\nrecurrence_map = {\n    '': 'N/A',\n    'd': 'daily', \n    'w': 'weekly',\n    'm': 'monthly',\n    'q': 'quarterly'\n}\n\ndef main():\n    token = input(\"API Token:\")\n    csv_filename = input(\"CSV path to filename (default: ./scheduled.csv):\")\n    if csv_filename == \"\":\n        csv_filename = \"./scheduled.csv\"\n\n    headers = {\"Authorization\": \"JWT {}\".format(token)}\n\n    api_base_url = \"https://api.probely.com\"\n    targets_endpoint = urljoin(\n        api_base_url, \"targets/?include=compliance&length=10000\"\n    )\n\n    response = requests.get(targets_endpoint, headers=headers)\n    results = response.json()[\"results\"]\n\n    with open(csv_filename, \"w\") as csv_file:\n        csv_writer = csv.writer(\n            csv_file, delimiter=\",\", quotechar='\"', quoting=csv.QUOTE_ALL\n        )\n        csv_writer.writerow([\"ID\", \"NAME\", \"URL\", \"NEXT SCAN DATE\", \"RECURRENCE\"])\n        for result in results:\n            next_scan = result[\"next_scan\"] if result.get(\"next_scan\") else None\n            if not next_scan:\n                continue\n            row = [\n                result[\"id\"],\n                result[\"site\"][\"name\"],\n                result[\"site\"][\"url\"],\n                next_scan.get(\"date_time\", \"\"),\n                recurrence_map[next_scan.get(\"recurrence\", \"\")]\n            ]\n            csv_writer.writerow(row)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"targets_scheduled_scans_csv.py","file_name":"targets_scheduled_scans_csv.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"566749749","text":"import json\nimport logging\nimport datetime\nimport threading\nfrom autobahn.twisted.websocket import WebSocketClientProtocol, WebSocketClientFactory\nfrom twisted.internet.protocol import ReconnectingClientFactory\n\nimport configs\nimport utils\n\nlogging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p',\n                    filename=configs.LOGGING_FILE, level=logging.DEBUG)\n\n\nclass StreamConnection:\n    def run(self, socket_protocol):\n        if 'rtsp' in configs.PROTOCOL_USED:\n            # RTSP protocol\n            thread = threading.Thread(target=utils.use_rtsp_con, args=(socket_protocol, configs.RTSP_IP_CAM_URL))\n            thread.daemon = True  # Daemonize thread\n            thread.start()\n        if 'mjpg' in configs.PROTOCOL_USED:\n            # HTTP with mjpg format\n            thread = threading.Thread(target=utils.use_mjpg_con, args=(socket_protocol, configs.MJPG_IP_CAM_URL,\n                                                                       configs.MJPG_USERNAME, configs.MJPG_PASSWORD))\n            thread.daemon = True  # Daemonize thread\n            thread.start()\n\n\nclass AppProtocol(WebSocketClientProtocol):\n\n    def onConnect(self, response):\n        logging.info(\"Connected to the server\")\n        self.factory.resetDelay()\n\n        stream_conn = StreamConnection()\n\n        thread = threading.Thread(target=stream_conn.run, args=(self,))\n        thread.daemon = True  # Daemonize thread\n        thread.start()\n\n    def onOpen(self):\n        logging.info(\"Connection is open.\")\n\n    def onMessage(self, payload, isBinary):\n        if (isBinary):\n            logging.info(\"Got Binary message {0} bytes\".format(len(payload)))\n        else:\n            data = payload.decode('utf8')\n            msg_data = json.loads(data)\n            if msg_data['type'] == 'RECOGNIZED' and msg_data['name'] != configs.NOT_RECOGNIZED:\n                temp_now = datetime.datetime.now().timestamp()\n                if utils.is_enough_waited_after_success(temp_now):\n                    utils.last_rec_timestamp = datetime.datetime.now().timestamp()\n                logging.info(\"Got Text message from the server {0}\".format(data))\n\n    def onClose(self, wasClean, code, reason):\n        logging.info(\"Connect closed {0}\".format(reason))\n\n\nclass AppFactory(WebSocketClientFactory, ReconnectingClientFactory):\n    protocol = AppProtocol\n\n    def clientConnectionFailed(self, connector, reason):\n        logging.info(\"Unable connect to the server {0}\".format(reason))\n        self.retry(connector)\n\n    def clientConnectionLost(self, connector, reason):\n        logging.info(\"Lost connection and retrying... {0}\".format(reason))\n        self.retry(connector)\n\n\nif __name__ == '__main__':\n    import sys\n    from twisted.python import log\n    from twisted.internet import reactor\n\n    log.startLogging(sys.stdout)\n    factory = AppFactory(configs.SOCKET_URL)\n    reactor.connectTCP(configs.SOCKET_SERVER, configs.SOCKET_PORT, factory)\n    reactor.run()\n","sub_path":"ip-cam-client.py","file_name":"ip-cam-client.py","file_ext":"py","file_size_in_byte":2987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"279843123","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nfrom itertools import product\nimport sys\nsys.path.append('../Environments/')\nfrom ColoredGridWorld.MDP import MDP\nfrom ColoredGridWorld import OBMDP\nsys.path.append('../Algorithms/')\nfrom ValueIteration import ValueIteration\nfrom DictValueIteration import ValueIteration as DictValueIteration\nimport math \nimport copy\n\n\n# In[ ]:\n\n\nclass HashableBelief(object):\n    def __init__(self, beliefDict):\n        self.beliefDict = beliefDict\n    \n    def __call__(self):\n        return self.beliefDict\n    \n    def __eq__(self, other): \n        if not isinstance(other, HashableBelief):\n            return NotImplemented\n\n        return self.beliefDict == other.beliefDict \n    \n    def __hash__(self):\n        return hash((frozenset(self.beliefDict.items())))\n\n\nclass HashableWorld(object):\n    def __init__(self, colourReward, isDeterministic):\n        self.colourReward = colourReward\n        self.isDeterministic = isDeterministic\n        \n    def __call__(self):\n        return (self.colourReward)\n    \n    def __eq__(self, other): \n        if not isinstance(other, HashableWorld):\n            return NotImplemented\n\n        return self.colourReward == other.colourReward and self.isDeterministic == other.isDeterministic\n    \n    def __hash__(self):\n        return hash((frozenset(self.colourReward.items()), self.isDeterministic))\n    \n\"\"\"\n    Constructs all possible colour reward mappings \n    Input: \n        variableColours: list of colours with variable rewards\n        variableReward: list of possible scalar rewards for the variable colours\n        constantRewardDict: dict of colour: reward for colours whose rewards are fixed\n    Output:\n        utilitySpace: list of dictionaries corresponding to all possible colours reward mappings\n\n\"\"\"\n    \ndef buildUtilitySpace(variableColours, variableReward, constantRewardDict):\n    utilitySpace = [{key:value for key, value in zip(variableColours, permutations)} for permutations in product(variableReward, repeat = len(variableColours))]\n    for i in range(len(utilitySpace)):\n        utilitySpace[i].update(constantRewardDict)\n    return utilitySpace\n\n\"\"\"\n    Constructs mapping of envs to their MDPs and Policies  \n    Input:\n        dimensions: tuple of mXn\n        stateSpace: dict of state: colours\n        actions: list of actions\n        envSpace: list of (world,goal) tuples\n        hyperparameters: list of convergenceTolerance, gamma(discount factor), alpha(softmax temp), eps(non-value randomness)\n        \n    Output: \n        dictionary of all env:(MDP, Policy)\n\"\"\"\n\ndef buildEnvPolicySpace(dimensions, stateSpace, actions, envSpace, hyperparameters):\n    convergenceTolerance, gamma, alpha, eps = hyperparameters \n    envMDPs = [MDP(dimensions, stateSpace, world()) for world,goal in envSpace] \n    rewardFunctions = [MDP() for MDP in envMDPs]\n    transitionFunctions = [MDP.transitionFunction for MDP in envMDPs]\n    valueTable = {key: 0 for key in stateSpace.keys()}\n    ValueIterations = [ValueIteration(actions, transitionFunction, rewardFunction, valueTable, [env[1]], convergenceTolerance, gamma, alpha, eps, True) for rewardFunction, transitionFunction, env in zip(rewardFunctions, transitionFunctions,envSpace)]\n    ValueAndPolicyTables = [ValueIteration() for ValueIteration in ValueIterations]\n    envPolicies = [ValueAndPolicyTable[1] for ValueAndPolicyTable in ValueAndPolicyTables]\n    return {(env):(MDP,policy) for env, MDP, policy in zip(envSpace, envMDPs, envPolicies)}\n\n\"\"\"\n    Constructs mapping of envs to their OBMDPs and Policies  \n    Input:\n        jointStateSpace: list of all possible (objectState, HashableBelief) tuples\n        actions: list of actions\n        envMDPsAndPolicies: dictionary of all env:(MDP, Policy)\n        getNextBelief: function that inputs (jointState, action, nextObjectState) and returns the next belief state\n        beliefHyperparameters: list of convergenceTolerance, beta, gamma(discount factor), alpha(softmax temp), eps(non-value randomness)\n        \n    Output: \n        dictionary of all env:(OBMDP, Policy)\n\n\"\"\"\n\ndef buildPragmaticEnvPolicySpace(jointStateSpace, actions, envMDPsAndPolicies, getNextBelief, beliefUtilityFn, beliefHyperparameters):\n    convergenceTolerance, beta, beliefGamma, beliefAlpha, beliefEps = beliefHyperparameters\n    #beliefUtilityFn = OBMDP.getBeliefUtility()\n    envOrder = tuple(env for env in envMDPsAndPolicies.keys())\n    envOBMDPs = [OBMDP.OBMDP(env, True, beta, envMDPsAndPolicies[env][0].transitionFunction, getNextBelief) for env in envOrder]\n    jointRewardFunctions = [OBMDp(envMDPsAndPolicies[env][0](), beliefUtilityFn) for env, OBMDp in zip(envOrder, envOBMDPs)]\n    #jointTransitionDicts = [{jointState:{action:OBMDP.transitionFunction(jointState, action) for action in actions} for jointState in jointStateSpace if jointState[0]!=env[1]} for OBMDP, env in zip(envOBMDPs, envOrder)]\n    jointTransitionDicts = []\n    for OBMDp, env in zip(envOBMDPs, envOrder):\n        jointDict = {jointState:{action:OBMDp.transitionFunction(jointState, action) for action in actions} for jointState in jointStateSpace if jointState[0]!=env[1]} \n        jointTransitionDicts.append(jointDict)\n        print(str(env[0]()) + \" transition dict done\")\n    jointRewardDicts = []\n    for jointTransitionDict, jointRewardFn in zip(jointTransitionDicts, jointRewardFunctions):\n        jointRewardDict = copy.deepcopy(jointTransitionDict)\n        for jointState, actionAndNextStateDict in jointRewardDict.items():\n            for action, nextJointStateAndProb in actionAndNextStateDict.items():\n                for nextJointState in nextJointStateAndProb.keys():\n                    jointRewardDict[jointState][action][nextJointState] = jointRewardFn(jointState, action, nextJointState)\n        jointRewardDicts.append(jointRewardDict)\n    valueTable = {key: 0 for key in jointStateSpace}\n    jointGoalStatesList = [[jointState for jointState in jointStateSpace if jointState[0] == env[1]] for env in envOrder]\n    ValueIterations = [DictValueIteration(jointTransitionDict, jointRewardDict, valueTable, jointGoalStates, convergenceTolerance, beliefGamma, beliefAlpha, beliefEps) for jointTransitionDict, jointRewardDict, jointGoalStates in zip(jointTransitionDicts, jointRewardDicts, jointGoalStatesList)]\n    ValueAndPolicyTables = [ValueIteration() for ValueIteration in ValueIterations]\n    envPolicies = [ValueAndPolicyTable[1] for ValueAndPolicyTable in ValueAndPolicyTables]\n    return {env:(OBMDp, policy) for env, OBMDp, policy in zip(envOrder, envOBMDPs, envPolicies)}\n    \n\"\"\"\n    Returns list of all possible worlds\n    Input: \n        utilitySpace: list of possible colour:reward dictionaries\n        transitionSpace: list of boolean values (True for deterministic, False for not)\n    Output: \n        list of World objects\n\"\"\"\n\ndef buildWorldSpace(utilitySpace, transitionSpace):\n    return [HashableWorld(colourReward, isDeterministic) for colourReward, isDeterministic in product(utilitySpace, transitionSpace)]\n\n\"\"\"\n    Given a dictionary of env (i.e. (world, goal) tuple):_ returns a dictionary with envs replaced with their x (trap) o (safe) labels\n    Input:\n        envDict: dict of the form env:_\n        orderOfTrapColours: list/tuple of the order of potential trap states for the label ordering \n                            for eg. when orange and purple are traps with order being (orange, purple, blue), label will be xxo\n    Output:\n        dictionary with envs replaced with desired labels\n\"\"\"\n\ndef mapEnvToLabel(envDict, orderOfTrapColours):\n    envLabels = {}\n    for env in envDict.keys():\n        colourReward = env[0]()\n        label = \"\"\n        for colour in orderOfTrapColours:\n            if(colourReward[colour]<0):\n                label = label + \"x\"\n            else: \n                label = label + \"o\"\n        envLabels[env] = label\n    return {envLabels[env]:prob for env, prob in envDict.items()} \n\n","sub_path":"Environments/ColoredGridWorld/SetUpInferenceSpace.py","file_name":"SetUpInferenceSpace.py","file_ext":"py","file_size_in_byte":7937,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"544709703","text":"# -*- coding: utf-8 -*-\nimport argparse\n\nparser = argparse.ArgumentParser(description='Simple config parser')\nparser.add_argument('-in', action='store', dest='in_file', help='File to parse')\nparser.add_argument('-out', action='store', dest='out_file', help='Output file')\nparser.add_argument('-search', action='store', dest='search', help='word to search')\nparser.add_argument('-v', dest='verbose', help='verbose output', action='store_true')\n\nargs = parser.parse_args()\n\nif not args.in_file or not args.out_file:\n    print('In or Out file not set')\n    exit()\n\nif not args.search:\n    print(\"Specify word to search\")\n    exit()\n\ntry:\n    f = open(str(args.in_file), 'r')\nexcept FileNotFoundError:\n    print(\"In file doesn't exist\")\n    exit()\n\nout = open(str(args.out_file), 'a')\nlines = f.readlines()\ni = 0\nfor line in lines:\n    # For debug\n    i += 1\n    if args.verbose:\n        print(i, line)\n    try:\n        if str(args.search).lower() in line.lower():\n            out.write(line)\n    except Exception:\n        print('######### CANNOT READ STRING {} #########'.format(i))\n        out.write('############# CANT READ STRING {}###########'.format(i))\n\nf.close()\nout.close()\n","sub_path":"simple_parser.py","file_name":"simple_parser.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"624244253","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n#  bubblesort.py\n\ndef main(args):\n    x = True\n    tb = [2, 3, 5, 6, 10, 9, 4]\n    ltb = len(tb)-1\n    while ltb > 0 and x:\n        x = False\n        for i in range(ltb):\n            if tb[i] > tb[i+1]:\n                x = True\n                d = tb[i]\n                tb[i] = tb[i+1]\n                tb[i+1] = d\n    print(tb)\n\nif __name__ == '__main__':\n    import sys\n    sys.exit(main(sys.argv))\n","sub_path":"python/bubblesort/bubblesort.py","file_name":"bubblesort.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"62314664","text":"import os\r\nimport csv\r\nimport sys\r\n\r\nbudget_csv = os.path.join(r\"Resources\\budget_data.csv\")\r\n\r\nwith open(budget_csv, \"r\") as csvfile:\r\n    csv_reader = csv.reader(csvfile, delimiter = \",\")\r\n\r\n    #stores header row\r\n    header = next(csv_reader)\r\n\r\n    total_months = 0\r\n    net_total = 0\r\n    profit_losses = []\r\n    dates = []\r\n\r\n    for rows in csv_reader:\r\n        \r\n        #total number of months in dataset\r\n        total_months = total_months + 1\r\n\r\n        #net total of profit/losses\r\n        net_total = net_total + int(rows[1])\r\n\r\n        #creates list of profit/loss column\r\n        profit_losses.append(int(rows[1]))\r\n\r\n        if total_months > 1:\r\n            dates.append(rows[0])\r\n\r\n    #calculates profit/loss difference per month\r\n    monthly_diff = [profit_losses[value + 1] - profit_losses[value] for value in range(len(profit_losses)-1)]\r\n\r\n    #solution for average of changes in profit/loss over entire period\r\n    avg_chg_profitLoss = sum(monthly_diff) / (len(profit_losses)-1)\r\n    \r\n    max_monthly_diff = 0\r\n    min_monthly_diff = 0\r\n\r\n    max_date = \"\"\r\n    min_date = \"\"\r\n    \r\n    for date, values in zip(dates, monthly_diff):\r\n\r\n        #calculates greatest increase in profits over entire period\r\n        if values > max_monthly_diff:\r\n\r\n            max_monthly_diff = values\r\n\r\n            max_date = date\r\n\r\n        #calculates decrease in losses over entire period\r\n        elif values < min_monthly_diff:\r\n\r\n            min_monthly_diff = values\r\n\r\n            min_date = date\r\n\r\n    #print analysis results to terminal\r\n    print(\"Financial Analysis\")\r\n    print(\"----------------------\")\r\n    print(f'Total Months: {total_months}')\r\n    print(f'Total: {\"${:,.2f}\".format(net_total)}')\r\n    print(f'Average Change: {\"${:,.2f}\".format(avg_chg_profitLoss)}')\r\n    print(f'Greatest Increase in Profits: {max_date} ({\"${:,.2f}\".format(max_monthly_diff)})')\r\n    print(f'Greatest Decrease in Profits: {min_date} ({\"${:,.2f}\".format(min_monthly_diff)})')\r\n    \r\n    #exports print statements as txt file to Analysis folder in PyBank\r\n    sys.stdout = open(\"PyBank/Analysis/Analysis\", \"w\")\r\n    \r\n    print(\"Financial Analysis\")\r\n    print(\"----------------------\")\r\n    print(f'Total Months: {total_months}')\r\n    print(f'Total: {\"${:,.2f}\".format(net_total)}')\r\n    print(f'Average Change: {\"${:,.2f}\".format(avg_chg_profitLoss)}')\r\n    print(f'Greatest Increase in Profits: {max_date} ({\"${:,.2f}\".format(max_monthly_diff)})')\r\n    print(f'Greatest Decrease in Profits: {min_date} ({\"${:,.2f}\".format(min_monthly_diff)})')\r\n    ","sub_path":"Python-Challenge/PyBank/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2564,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"597813999","text":"from flask import Flask, redirect, url_for, g, render_template, request, session, jsonify, json\nfrom spotify_requests import spotify\nfrom spotify_requests import spotify as sp\nfrom os.path import abspath\nimport ConfigParser\nimport pyrebase\n\n\n\napp = Flask(__name__)\napp.secret_key = 'key key its a key of keys'\n\ncfg = ConfigParser.ConfigParser()\ncfg.read(\"/home/beauho/Programming/flaskappConfig/config.ini\")\n\nconfig = {\n\t\"apiKey\": cfg.get('info','FIREBASE_API_KEY'),\n\t\"authDomain\": \"matchify-7b750.firebaseapp.com\",\n  \t\"databaseURL\": \"https://matchify-7b750.firebaseio.com\",\n  \t\"projectId\": \"matchify-7b750\",\n  \t\"storageBucket\": \"matchify-7b750.appspot.com\",\n  \t\"serviceAccount\": \"/home/beauho/Programming/flaskapp/firebase-private-key.json\",\n  \t\"messagingSenderId\": \"367663586987\"\n}\n\nfirebase = pyrebase.initialize_app(config)\ndb = firebase.database()\n\n#Authorization API Calls\n@app.route(\"/auth\")\ndef auth():\n\treturn redirect(spotify.AUTH_URL)\n\n@app.route(\"/callback\")\ndef callback():\n\tauth_token = request.args['code']\n\tauth_header = spotify.authorize(auth_token)\n\tsession['auth_header'] = auth_header\n\n\treturn redirect(url_for('home'))\n\t#return profile()\n\ndef valid_toek(resp):\n\treturn resp is not None and not 'error' in resp\n\n#@app.route(\"/logout\")\n#def logout():\n\n#----------------END Authorizaton\n\n\n#Spotify Helper functions\n\ndef getUserId():\n\tprofileJSON = spotify.get_users_profile(session['auth_header'])\n\treturn profileJSON[\"id\"]\n\n#----------------END Helper\n\n#Home Page Calls\n@app.route(\"/\")\ndef home():\n\treturn render_template(\"home.html\")\n\n@app.route(\"/home\")\ndef template():\n\treturn render_template(\"home.html\")\n#----------------END Home Calls\n\n#Account Synchronization\n\n@app.route(\"/topArtists\")\ndef topArtists(artists=None,tracks=None, profile=None):\n\tif (session['auth_header'] == None):\n\t\treturn redirect(url_for('auth'))\n\t#Grab account info\n\tprofileJSON = spotify.get_users_profile(session['auth_header'])\n\t#Grab account data\n\tartists = spotify.get_users_top(session['auth_header'],'artists')\n\n\tif (artists is None):\n\t\treturn str(\"Sorry, this account has no listening information and cannot be used. Listen to some music on Spotify before using.\")\n\n\tif (profileJSON != None):\n\t\treturn render_template('data.html', artists=artists[\"items\"], profile=profileJSON)\n\telse:\n\t\treturn redirect(url_for('home'))\n\n@app.route(\"/topTracks\")\ndef topTracks():\n\tif (session['auth_header'] == None):\n\t\treturn redirect(url_for('auth'))\n\t#Grab account info\n\tprofileJSON = spotify.get_users_profile(session['auth_header'])\n\t#Grab account data\n\ttracks = spotify.get_users_top(session['auth_header'], 'tracks')\n\treturn str(tracks)\n\n@app.route(\"/updateAccountData\")\ndef updateAccountData():\n\tif (session['auth_header'] == None):\n\t\treturn redirect(url_for('auth'))\n\t#Get data\n\tprofileJSON = spotify.get_users_profile(session['auth_header'])\n\ttracks = spotify.get_users_top(session['auth_header'], 'tracks')\n\tartists = spotify.get_users_top(session['auth_header'], 'artists')\n\n\tprofileID = profileJSON[\"id\"]\n\n\tdb.child(\"matchData\").child(profileID).child(\"artists\").set(artists)\n\tdb.child(\"matchData\").child(profileID).child(\"tracks\").set(tracks)\n\n\treturn str(\"For account: \"+profileID+\" tracks:\"+str(tracks)+\" artists:\"+str(artists))\n\n\n@app.route(\"/updateAccountInfo\", methods=['POST', 'GET'])\ndef updateAccountInfo(accountUpdated=None, AccountInfo=None):\n\tif (session['auth_header'] == None):\n\t\treturn redirect(url_for('auth'))\n\taccountUpdated = False\n\tprofileJSON = spotify.get_users_profile(session['auth_header'])\n\tprofileID = profileJSON[\"id\"]\n\tAccountInfo = {}\n\t#If the form submitted\n\tif (request.method == 'POST'):\n\t\t#Fill Account Info\n\t\tAccountInfo[\"name\"] = request.form.get(\"name\")\n\t\tAccountInfo[\"email\"] = request.form.get(\"email\")\n\t\tAccountInfo[\"phone\"] = request.form.get(\"number\")\n\t\tAccountInfo[\"gender\"] = request.form.get(\"gender\")\n\t\tAccountInfo[\"preference\"] = request.form.get(\"preference\")\n\t\t#Update Info in database\n\t\tdb.child(\"users\").child(profileID).set(AccountInfo)\n\t\taccountUpdated = True\n\telse:\n\t\t#Get Account Info\n\t\tQueryList = db.child(\"users\").child(profileID).get()\n\t\tAccountInfo = QueryList[0]\n\t\tif (AccountInfo == None):\n\t\t\tAccountInfo = {}\n\treturn render_template(\"accountInfo.html\", accountUpdated=accountUpdated, AccountInfo=AccountInfo)\n\n@app.route(\"/updateAccountMatch\")\ndef updateAccountMatch():\n\tif (session['auth_header'] == None):\n\t\treturn redirect(url_for('auth'))\n\n@app.route(\"/getAccountData\")\ndef getAccountData():\n\tif (session['auth_header'] == None):\n\t\treturn redirect(url_for('auth'))\n\n#----------------END Account Sync\n\n#View Other Accounts\n\n@app.route(\"/account\")\ndef account():\n\t#Get query key value pair for userID\n\tprofileID = request.args.get(\"id\")\n\tif (profileID == None):\n\t\treturn str(\"Profile ID Required\")\n\t#Grab account info\n\tQueryList = db.child(\"users\").child(profileID).get()\n\tAccountInfo = QueryList.val()\n\tif (AccountInfo == None):\n\t\treturn str(\"This account doesn't exist in our database\")\n\telse:\n\t\treturn str(AccountInfo)\n\n#----------------End View\n\n@app.route(\"/firebaseTest\")\ndef fbTest():\n\tnew_event = {\"Test\": \"Value\"}\n\tdb.child(\"events\").child(\"0\").set(new_event)\n\treturn str(new_event)\n\n#This page is how to pass key/value pairs to the server side\n@app.route(\"/validate\")\ndef validate():\n\tusername = request.args.get(\"username\")\n\tpassword = request.args.get(\"password\")\n\treturn str(\"username: \"+username+\" password: \"+password)\n\n#This page shows how to export json files\n@app.route(\"/jsontest\")\ndef makejson():\n\td = {'car': {'color': 'red', 'make': 'Nissan', 'model': 'Altima'}}\n\treturn jsonify(d)\n\nif __name__ == \"__main__\":\n\tapp.run(debug=True, port=spotify.PORT)\n","sub_path":"flaskapp.py","file_name":"flaskapp.py","file_ext":"py","file_size_in_byte":5637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"247645383","text":"# coding=utf-8\nimport sqlite3\n\n\nclass Province:\n    def __init__(self, nr, id, name):\n        self.nr = nr\n        self.id = id\n        self.name = name\n\n    def __str__(self):\n        return '{}. {}'.format(self.nr, self.name.encode(\"utf-8\"))\n\n\nclass City:\n    def __init__(self, province, city):\n        self.province_id = province.id\n        self.province = province.name\n        self.district = city[1]\n        self.commune = city[2]\n        self.city = city[3]\n\n    def as_string(self):\n        return u'Województwo: {}, powiat: {}, miejscowość: {}, typ: {}' \\\n            .format(self.province, self.district, self.city, self.commune)\n\n\nclass DB:\n    def __init__(self):\n        self.db = sqlite3.connect(\"utils/teryt.sqlite\")\n\n    def get_province_list(self):\n        query = self.db.execute('select WOJ, NAZWA from TERC where POW=\"\"')\n        cursor = query.fetchall()\n        provinces = []\n        for i, province in enumerate(cursor):\n            provinces.append(Province(i + 1, province[0], province[1]))\n        query.close()\n        return provinces\n\n    def search_city(self, province, name):\n        query = self.db.execute('select WOJ, POW, NAZDOD, NAZWA from TERC where WOJ=(?) and NAZWA like (?) and POW!=\"\"',\n                                (province.id, name + '%',))\n        cursor = query.fetchall()\n        cities = []\n        for city in cursor:\n            if len(city[2]) > 0:\n                target = [\n                    province.name,\n                    self.search_district_name(province, city[1]),\n                    city[2],\n                    city[3]\n                ]\n                cities.append(City(province, target))\n        query.close()\n        return cities\n\n    def search_district_name(self, province, id):\n        query = self.db.execute('select NAZWA from TERC where WOJ=(?) and GMI!=\"\" and POW=(?)', (province.id, id,))\n        cursor = query.fetchall()\n        name = cursor[0][0]\n        query.close()\n        return name\n\n    def search_for_trouble(self, province, city):\n        troublemakers = []\n        target = city\n        while True:\n            query = self.db.execute('select KOD, NAZWA from SKARB where WOJ=(?) and NAZWA like (?)',\n                                    (province, u'%{}%'.format(target),))\n            cursor = query.fetchall()\n            if len(cursor) > 0:\n                for x in cursor:\n                    troublemakers.append([x[0], x[1]])\n            query.close()\n            if len(troublemakers) > 0:\n                return troublemakers\n            target = target[:-1]\n","sub_path":"utils/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"348536549","text":"#-*- coding: utf-8 -*-\r\nfrom django.contrib import admin\r\nfrom produtos.models import Aplicacao, Produto, Galeria, Faq\r\n\r\n\r\n\r\n\r\n\r\nclass AdminAplicacao(admin.ModelAdmin):\r\n\tlist_display = ('aplicacao',)\r\n\tlist_filter  = ('aplicacao',)\r\nadmin.site.register(Aplicacao, AdminAplicacao)\r\n\r\n\r\n\r\n\r\n\r\nclass AdminProduto(admin.ModelAdmin):\r\n\tfieldsets = [\r\n\t\t(None,\t{'fields':['aplicacao', 'posicao', 'imagem', 'produto', \r\n\t\t\t'descricao', 'ficha', 'aplicacoes']}),\r\n\t\t('SEO',\t{'fields': ['marcador'], 'classes': ['collapse']}),\r\n\t]\r\n\r\n\tlist_display = ('produto', 'marcador',)\r\n\tlist_filter  = ('produto', 'marcador',)\r\nadmin.site.register(Produto, AdminProduto)\r\n\r\n\r\n\r\n\r\n\r\nclass AdminGaleria(admin.ModelAdmin):\r\n\tlist_display = ('produto', 'fotografia', 'video', 'publicada_em',)\r\n\tlist_filter  = ('produto', 'fotografia', 'video', 'publicada_em',)\r\nadmin.site.register(Galeria, AdminGaleria)\r\n\r\n\r\nadmin.site.register(Faq)","sub_path":"produtos/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"394211165","text":"import json\nimport logging\nimport os\nfrom typing import Dict, Any, Optional, Tuple\n\nfrom twilio.base.exceptions import TwilioException\nfrom twilio.rest import Client\n\nlogger = logging.getLogger(__name__)\n\n\nclass Action:\n    def __init__(self, invocation_event: Dict[str, Any]):\n        self.__invocation_event: Dict[str, Any] = invocation_event\n        self.__parameters: Dict[str, Any] = invocation_event['ResourceProperties']\n        self.__resource_id: Optional[str] = invocation_event.get('PhysicalResourceId')\n\n        try:\n            self.TWILIO_ACCOUNT_SID = os.environ['TWILIO_ACCOUNT_SID']\n            self.TWILIO_AUTH_TOKEN = os.environ['TWILIO_AUTH_TOKEN']\n            self.TWILIO_WORKSPACE_SID = os.environ['TWILIO_WORKSPACE_SID']\n            self.TWILIO_TASK_QUEUE_NAME = self.__parameters['TwilioTaskQueueName']\n        except KeyError as ex:\n            logger.error(f'Missing environment: {repr(ex)}.')\n            raise\n\n        self.client = self.__get_twilio_client(self.TWILIO_ACCOUNT_SID, self.TWILIO_AUTH_TOKEN)\n\n    def create(self) -> Tuple[Optional[Dict[Any, Any]], Optional[str]]:\n        \"\"\"\n        Creates a resource.\n\n        :return: A tuple containing two items:\n            1. Custom data to return back to CloudFormation service.\n            2. Physical resource id (can be empty).\n        \"\"\"\n        logger.info(f'Initiating resource creation with these parameters: {json.dumps(self.__parameters)}.')\n\n        task_queue = self.client.taskrouter.workspaces.get(self.TWILIO_WORKSPACE_SID).task_queues.create(self.TWILIO_TASK_QUEUE_NAME)\n        task_queue_sid = task_queue.sid\n\n        return {'TaskQueueSid': task_queue_sid}, task_queue_sid\n\n    def update(self) -> Tuple[Optional[Dict[Any, Any]], Optional[str]]:\n        \"\"\"\n        Updates a resource.\n\n        :return: A tuple containing two items:\n            1. Custom data to return back to CloudFormation service.\n            2. Physical resource id (can be empty).\n        \"\"\"\n        logger.info(f'Initiating resource update with these parameters: {json.dumps(self.__parameters)}.')\n\n        task_queue_sid = self.__resource_id\n        task_queue = self.client.taskrouter.workspaces.get(self.TWILIO_WORKSPACE_SID).task_queues.get(task_queue_sid).fetch()\n\n        if task_queue.friendly_name != self.TWILIO_TASK_QUEUE_NAME:\n            task_queue.update(friendly_name=self.TWILIO_TASK_QUEUE_NAME)\n\n        return {'TaskQueueSid': task_queue_sid}, task_queue_sid\n\n    def delete(self) -> Tuple[Optional[Dict[Any, Any]], Optional[str]]:\n        \"\"\"\n        Deletes a resource.\n\n        :return: A tuple containing two items:\n            1. Custom data to return back to CloudFormation service.\n            2. Physical resource id (can be empty).\n        \"\"\"\n        logger.info(f'Initiating resource deletion with these parameters: {json.dumps(self.__parameters)}.')\n\n        task_queue_sid = self.__resource_id\n        self.client.taskrouter.workspaces.get(self.TWILIO_WORKSPACE_SID).task_queues.get(task_queue_sid).delete()\n\n        return {'TaskQueueSid': task_queue_sid}, task_queue_sid\n\n    @staticmethod\n    def __get_twilio_client(account_sid: str, auth_token: str) -> Client:\n        \"\"\"\n        Creates a Twilio Client.\n\n        :return: Twilio Client.\n        \"\"\"\n        try:\n            return Client(username=account_sid, password=auth_token)\n        except TwilioException as ex:\n            logger.error(f'Could not create Twilio client. Reason: {repr(ex)}.')\n            raise\n","sub_path":"b_cfn_twilio_task_queue/source/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":3486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"440744169","text":"# _*_ encoding: utf-8 _*_\n__author__ = 'wjk'\n__date__ = '2019/9/2 15:59'\n\nimport pandas as pd\n\n\n# drop_min_atom是去掉数量较少的原子类型\ndef select_atom_type(file, se_atom_type, total_num, drop_min_atom, charge_min, charge_max):\n\n\n    data = pd.read_csv(file).iloc[0:total_num, ]\n    print(data.shape)\n    #  去掉'cl'，并reset_index\n    data = data.drop(data[data.atom_type == 'cl'].index).reset_index(drop=True)\n\n    # print(data['atom_type'].describe())\n\n    # 前15种，占总数的94%���舍弃其他\n    # print(data['atom_type'].value_counts()[:15].sum() / 11750)\n    #\n    # print(data['atom_type'].value_counts()[:15])\n\n    print('去掉cl以后：', data.shape)\n\n    # all_kind = data['atom_type'].value_counts().index.tolist()\n    # 选择原子类型\n    drop_kind = data['atom_type'].value_counts()[drop_min_atom:].index.tolist()\n\n    # select_kind = data['atom_type'].value_counts()[0:drop_atom_type - 1].index.tolist()\n    # 当atom_type_seq大于1时，用这个\n    # delete_kind.extend(select_kind)\n    # 当atom_type_seq等于1时，用这个\n    # delete_kind.append(select_kind[0])\n\n    #  去掉异常atom，并reset_index\n    data = data.drop(data[data.atom_type.isin(drop_kind)].index).reset_index(drop=True)\n    print('去掉少数atom以后：', data.shape)\n\n    # 将同一元素合并\n    delete_kind = []\n    #  去掉其他atom，并reset_index\n    atom_type_names = data['atom_type'].value_counts()[0:].index.tolist()\n    for item in atom_type_names:\n        a = item[0]\n        if item[0] != se_atom_type:\n            delete_kind.append(item)\n    data = data.drop(data[data.atom_type.isin(delete_kind)].index).reset_index(drop=True)\n    print('去掉其他atom以后：', data.shape)\n    '''\n    charge异常值处理\n    '''\n    # C.ar处理异常值，去掉 lr=0.00001  LOSS=0.0020\n    # data = data[data['charge'] > -0.07]\n    #\n    # data = data[data['charge'] < 0.25]\n\n    # # H处理异常值，去掉 (31137, 115) 4.0220e-04\n    data = data[data['charge'] > charge_min]\n\n    data = data[data['charge'] < charge_max]\n\n    # O.2 lr=0.00005 lr=0.00005 LOOS=4.5794e-05\n    # data = data[data['charge'] > -0.5]\n    # data = data[data['charge'] < -0.1]\n\n    # # N.am (8152, 115) (lr=0.0001) lr=0.00005 loose=2.5326e-05\n    # data = data[data['charge'] > -0.33]\n    #\n    # data = data[data['charge'] < -0.2]\n\n    # C.3 random_seed=2 lr=0.00005 LOSS = 6.2010e-05，val_mean_absolute_error: 0.0063 交叉验证# [0.0008005221155932078, 0.019119028501428555]\n    # data = data[data['charge'] > -0.06]\n    # #-0.06~0.15\n    # data = data[data['charge'] < 0.15]\n\n    print(data.shape)\n    return data","sub_path":"feature_engineering/select_atom_type.py","file_name":"select_atom_type.py","file_ext":"py","file_size_in_byte":2643,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"38913709","text":"#!/usr/bin/python3\n\"\"\"\nScript that starts a Flask web application\n\"\"\"\nfrom flask import Flask\nfrom flask import render_template\nfrom models import storage\n\napp = Flask(__name__)\n\n\n@app.route('/states', strict_slashes=False)\n@app.route('/states/<id>', strict_slashes=False)\ndef states_id(id=None):\n    \"\"\"view function that displays the state/cities objects with a given id\"\"\"\n    all_states = storage.all(\"State\").values()\n    state_found = None\n    if id is not None:\n        for state in all_states:\n            if state.id == id:\n                state_found = state\n    return render_template('9-states.html', all_states=all_states, id=id,\n                           state_found=state_found)\n\n\n@app.teardown_appcontext\ndef teardown(self):\n    \"\"\"function that removes the current SQLAlchemy Session\"\"\"\n    storage.close()\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=5000)\n","sub_path":"web_flask/9-states.py","file_name":"9-states.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"119849746","text":"import minimalmodbus\r\nimport serial\r\nimport Smart_Sensor_IPSO as IPSO\r\nimport Smart_Sensor_Registers as ss_reg\r\nfrom time import sleep\n\nclass SmartSensor:\r\n   \r\n\r\n    def __init__(self, port, address = 1, debug = False):\r\n        '''\r\n        Establish a Smart Sensor connection using Smart Sensor USB adaptor\r\n        '''\r\n        global Device\r\n        minimalmodbus.BAUDRATE = 38400\r\n        minimalmodbus.PARITY = serial.PARITY_EVEN\r\n        minimalmodbus.TIMEOUT = 0.5\r\n        Device = minimalmodbus.Instrument(port, address)\r\n        #print(Device)\n        if (debug == True):\r\n            Device.debug = True\r\n            print(\"Device Setup\", Device)\r\n\r\n    def Device_ID(self,Device):\r\n        '''\r\n        Returns the Device ID as a 64 bit value\r\n        '''\r\n        sleep(float(0.03))\n        xx = Device.read_registers(ss_reg.Register(\"DEVICE_ID\"), 4)\r\n        value = xx[0]\r\n        value = (value << 16) + xx[1]\r\n        value = (value << 16) + xx[2]\r\n        value = (value << 16) + xx[3]\r\n        return value\r\n\r\n    def Device_Type(self,Device):\r\n        '''\r\n        Returns the Device Type string name\r\n        '''\r\n        \n        sleep(float(0.03))\n        xx = Device.read_registers(ss_reg.Register(\"DEVICE_NAME_STRING\"),8)\r\n        name_bytes = str((chr(xx[0] & 0xff))) + str((chr(xx[1] >> 8))) + str((chr(xx[1] & 0xff))) + str((chr(xx[2] >> 8))) + str((chr(xx[2] & 0xff))) + str((chr(xx[3] >> 8))) + str((chr(xx[3] & 0xff))) + str((chr(xx[4] >> 8))) + str((chr(xx[4] & 0xff)))\n\t#print(\"NameBytes\")\n        #print(name_bytes)\r\n        return (name_bytes)\r\n\r\n    def Firmware_Version(self,Device):\r\n        '''\r\n        Returns the Hardware version as 'xxx.xxx.xxx.xxx'\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_long(ss_reg.Register(\"FIRMWARE_VERSION\"))\r\n        return Serial_Number_To_String(xstr)\r\n       \r\n    def Core_Version(self,Device):\r\n        '''\r\n        Returns the Core version as 'xxx.xxx.xxx.xxx'\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_long(ss_reg.Register(\"CORE_VERSION\"))\r\n        return Serial_Number_To_String(xstr)\r\n       \r\n    def Hardware_Version(self,Device):\r\n        '''\r\n        Returns the Hardware version as 'xxx.xxx.xxx.xxx'\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_long(ss_reg.Register(\"HARDWARE_VERSION\"))\r\n        return Serial_Number_To_String(xstr)\r\n\r\n    def User_Hours(self,Device):\r\n        '''\r\n        Returns the number of hours since last reset\r\n        '''\r\n        sleep(float(0.03))\n        return (Device.read_register(ss_reg.Register(\"USER_HOURS\")))\r\n\r\n    def Manufactured_Date(self,Device):\r\n        '''\r\n        Returns the Manufactured date as \"YYYY/MN/DY\"\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"MANUFACTURED_DATE\"))\r\n        return Date_To_String(xstr)\r\n        \r\n\r\n    def Operating_Time(self,Device):\r\n        '''\r\n        Returns the total time under power as DDDDD-HH:MM:SS\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_long(ss_reg.Register(\"OPERATING_TIME\"))\r\n        return Time_To_String(xstr)\r\n\r\n    def Calibration_Date(self,Device):\r\n        '''\r\n        Returns the Calibraion date as \"YYYY/MN/DY\"\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"CALIBRATION_DATE\"))\r\n        return Date_To_String(xstr)\r\n\r\n    def Calibration_Time(self,Device):\r\n        '''\r\n        Returns the total time since calibration as DDDDD-HH:MM:SS\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_long(ss_reg.Register(\"CALIBRATION_TIME\"))\r\n        return Time_To_String(xstr)\r\n        \r\n    def Event_0_Timebase(self,Device):\r\n        ''' \r\n        Returns Event 0 timebase, typically the number of seconds between sensor updates\r\n        '''\r\n        sleep(float(0.03))\n        return (Device.read_register(ss_reg.Register(\"EVENT_0_TIMEBASE\")))\r\n\r\n    def Event_1_Timebase(self,Device):\r\n        ''' \r\n        Returns Event 1 timebase\r\n        '''\r\n        sleep(float(0.03))\n        return (Device.read_register(ss_reg.Register(\"EVENT_1_TIMEBASE\")))\r\n\r\n    def Number_Of_Sensors(self,Device):\r\n        '''\r\n        Returns the number of active sensors\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"NUMOFSENSORS_OUTPUTS\"))\r\n        return ((xstr >> 8) & 0xff)\r\n\r\n    def Number_Of_Outputs(self,Device):\r\n        '''\r\n        Returns the number of active outputs\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"NUMOFSENSORS_OUTPUTS\"))\r\n        return (xstr & 0xff)\r\n\r\n    def Operating_Voltage(self,Device):\r\n        '''\r\n        Returns the number of active outputs\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"OPERATING_TEMP_VOLTAGE\"))\r\n        return ((xstr & 0xff) / 10)\r\n\r\n    def Operating_Temperature(self,Device):\r\n        '''\r\n        Returns the number of active outputs\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"OPERATING_TEMP_VOLTAGE\"))\r\n        return ((xstr >> 8) & 0xff)\r\n\r\n    def Fault_Process(self,Device):\r\n        '''\r\n        Returns the number of active outputs\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"FAULT_PROCESS_CODE\"))\r\n        return ((xstr >> 8) & 0xff)\r\n\r\n    def Fault_Code(self,Device):\r\n        '''\r\n        Returns the number of active outputs\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_register(ss_reg.Register(\"FAULT_PROCESS_CODE\"))\r\n        return ((xstr >> 8) & 0xff)\r\n\r\n    def Current_Time(self,Device):\r\n        '''\r\n        Returns the current time as DDDDD-HH:MM:SS\r\n        '''\r\n        sleep(float(0.03))\n        xstr = Device.read_long(ss_reg.Register(\"CURRENT_TIME\"))\r\n        return Time_To_String(xstr)\r\n\r\n    def Device_Name(self,Device):\r\n        sleep(float(0.03))\n        xstr = Device.read_string(ss_reg.Register(\"DEVICE_NAME\"), 16)\r\n        return (xstr)\r\n\r\n    # Sensor Information\r\n    #-------------------\r\n\r\n    def Sensor_Name(self, sensor,Device):\r\n        '''\r\n        Return the user assigned name of a sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_0_NAME\"),8)\r\n        elif (sensor == 1):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_1_NAME\"),8)\r\n        elif (sensor == 2):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_2_NAME\"),8)\r\n        elif (sensor == 3):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_3_NAME\"),8)\r\n\r\n    def Output_Name(self, sensor,Device):\r\n        '''\r\n        Return the user assigned name of a sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_string(ss_reg.Register(\"OUTPUT_0_NAME\"),8)\r\n        elif (sensor == 1):\r\n            return Device.read_string(ss_reg.Register(\"OUTPUT_1_NAME\"),8)\r\n        elif (sensor == 2):\r\n            return Device.read_string(ss_reg.Register(\"OUTPUT_2_NAME\"),8)\r\n        elif (sensor == 3):\r\n            return Device.read_string(ss_reg.Register(\"OUTPUT_3_NAME\"),8)\r\n\r\n    def Sensor_Min_Value(self, sensor,Device):\r\n        '''\r\n        Return the minimum VALUE measured by the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_MINVALUE\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_MINVALUE\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_2_MINVALUE\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_3_MINVALUE\"))\r\n        else:\r\n            return (none)\r\n        end\r\n\r\n\r\n    def Sensor_Max_Value(self, sensor,Device):\r\n        '''\r\n        Return the maximum Value measured by the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_MAXVALUE\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_MAXVALUE\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_2_MAXVALUE\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_3_MAXVALUE\"))\r\n        else:\r\n            return (none)\r\n \r\n    def Sensor_Min_Range(self, sensor,Device):\r\n        '''\r\n        Return the minimum RANGE measured by the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_MINRANGE\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_MINRANGE\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_2_MINRANGE\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_3_MINRANGE\"))\r\n         \r\n    def Sensor_Max_Range(self, sensor,Device):\r\n        '''\r\n        Return the maximum RANGE measured by the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_MAXRANGE\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_MAXRANGE\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_2_MAXRANGE\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_3_MAXRANGE\"))\r\n        else:\r\n            return (none)\r\n \r\n    def Sensor_Precision(self, sensor,Device):\r\n        '''\r\n        Return the PRECISION measured by the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_register(ss_reg.Register(\"SENSOR_0_PRECISION\"))\r\n        elif (sensor == 1):\r\n            return Device.read_register(ss_reg.Register(\"SENSOR_1_PRECISION\"))\r\n        elif (sensor == 2):\r\n            return Device.read_register(ss_reg.Register(\"SENSOR_2_PRECISION\"))\r\n        elif (sensor == 3):\r\n            return Device.read_register(ss_reg.Register(\"SENSOR_3_PRECISION\"))\r\n        \r\n    def Sensor_Measurement(self, sensor,Device):\r\n        '''\r\n        Return the meaurement type of the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return IPSO.Measurement_Type(Device.read_register(ss_reg.Register(\"SENSOR_0_IPSO_TYPE\")))\r\n        elif (sensor == 1):\r\n            return IPSO.Measurement_Type(Device.read_register(ss_reg.Register(\"SENSOR_1_IPSO_TYPE\")))\r\n        elif (sensor == 2):\r\n            return IPSO.Measurement_Type(Device.read_register(ss_reg.Register(\"SENSOR_2_IPSO_TYPE\")))\r\n        elif (sensor == 3):\r\n            return IPSO.Measurement_Type(Device.read_register(ss_reg.Register(\"SENSOR_3_IPSO_TYPE\")))\r\n\r\n    def Sensor_Max_Range(self, sensor,Device):\r\n        '''\r\n        Return the maximum RANGE measured by the sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_MAXRANGE\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_MAXRANGE\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_2_MAXRANGE\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_3_MAXRANGE\"))\r\n        else:\r\n            return (none)\r\n \r\n    def Sensor_Scale_Gain(self, sensor,Device):\r\n        '''\r\n        Return the GAIN scaling factor for the specified sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_SCALE_GAIN\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_SCALE_GAIN\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_SCALE_GAIN\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_SCALE_GAIN\"))\r\n       \r\n    def Sensor_Scale_Offset(self, sensor,Device):\r\n        '''\r\n        Return the GAIN scaling factor for the specified sensor\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_SCALE_OFFSET\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_SCALE_OFFSET\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_SCALE_OFFSET\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_SCALE_OFFSET\"))\r\n      \r\n        \r\n    def Sensor_Reading(self, sensor,Device):\r\n        '''\r\n        Read a selected sensor reading\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_0_DATA\"))\r\n        elif (sensor == 1):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_1_DATA\"))\r\n        elif (sensor == 2):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_2_DATA\"))\r\n        elif (sensor == 3):\r\n            return Device.read_float(ss_reg.Register(\"SENSOR_3_DATA\"))\r\n        else:\r\n            return (none)\r\n        \n    def wait_system_ready(Device, max_wait=3):\n        \"\"\"\n            Poll sensor until it is ready, optional timeout value\n        :param max_wait: timeout in seconds\n        \"\"\"\n        wait_time = 0.2\n        while max_wait > 0:\n            try:\n                s = Device.read_register(0xf012)\n                #if Device.device_ready:\n                #    break\n            except:\n                pass\n            time.sleep(wait_time)\n            max_wait -= wait_time\n\n        \n    def SoftReset(self, sensor, Device):\n        \"\"\"\n            Soft Reset the sensor\n        \"\"\"\n\n        Device.write_register(0xf013, 4)\n        Device.wait_system_ready()\n\n    def FactoryReset(self, sensor, Device):\n        \"\"\"\n            Factory reset the sensor\n        \"\"\"\n        Device.write_register(0xf013, 5)\n        Device.wait_system_ready()\n        \n    def PowerReset(sensor, Device):\n        \"\"\"\n            Power Reset the sensor\n        \"\"\"\n\n        Device.write_register(0xf013, 6)\n        Device.wait_system_ready()\n\n    def Sensor_Reset(self, sensor,ResetType, Device):\n        if (ResetType == 0):\n            FactoryReset(sensor, Device)\n        if (ResetType == 1):\n            PowerReset(sensor, Device)\n        if (ResetType == 2):\n            SoftReset(sensor, Device)\n            \n\n        \n    def Output_Data(self, sensor, value,Device):\r\n        '''\r\n        Read a selected sensor reading\r\n        '''\r\n        sleep(float(0.03))\n        print(\"OutputSensor \" + str(sensor) + \"Value \" + str(value))\n        if (sensor == 0):\r\n            return Device.write_float(ss_reg.Register(\"OUTPUT_0\"), value)\r\n        elif (sensor == 1):\r\n            return Device.write_float(ss_reg.Register(\"OUTPUT_1\"), value)\r\n        elif (sensor == 2):\r\n            return Device.write_float(ss_reg.Register(\"OUTPUT_2\"), value)\r\n        elif (sensor == 3):\r\n            return Device.write_float(ss_reg.Register(\"OUTPUT_3\"), value)\r\n        else:\r\n            return (none)\r\n \r\n    def Sensor_Units(self, sensor,Device):\r\n        '''\r\n        Read the selected sensor units\r\n        '''\r\n        sleep(float(0.03))\n        if (sensor == 0):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_0_UNITS\"),2)\r\n        elif (sensor == 1):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_1_UNITS\"),2)\r\n        elif (sensor == 2):\r\n            return Device.read_string(ss_reg.Register(\"SENSOR_2_UNITS\"),2)\r\n        elif (sensor == 3):\r\n             return Device.read_string(ss_reg.Register(\"SENSOR_3_UNITS\"),2)\r\n        else:\r\n            return (none)\r\n \r\n\r\n    pass\r\n\r\n\r\n\r\ndef Serial_Number_To_String(serial_num):\r\n    '''\r\n    Converts serial number to string format (xxx.xxx.xxx.xxx)\r\n    '''\r\n    return('{0}.{1}.{2}.{3}'.format(((serial_num >> 24) & 0xff),\r\n                                        ((serial_num >> 16) & 0xff),\r\n                                        ((serial_num >> 8) & 0xff),\r\n                                        ((serial_num >> 0) & 0xff)))\r\n\r\ndef Date_To_String(date):\r\n    ''' \r\n    Converts a date to a string format (YYYY/MM/DD)\r\n    '''\r\n    str = '{0:0>4}/{1:0>2}/{2:0>2}'.format((((date>> 9) & 0xff) + 2000),\r\n                                            ((date >> 5) & 0x0f),\r\n                                            (date & 0x1f))\r\n    return (str)\r\n\r\ndef Time_To_String(xstr):\r\n    '''\r\n    Converts a time to string format (DDD-HR:MN:SS)\r\n    '''\r\n    secs = int(xstr % 60)\r\n    xstr = xstr / 60\r\n    mins = int(xstr % 60)\r\n    xstr = xstr / 60\r\n    hours = int(xstr % 24)\r\n    days = int(xstr / 24)\r\n    str = '{0}-{1:0>2}:{2:0>2}:{3:0>2}'.format(days, hours, mins, secs)\r\n    return (str)\r\n\r\n\r\n","sub_path":"avnet-iot/IoTConnect/sample/Smart_Sensor/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":17102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"327953486","text":"import pigo\nimport time\nimport random\n\n'''\nMR. A's Final Project Student Helper\n'''\n\nclass GoPiggy(pigo.Pigo):\n\n    ########################\n    ### CONTSTRUCTOR - this special method auto-runs when we instantiate a class\n    #### (your constructor lasted about 9 months)\n    ########################\n\n    def __init__(self):\n        print(\"Your piggy has be instantiated!\")\n        # Our servo turns the sensor. What angle of the servo( ) method sets it straight?\n        self.MIDPOINT = 106\n        # YOU DECIDE: How close can an object get (cm) before we have to stop?\n        self.STOP_DIST = 30\n        # YOU DECIDE: What left motor power helps straighten your fwd()?\n        self.LEFT_SPEED = 120\n        # YOU DECIDE: What left motor power helps straighten your fwd()?\n        self.RIGHT_SPEED = 120\n        # This one isn't capitalized because it changes during runtime, the others don't\n        self.turn_track = 0\n        # Our scan list! The index will be the degree and it will store distance\n        self.scan = [None] * 180\n        self.set_speed(self.LEFT_SPEED, self.RIGHT_SPEED)\n        # let's use an event-driven model, make a handler of sorts to listen for \"events\"\n        while True:\n            self.stop()\n            self.menu()\n\n\n    ########################\n    ### CLASS METHODS - these are the actions that your object can run\n    #### (they can take parameters can return stuff to you, too)\n    #### (they all take self as a param because they're not static methods)\n    ########################\n\n\n    ##### DISPLAY THE MENU, CALL METHODS BASED ON RESPONSE\n    def menu(self):\n        ## This is a DICTIONARY, it's a list with custom index values\n        # You may change the menu if you'd like to add an experimental method\n        menu = {\"n\": (\"Navigate forward\", self.nav),\n                \"d\": (\"Dance\", self.dance),\n                \"c\": (\"Calibrate\", self.calibrate),\n                \"t\": (\"Turn test\", self.turn_test),\n                \"s\": (\"Check status\", self.status),\n                \"o\": (\"Count Obstacles\", self.count_obstacles),\n                \"q\": (\"Quit\", quit)\n                }\n\n        # loop and print the menu...\n        for key in sorted(menu.keys()):\n            print(key + \":\" + menu[key][0])\n        # store the user's answer\n        ans = raw_input(\"Your selection: \")\n        # activate the item selected\n        menu.get(ans, [None, error])[1]()\n\n    def count_obstacles(self):\n        # run a scan\n        self.wide_scan()\n        # count how many obstacles I've found\n        counter = 0\n        # starting state assumes no obstacle\n        found_something = False\n        # loop through all my scan data\n        for x in self.scan:\n            # if x is not None and really close\n            if x and x < self.STOP_DIST:\n                # If I've already found something\n                if found_something:\n                    print(\"obstacles continues\")\n                    # if this is a ne obstacle\n                else:\n                    # switch my tracker\n                    found_something = True\n                    print(\"start of new obstacle\")\n            # if my data show safe distances...\n            if x and x > self.STOP_DIST:\n                # if my tracker has been triggered...\n                if found_something:\n                    print(\"end of obstacle\")\n                    # reset tracker\n                    found_something = False\n                    # increase count of obstacles\n                    counter += 1\n        print('Total number of obstacles in this scan: ' + str(counter))\n        return counter\n\n    def turn_test(self):\n        while True:\n            ans = raw_input('Turn right, left or stop? (r/l/s): ')\n            if ans == 'r':\n                val = int(raw_input('/nBy how much?: '))\n                self.encR(val)\n            elif ans == 'l':\n                val = int(raw_input('/nBy how much?: '))\n                self.encL(val)\n            else:\n                break\n        self.restore_heading()\n\n    def restore_heading(self):\n        print(\"Now I'll turn back to the starting position\")\n        # make self.turn_track go back to zero\n        if self.turn_track > 0:\n            print('I must have turned right a lot now I should turn left')\n            self.encL(abs(self.turn_track))\n\n        elif self.turn_track < 0:\n            print('I must have turned left a lot no I should turn tp self.encR(??)')\n            self.encR(abs(self.turn_track))\n\n\n\n\n\n    #YOU DECIDE: How does your GoPiggy dance?\n    def dance(self):\n        print(\"Piggy dance\")\n        ##### WRITE YOUR FIRST PROJECT HERE\n        self.shimmy()\n        self.wow()\n        self.head_shake()\n        # self.back_it_up()\n\n    def shimmy(self):\n        print(\"shimmy\")\n        for x in range(1):\n            self.servo(50)\n            self.encR(40)\n            self.servo(140)\n            self.encL(60)\n\n    def wow(self):\n        print(\"wow\")\n        for x in range(1):\n            self.encR(90)\n            self.servo(35)\n            self.encL(95)\n            self.encR(65)\n            self.encF(18)\n            self.servo(30)\n            self.encL(60)\n            self.encB(18)\n\n    def head_shake(self):\n        for x in range(6):\n            self.servo(30)\n            self.servo(150)\n        self.servo(self.MIDPOINT)\n\n\n    ########################\n    ### MAIN LOGIC LOOP - the core algorithm of my navigation\n    ### (kind of a big deal)\n    ########################\n\n    def nav(self):\n        print(\"-----------! NAVIGATION ACTIVATED !------------\\n\")\n        print(\"[ Press CTRL + C to stop me, then run stop.py ]\\n\")\n        print(\"-----------! NAVIGATION ACTIVATED !------------\\n\")\n        # this is the loop part of the \"main logic loop\"\n        count = 0\n        while True:\n            if self.is_clear():\n                self.encF(35)\n                count += 1\n            # trying to make robot move backwards when locating obstacle\n            if self.dist() < self.STOP_DIST:\n                self.encB(5)\n            if count > 5 and self.turn_track != 0:\n                self.restore_heading()\n                count = 0\n            answer = self.choose_path()\n            if answer == \"left\":\n                self.encL(6)\n            elif answer == \"right\":\n                self.encR(6)\n                # trying to change navigation\n                # trying to make the robot move further when clear\n\n    def maneuver(self):\n        # I have turned right and need to check my left side\n        if self.turn_track > 0:\n            while self.is_clear():\n                # go forward a little bit\n                self.encF(5)\n            # look left\n            self.servo(self.MIDPOINT + 60)\n            # see if it's above self.STOP_DIST + 20\n            if self.dist() > self.STOP_DIST + 20:\n                # restore_heading\n                self.restore_heading()\n                # return\n                return\n            # look straight ahead again\n            self.servo(self.MIDPOINT)\n            # I have turned left and need to check my right side\n\n    def cruise(self):\n        self.fwd()  # I added this to pigo\n        while self.is_clear():\n            time.sleep(.1)\n        self.stop()\n        self.encB(3)\n\n\n    def encR(self, enc):\n        pigo.Pigo.encR(self, enc)\n        self.turn_track += enc\n\n    def encL(self, enc):\n       pigo.Pigo.encL(self, enc)\n       self.turn_track -= enc\n\n\n\n\n####################################################\n############### STATIC FUNCTIONS\n\ndef error():\n    print('Error in input')\n\n\ndef quit():\n    raise SystemExit\n\n# The app starts right here when we instantiate our GoPiggy\n\ntry:\n    g = GoPiggy()\nexcept (KeyboardInterrupt, SystemExit):\n    from gopigo import *\n    stop()","sub_path":"student.py","file_name":"student.py","file_ext":"py","file_size_in_byte":7746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"620930747","text":"# Chris Peabody cgpzbd@mst.edu\r\n# Sokoban puzzle\r\n# CS 5400, Artificial intelligence, F16\r\n# Assignment 2 (GrBeFGS FUNCTION)\r\n\r\nimport queue\r\nimport node\r\n\r\ndef GrBeFGS(state):\r\n\t# Make the frontier and put the starting state in\r\n\tfrontier = queue.PriorityQueue()\r\n\troot = node.Node(state, '')\r\n\tfrontier.put(root)\r\n\r\n\t# Technically is the explored\r\n\t# AND frontier sets of states\r\n\texplored = []\r\n\r\n\twhile True:\r\n\t\tif frontier.empty():\r\n\t\t\tbreak # None represents a failure\r\n\t\t\r\n\t\tcurNode = frontier.get()\r\n\t\tif curNode.isGoal():\r\n\t\t\treturn curNode\r\n\r\n\t\tfor child in curNode.CHILDREN():\r\n\t\t\tif child.STATE not in explored:\r\n\t\t\t\tfrontier.put(child)\r\n\t\t\t\texplored.append(child.STATE)","sub_path":"Fall-2016/cs5401/as3/grbefgs.py","file_name":"grbefgs.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"112897476","text":"\n# ====================== #\n# lr/bs/mom-scale table  #\n# ====================== #\n\ndef scale_table():\n    name_re = re.compile((\"_\".join([\"{word}\"]*5+[\"{value}\"]*3+[\"{word}\",\"{value}\"]+[\"{value}\"]*3)+\".*\\.json\").format(word=word_re,value=value_re))\n    def name_match_fn(d,m):\n        d[\"expname\"], d[\"net\"], d[\"norm\"], d[\"activation\"], d[\"optimizer\"], d[\"learning_rate\"], d[\"weight_decay\"], d[\"momentum\"], d[\"scheduler\"], d[\"bias\"], d[\"lrscale\"], d[\"momscale\"], d[\"bsscale\"] = m[1], m[2], m[3], m[4], m[5], m[6], m[7], m[8], m[9], m[10], m[11], m[12], m[13]\n    data = get_data(name_re, name_match_fn)\n\n    scalar2d = \"scalar2d-[tm|trd][sinceLast] JI(last,current)\"\n    # scalar2d = \"scalar2d-[tm|trd] % max Entropy\"\n    # scalar2d = \"scalar2d-[tm|trd][sinceLast][>T] JI(last,current)\"\n    data = dict(filter(lambda elem: scalar2d in elem[1], data.items()))\n    keys = list(data.keys())\n\n    lrscales = sorted(list(set([d[\"lrscale\"] for d in data.values()])), key=lambda s: float(s))\n    bsscales = sorted(list(set([d[\"bsscale\"] for d in data.values()])), key=lambda s: float(s))\n    momscales = sorted(list(set([d[\"momscale\"] for d in data.values()])), key=lambda s: float(s))\n\n    # ax_x = 7\n    # ax_y = 7\n    # plots = np.random.choice(keys,ax_x*ax_y)\n    # fig, axs = plt.subplots(ax_y,ax_x, figsize=(10,5))\n\n    vars = [\"lrscales\", \"momscales\"]\n    # vars = [\"lrscales\", \"bsscales\"]\n    # vars = [\"momscales\", \"bsscales\"]\n    var_x = lrscales if \"lrscales\" in vars else momscales\n    var_y = bsscales if \"bsscales\" in vars else momscales\n    str_x = \"learning rate scaling\" if \"lrscales\" in vars else \"momentum scaling\"\n    str_y = \"batch size scaling\" if \"bsscales\" in vars else \"momentum scaling\"\n    lr_str = \"%s\" if \"lrscales\" in vars else \"1.0\"\n    mom_str = \"%s\" if \"momscales\" in vars else \"1.0\"\n    bs_str = \"%s\" if \"bsscales\" in vars else \"1.0\"\n    ax_x = len(var_x)\n    ax_y = len(var_y)\n\n    plots = [\"scalings-c10_convnet_batchnorm_relu_sgd_lr=std_wd=std_mom=std_multistep_bias=std_lrscale=%s_momscale=%s_bsscale=%s.json\" % (lr_str,mom_str,bs_str) % (l,m) for m in var_y for l in var_x]\n    # plots = [\"scalings-c10_resnet20,basicblock_pre_batchnorm_relu_sgd_lr=std_wd=std_mom=std_multistep_bias=std_lrscale=%s_momscale=%s_bsscale=%s.json\" % (lr_str,mom_str,bs_str) % (l,m) for l in var_x for m in var_y]\n    fig, axs = plt.subplots(ax_y,ax_x, figsize=(10,5))\n\n\n    min_z = min([np.array(data[key][scalar2d][\"z\"]).min() for key in plots if key in data])\n    max_z = max([np.array(data[key][scalar2d][\"z\"]).max() for key in plots if key in data])\n    for ax, key in zip(axs.flat,plots):\n        if key not in data:\n            print(key,\"not found\")\n            continue\n        d = data[key]\n        ax.pcolormesh(d[scalar2d][\"x\"],d[scalar2d][\"y\"],d[scalar2d][\"z\"], vmin=min_z, vmax=max_z)\n        # ax.set_title(\"lr=%s mom=%s bs=%s\" % (d[\"lrscale\"], d[\"momscale\"], d[\"bsscale\"]))\n        ax.tick_params(\n            axis='both',          # changes apply to the x-axis\n            which='both',      # both major and minor ticks are affected\n            bottom=False,      # ticks along the bottom edge are off\n            top=False,         # ticks along the top edge are off\n            left=False,         # ticks along the top edge are off\n            right=False,         # ticks along the top edge are off\n            labelbottom=False,\n            labelleft=False\n        ) # labels along the bottom edge are off\n\n\n    fig.add_subplot(111, frameon=False)\n    plt.tick_params(labelcolor='none', top=False, bottom=False, left=False, right=False)\n    for i in range(ax_x):\n        plt.setp(axs[-1, i], xlabel=var_x[i])\n    for i in range(ax_y):\n        plt.setp(axs[i, 0], ylabel=var_y[i])\n    plt.grid(False)\n    plt.xlabel(str_x)\n    plt.ylabel(str_y)\n    plt.title(\"Value: \\\"%s\\\" for %s-vs-%s\" % (scalar2d, str_x, str_y))\n    plt.show()\n\n\n\n","sub_path":"util/divider/plottypes/scaletable.py","file_name":"scaletable.py","file_ext":"py","file_size_in_byte":3866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"239711970","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\ntests.tests_models.test_water_container\n~~~~~~~~~~~~~~~~~~~\nThis script contains tests for the WaterContainer model.\n\"\"\"\nfrom pytest import fixture, mark, raises\n\nfrom src.exceptions import CoffeeMachineException\nfrom src.exceptions import NotEnoughWater\nfrom src.models.container import WaterContainer\nfrom src.utils import Mililiters\n\n\n@fixture\ndef create_water_container(request) -> WaterContainer:\n    \"\"\"Create WaterContainer object for testing.\"\"\"\n    try:\n        _container_capacity = request.param\n    except AttributeError:\n        _container_capacity = TestWaterContainer.capacity_default\n    _container = WaterContainer(capacity=_container_capacity)\n    return _container\n\n\nclass TestWaterContainer:\n\n    capacity_default: Mililiters = 1000\n\n    @mark.parametrize(\n        ('create_water_container', 'water_volume', 'expectation'),\n        [(100, 200, raises(NotEnoughWater)),\n         (0, 1, raises(NotEnoughWater))],\n        indirect=['create_water_container']\n    )\n    def test_fill_level_raise_not_enough_water(self,\n                                               create_water_container: WaterContainer,\n                                               water_volume: Mililiters,\n                                               expectation: CoffeeMachineException) -> None:\n        \"\"\"Test an edge case, when there is not enough water to get from the WaterContainer\"\"\"\n        with expectation:\n            create_water_container.get_water(volume=water_volume)\n","sub_path":"tests/tests_models/test_water_container.py","file_name":"test_water_container.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"258349714","text":"import unittest\nfrom stores import alientech\nimport gevent.monkey\nimport logging\n\n\nclass AlienTechTestCase(unittest.TestCase):\n    def setUp(self):\n        logging.basicConfig(level=logging.INFO)\n        gevent.monkey.patch_all()\n        self._store = alientech.AlienTech(\n            'http://www.alientech.pt',\n            '/advanced_search_result.php?keywords=%25&pfrom=20&page={0}&sort=3d',\n            'cp1252',\n            '<b>(\\d+)</b>\\s*\\(de\\s*<b>(\\d+)</b>')\n\n    def test_get_total_pages(self):\n        print(self._store.get_total_pages())\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"src/stores/test_AlienTech.py","file_name":"test_AlienTech.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"52132512","text":"#!/usr/bin/env python\n\"\"\"This is a script for managing MassPay each week.\n\nMost of our payouts are handled by Balanced, but they're limited to people in\nthe U.S. We need to payout to people outside the U.S. (#126), and while we work\non a long-term solution, we are using PayPal. However, we've grown past the\npoint that PayPal's Instant Transfer feature is workable. This script is for\ninterfacing with PayPal's MassPay feature.\n\nThis script provides for:\n\n  1. Computing an input CSV by hitting the Gratipay database directly.\n  2. Computing two output CSVs (one to upload to PayPal, the second to use for POSTing\n      the exchanges back to Gratipay)\n  3. POSTing the exchanges back to Gratipay via the HTTP API.\n\nThe idea is that you run steps 1 and 2, then run through the MassPay UI on the\nPayPal website using the appropriate CSV from step 2, then run step 3.\n\n\"\"\"\nfrom __future__ import absolute_import, division, print_function, unicode_literals\n\nimport csv\nimport datetime\nimport getpass\nimport os\nimport sys\nfrom decimal import Decimal as D, ROUND_HALF_UP\n\nimport requests\nfrom gratipay import wireup\nfrom httplib import IncompleteRead\n\n\nos.chdir('../masspay')\nts = datetime.datetime.now().strftime('%Y-%m-%d')\nINPUT_CSV = '{}.input.csv'.format(ts)\nPAYPAL_CSV = '{}.output.paypal.csv'.format(ts)\nGRATIPAY_CSV = '{}.output.gratipay.csv'.format(ts)\nREPORT_CSV = '{}.report.paypal.csv'.format(ts)\n\n\ndef round_(d):\n    return d.quantize(D('0.01'), rounding=ROUND_HALF_UP)\n\ndef print_rule(w=80):\n    print(\"-\" * w)\n\n\nclass Payee(object):\n    username = None\n    email = None\n    gross = None\n    gross_perc = None\n    fee = None\n    net = None\n    additional_note = \"\"\n\n    def __init__(self, rec):\n        self.username, self.email, fee_cap, amount = rec\n        self.gross = D(amount)\n        self.fee = D(0)\n        self.fee_cap = D(fee_cap)\n        self.net = self.gross\n\n    def assess_fee(self):\n\n        # In order to avoid slowly leaking escrow, we need to be careful about\n        # how we compute the fee. It's complicated, but it goes something like\n        # this:\n        #\n        #   1. We want to pass PayPal's fees through to each payee.\n        #\n        #   2. With MassPay there is no option to have the receiver pay the fee,\n        #       as there is with Instant Transfer.\n        #\n        #   3. We have to subtract the fee before uploading the spreadsheet\n        #       to PayPal.\n        #\n        #   4. If we upload 15.24, PayPal upcharges to 15.54.\n        #\n        #   6. If we upload 15.25, PayPal upcharges to 15.56.\n        #\n        #   7. They only accept whole cents. We can't upload 15.245.\n        #\n        #   8. What if we want to hit 15.55?\n        #\n        #   9. We can't.\n        #\n        #  10. Our solution is to leave a penny behind in Gratipay for\n        #       affected payees.\n        #\n        #  11. BUT ... if we upload 1.25, PayPal upcharges to 1.28. Think about\n        #       it.\n        #\n        # See also: https://github.com/gratipay/gratipay.com/issues/1673\n        #           https://github.com/gratipay/gratipay.com/issues/2029\n        #           https://github.com/gratipay/gratipay.com/issues/2198\n        #           https://github.com/gratipay/gratipay.com/pull/2209\n        #           https://github.com/gratipay/gratipay.com/issues/2296\n\n        target = net = self.gross\n        while 1:\n            net -= D('0.01')\n            fee = round_(net * D('0.02'))\n            fee = min(fee, self.fee_cap)\n            gross = net + fee\n            if gross <= target:\n                break\n        self.gross = gross\n        self.net = net\n        self.fee = fee\n\n        remainder = target - gross\n        if remainder > 0:\n            n = \"{:.2} remaining due to PayPal rounding limitation.\".format(remainder)\n            self.additional_note = n\n\n        return fee\n\n\ndef compute_input_csv():\n    db = wireup.db(wireup.env())\n    participants = db.all(\"\"\"\n\n        SELECT participants.*::participants\n          FROM participants\n         WHERE paypal_email IS NOT null\n           AND balance > 0\n      ORDER BY balance DESC\n\n    \"\"\")\n    writer = csv.writer(open(INPUT_CSV, 'w+'))\n    print_rule(88)\n    headers = \"username\", \"email\", \"fee cap\", \"balance\", \"tips\", \"amount\"\n    print(\"{:<24}{:<32} {:^7} {:^7} {:^7} {:^7}\".format(*headers))\n    print_rule(88)\n    total_gross = 0\n    for participant in participants:\n        total = participant.giving + participant.pledging\n        amount = participant.balance - total\n        if amount < 0.50:\n            # Minimum payout of 50 cents. I think that otherwise PayPal upcharges to a penny.\n            # See https://github.com/gratipay/gratipay.com/issues/1958.\n            continue\n        total_gross += amount\n        print(\"{:<24}{:<32} {:>7} {:>7} {:>7} {:>7}\".format( participant.username\n                                                           , participant.paypal_email\n                                                           , participant.paypal_fee_cap\n                                                           , participant.balance\n                                                           , total\n                                                           , amount\n                                                            ))\n        row = (participant.username, participant.paypal_email, participant.paypal_fee_cap, amount)\n        writer.writerow(row)\n    print(\" \"*80, \"-\"*7)\n    print(\"{:>88}\".format(total_gross))\n\n\ndef compute_output_csvs():\n    payees = [Payee(rec) for rec in csv.reader(open(INPUT_CSV))]\n    payees.sort(key=lambda o: o.gross, reverse=True)\n\n    total_fees = sum([payee.assess_fee() for payee in payees])  # side-effective!\n    total_net = sum([p.net for p in payees])\n    total_gross = sum([p.gross for p in payees])\n    assert total_fees + total_net == total_gross\n\n    paypal_csv = csv.writer(open(PAYPAL_CSV, 'w+'))\n    gratipay_csv = csv.writer(open(GRATIPAY_CSV, 'w+'))\n    print_rule()\n    print(\"{:<24}{:<32} {:^7} {:^7} {:^7}\".format(\"username\", \"email\", \"gross\", \"fee\", \"net\"))\n    print_rule()\n    for payee in payees:\n        paypal_csv.writerow((payee.email, payee.net, \"usd\"))\n        gratipay_csv.writerow(( payee.username\n                            , payee.email\n                            , payee.gross\n                            , payee.fee\n                            , payee.net\n                            , payee.additional_note\n                             ))\n        print(\"{username:<24}{email:<32} {gross:>7} {fee:>7} {net:>7}\".format(**payee.__dict__))\n\n    print(\" \"*56, \"-\"*23)\n    print(\"{:>64} {:>7} {:>7}\".format(total_gross, total_fees, total_net))\n\n\ndef load_statuses():\n    _status_map = {'Completed': 'succeeded', 'Denied': 'failed'} # PayPal -> Gratipay\n    statuses = {}\n    fp = open(REPORT_CSV)\n    for line in fp:\n        if line.startswith('Transaction ID,Recipient'):\n            break\n    for rec in csv.reader(fp):\n        statuses[rec[1]] = _status_map[rec[5]]\n    return statuses\n\n\ndef post_back_to_gratipay():\n\n    try:\n        gratipay_api_key = os.environ['GRATIPAY_API_KEY']\n    except KeyError:\n        gratipay_api_key = getpass.getpass(\"Gratipay API key: \")\n\n    try:\n        gratipay_base_url = os.environ['GRATIPAY_BASE_URL']\n    except KeyError:\n        gratipay_base_url = 'https://gratipay.com'\n\n    statuses = load_statuses()\n\n    nposts = 0\n    for username, email, gross, fee, net, additional_note in csv.reader(open(GRATIPAY_CSV)):\n        url = '{}/{}/history/record-an-exchange'.format(gratipay_base_url, username)\n        note = 'PayPal MassPay to {}.'.format(email)\n        if additional_note:\n            note += \" \" + additional_note\n        print(note)\n        status = statuses[email]\n\n        data = {'amount': '-' + net, 'fee': fee, 'note': note, 'status': status}\n        try:\n            response = requests.post(url, auth=(gratipay_api_key, ''), data=data)\n        except IncompleteRead:\n            print('IncompleteRead, proceeding (but double-check!)')\n        else:\n            if response.status_code == 200:\n                nposts += 1\n            else:\n                if response.status_code == 404:\n                    print('Got 404, is your API key good? {}'.format(gratipay_api_key))\n                else:\n                    print('... resulted in a {} response:'.format(response.status_code))\n                    print(response.text)\n                raise SystemExit\n        print(\"POSTed MassPay back to Gratipay for {} users.\".format(nposts))\n\n\ndef run_report():\n    \"\"\"Print a report to help Determine how much escrow we should store in PayPal.\n    \"\"\"\n    totals = []\n    max_masspay = max_weekly_growth = D(0)\n    for filename in os.listdir('.'):\n        if not filename.endswith('.input.csv'):\n            continue\n\n        datestamp = filename.split('.')[0]\n\n        totals.append(D(0))\n        for rec in csv.reader(open(filename)):\n            amount = rec[-1]\n            totals[-1] += D(amount)\n\n        max_masspay = max(max_masspay, totals[-1])\n        if len(totals) == 1:\n            print(\"{} {:8}\".format(datestamp, totals[-1]))\n        else:\n            weekly_growth = totals[-1] / totals[-2]\n            max_weekly_growth = max(max_weekly_growth, weekly_growth)\n            print(\"{} {:8} {:4.1f}\".format(datestamp, totals[-1], weekly_growth))\n\n    print()\n    print(\"Max Withdrawal:    ${:9,.2f}\".format(max_masspay))\n    print(\"Max Weekly Growth:  {:8.1f}\".format(max_weekly_growth))\n    print(\"5x Current:        ${:9,.2f}\".format(5 * totals[-1]))\n\n\ndef main():\n    if not sys.argv[1:]:\n        print(\"Looking for files for {} ...\".format(ts))\n        for filename in (INPUT_CSV, PAYPAL_CSV, GRATIPAY_CSV):\n            print(\"  [{}] {}\".format('x' if os.path.exists(filename) else ' ', filename))\n        print(\"Rerun with one of these options:\")\n        print(\"  -i - hits db to generate input CSV (needs envvars via heroku + honcho)\")\n        print(\"  -o - computes output CSVs (doesn't need anything but input CSV)\")\n        print(\"  -p - posts back to Gratipay (prompts for API key)\")\n    elif '-i' in sys.argv:\n        compute_input_csv()\n    elif '-o' in sys.argv:\n        compute_output_csvs()\n    elif '-p' in sys.argv:\n        post_back_to_gratipay()\n\n\nif __name__ == '__main__':\n    try:\n        main()\n    except KeyboardInterrupt:\n        print()\n","sub_path":"bin/masspay.py","file_name":"masspay.py","file_ext":"py","file_size_in_byte":10358,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"365871534","text":"import matplotlib.pyplot as plt\nfrom numpy.random import rand\nfrom numpy import arange\n\ndates = ['2015-08-25', '2016-01-07', '2016-02-11', '2016-04-26', '2016-06-10', '2016-08-04', '2016-09-14', '2016-12-16', '2017-01-25', '2017-08-09', '2017-08-15', '2017-12-08']\nscores = [95, 91, 98, 93, 97, 96, 98, 100, 99, 96, 96, 97]\n\ncount = ['138','170','155','243','232']\nplatform =['PlayStation','PlayStation','PlayStation','PC','PlayStation']\n\n\ndef autolabel(rects):\n# attach some text labels\n    for ii,rect in enumerate(rects):\n        width = int(rect.get_width())\n\n        height = rect.get_height()\n        print(height,width)\n        yloc1=rect.get_y() + height /2.0\n        yloc2=rect.get_y() + height /2.0\n        if (width <= 5):\n            # Shift the text to the right side of the right edge\n            xloc1 = width + 1\n            yloc2=yloc2+0.3\n            # Black against white background\n            clr = 'black'\n            align = 'left'\n        else:\n            # Shift the text to the left side of the right edge\n            xloc1 = 0.98*width\n            # White on blue\n            clr = 'white'\n            align = 'right'\n        yloc1=rect.get_y() + height /2.0\n        print(xloc1,yloc1,yloc2)\n        ax.text(xloc1,yloc1, '%s'% (dates[ii]),horizontalalignment=align,\n                         verticalalignment='center',color=clr,weight='bold',\n                         clip_on=True)\n        ax.text(5,yloc2, '%s'% (scores[ii]),horizontalalignment='left',\n                         verticalalignment='center',color=clr,weight='bold',\n                         clip_on=True)\n\n\nval = [138,170,155,243,232]\nprint(val)# the bar lengths or count in your case\npos = [ 1996 , 1997,  1998,  1999,  2000]    # the bar centers on the y axis\nprint(pos)\n\nfig = plt.figure()\nax = fig.add_subplot(111)\nrects = ax.barh(dates, scores, align='center', height=0.4)\nprint(rects)\nautolabel(rects)\nax.set_ylabel('Year')\nax.set_xlabel('Count')\nax.set_title('horizontal bar chart')\nax.grid(False)\nplt.show()","sub_path":"DataScience/bartest2.py","file_name":"bartest2.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"465808187","text":"#!/usr/bin/env python3\n# -*- coding=UTF-8 -*-\n\n#爬取去哪儿网当天上海酒店信息\n\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nimport time\nfrom bs4 import BeautifulSoup\nimport codecs\n\ndriver = webdriver.Firefox()\n# driver = webdriver.PhantomJS()\n\ndriver.set_window_size(1080, 1080)\ndriver.get(\"http://hotel.qunar.com/\")\nassert '去哪儿网' in driver.title\n\ntoCity = '上海'\n\n# ele_toCity = driver.find_element_by_xpath(\"//div[@id='js_searchbox_flight']/input[3]\")\n\n# ele_hotel = driver.find_element_by_xpath(\".//*[@data-for='hotel']\")\n# ele_hotel.click()\n# time.sleep(10)\n#\nele_toCity = driver.find_element_by_id('toCity')\nele_fromDate = driver.find_element_by_name('fromDate')\nele_search = driver.find_element_by_class_name(\n    'search-button')\n\n\n\nele_toCity.clear()\nele_toCity.send_keys(toCity)\nele_toCity.click()\nele_search.click()\n# elem.send_keys(Keys.RETURN)\n\ntry:\n    WebDriverWait(driver,10).until(EC.title_contains(toCity))\nexcept Exception as e:\n    print(e)\n    # break\ntime.sleep(5)\njs = \"window.scrollTo(0,document.body.scrollHeight);\"\ndriver.execute_script(js)\ntime.sleep(5)\nhtm_const=driver.page_source\nsoup = BeautifulSoup(htm_const,'html.parser')\n\ninfos = soup.find_all(class_='item_hotel_info')\nf = codecs.open(toCity+'.html','a')\n\nfor info in infos:\n    f.write('--'*20)\n    content = info.get_text().replace(\" \",\"\").replace(\"\\t\",\"\").strip()\n    for line in [ln for ln in content.splitlines() if ln.strip()]:\n        f.write(line)\n        f.write('\\r\\n')\n    print(content)\n    f.close\n","sub_path":"qunarCrawler.py","file_name":"qunarCrawler.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"297791355","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\n\r\n\r\n#donner une liste\r\nL=[5,1,2,3,2,8,8,9,9]\r\nprint(L)            #afficher la liste avant traiter \r\nnum=len(L)\r\n\r\nT=L[:]\r\n\r\ni=0\r\nwhile (i<num):       #boucle jusqu'au dernier élément de la liste \r\n    T[i]=L.count(L[i])\r\n    print(T[i],i)\r\n    if T[i]>1:      #si il apparaît au moins 2 fois dans la liste \r\n        first=L.index(L[i])\r\n        j=first+1\r\n        while (j<num):  #boucle: du premier apparence au dernier  \r\n            if L[j]==L[i]:\r\n                \r\n                del(L[j])  #supprimer le deuxième ,troisième ...\r\n                num=num-1\r\n            j=j+1\r\n    i=i+1   \r\n         \r\nprint(L)      #afficher la liste après traiter\r\n\r\n  \r\n","sub_path":"INF_1_BE_1/Jiahe/BE1/enlever_doublons.py","file_name":"enlever_doublons.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"170544982","text":"# \"\"\"\n# This is the interface that allows for creating nested lists.\n# You should not implement it, or speculate about its implementation\n# \"\"\"\n#class NestedInteger:\n#    def __init__(self, value=None):\n#        \"\"\"\n#        If value is not specified, initializes an empty list.\n#        Otherwise initializes a single integer equal to value.\n#        \"\"\"\n#\n#    def isInteger(self):\n#        \"\"\"\n#        @return True if this NestedInteger holds a single integer, rather than a nested list.\n#        :rtype bool\n#        \"\"\"\n#\n#    def add(self, elem):\n#        \"\"\"\n#        Set this NestedInteger to hold a nested list and adds a nested integer elem to it.\n#        :rtype void\n#        \"\"\"\n#\n#    def setInteger(self, value):\n#        \"\"\"\n#        Set this NestedInteger to hold a single integer equal to value.\n#        :rtype void\n#        \"\"\"\n#\n#    def getInteger(self):\n#        \"\"\"\n#        @return the single integer that this NestedInteger holds, if it holds a single integer\n#        Return None if this NestedInteger holds a nested list\n#        :rtype int\n#        \"\"\"\n#\n#    def getList(self):\n#        \"\"\"\n#        @return the nested list that this NestedInteger holds, if it holds a nested list\n#        Return None if this NestedInteger holds a single integer\n#        :rtype List[NestedInteger]\n#        \"\"\"\n\nclass Solution:\n    def __init__(self):\n        self.nodes = []\n        \n    def helper(self, nestedList, height):\n        lst = []\n        integers = []\n        for each in nestedList:\n            if each.isInteger():\n                integers.append(each.getInteger())\n            else:\n                lst.append(each.getList())\n        if len(self.nodes)<=height:\n            self.nodes.append([])\n        self.nodes[height] += integers\n        for each in lst:\n            self.helper(each, height+1)\n    \n    def depthSumInverse(self, nestedList: List[NestedInteger]) -> int:\n        self.helper(nestedList, 0)\n        idx = 0\n        ans = 0\n        while self.nodes:\n            item = self.nodes.pop()\n            idx += 1\n            for each in item:\n                ans += each*idx\n        return ans\n","sub_path":"Problems/0364. Nested List Weight Sum II.py","file_name":"0364. Nested List Weight Sum II.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"314187616","text":"'''\nCSV Analyser\n\nA Python script to load and analyse a CSV document\n'''\n\n# Library Imports\nimport pandas as pd\nimport csv_analyser_functions as functions\n# Import datetime to access time and date functions\nimport datetime as dt\n\n# Log Program Start Time\nstart_time = dt.datetime.now()\n\n# File Path to CSV\npath = r''\n\n# Read CSV file to DataFrame\ndf = functions.read_file(path)\n\n# Get List of Columns\ncolumns = functions.get_column_names(df)\n\nprint('')\n\n# Get number of rows\nprint('Total rows')\nprint(len(df))\nprint('')\n\n# Get missing values\nlist_of_missing_values = []\n\nfor i in range(len(columns)):\n    name = str(columns[i])\n    values = functions.missing_values(df[columns[i]])\n    list_of_missing_values.append([name, values[0], values[1]])\n\nmissing_values = pd.DataFrame(\n    list_of_missing_values,\n    columns=['Column', 'Missing Values', 'Percentage']\n)\n\nprint('Number of missing values per column')\nprint(missing_values)\nprint('')\n\n# Get unique values\nlist_of_unique_values = []\n\nfor i in range(len(columns)):\n    name = str(columns[i])\n    value = len(functions.get_unique_values(df[columns[i]]))\n    list_of_unique_values.append([name, value])\n\nunique_values = pd.DataFrame(\n    list_of_unique_values,\n    columns=['Column', 'Unique Values']\n)\n\nprint('Number of unique values per column')\nprint(unique_values)\n\nprint('')\n\n# Calculate Program Run Time\nruntime = dt.datetime.now() - start_time\n# Print Program Runtime\nprint('Execution Time: ' + str(runtime.total_seconds()) + ' seconds')\n","sub_path":"CSV Analyser.py","file_name":"CSV Analyser.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"304356006","text":"from django.shortcuts import render\nfrom . import forms\n\n# Create your views here.\n\ndef regform(request):\n    form = forms.SignUp()\n    if request.method == 'POST':\n        form = forms.SignUp(request.POST)\n        html = \"We Have recived this form again\"\n        if form.is_valid():\n            html = html + 'The form is valid'\n    else:\n        html = \"Welcome First Time\"\n\n    return  render(request,'registration\\signUp.html',{'html':html,'form':form})\n\n\n\n","sub_path":"registration/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"334907909","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun 22 21:05:02 2018\n\n@author: thakur\n\"\"\"\n\n###### Duplicate features Elimination #########\n\ndef remove_duplicate(df):\n    \n    print('not splitting train test as believed to be done in previous step')\n    \n    duplicated_features=[]\n    \n    for i in range(len(df.columns)):\n        \n        if i%10==0:\n            print(i)\n            \n        col1=df.columns[i]\n        \n        for col2 in df.columns[i+1]:\n            \n            if df[col1].equals(df[col2]):\n                duplicated_features.append(col2)\n                \n                \n    df.drop(labels=duplicated_features,axis=1,inplace=True)\n    \n    \n    return df,duplicated_features\n","sub_path":"rm_duplicate.py","file_name":"rm_duplicate.py","file_ext":"py","file_size_in_byte":719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"308014616","text":"import copy\nclass Solution:\n    def getIntersectionNode(self, headA: ListNode, headB: ListNode) -> ListNode:\n        if headA == None or headB == None:      # 判断输入节点是否为空\n            return None\n        # 分别计算两个链表的长度,从而计算出两个链表的长度差\n        nodeA = copy.copy(headA)\n        nodeB = copy.copy(headB)\n        lengthNodeA = 0\n        lengthNodeB = 0\n        while nodeA != None:\n            lengthNodeA = lengthNodeA + 1\n            nodeA = nodeA.next\n        while nodeB != None:\n            lengthNodeB = lengthNodeB + 1\n            nodeB = nodeB.next\n        lengthGap = abs(lengthNodeA - lengthNodeB)  # 两个链表长度差\n\n        # 长度较长的列表的头结点先进行移动，移动距离是两个链表的长度差\n        if lengthNodeA >= lengthNodeB:\n            while lengthGap > 0:\n                lengthGap = lengthGap - 1\n                headA = headA.next\n        else:\n            while lengthGap > 0:\n                lengthGap = lengthGap - 1\n                headB = headB.next\n\n        # 通过上面的调整，此时两个链表的节点距离公共交点的距离是一致的\n        while headA != headB:\n            headA = headA.next\n            headB = headB.next\n        return headA","sub_path":"chap5/52_FirstCommonNodesInLists/FirstCommonNodesInLists.py","file_name":"FirstCommonNodesInLists.py","file_ext":"py","file_size_in_byte":1281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"544224295","text":"\"\"\"Server module.\"\"\"\nfrom logging.handlers import TimedRotatingFileHandler\nfrom urllib.parse import urlencode, parse_qs, urlparse\nimport logging  # pylint: disable=ungrouped-imports\nimport os\nimport shutil\nimport tempfile\n\nfrom appdirs import user_data_dir\nfrom flask import Flask, render_template, request, url_for, flash, send_from_directory\nfrom flask_admin import Admin, BaseView, expose\nfrom flask_admin.contrib.sqla import ModelView\nfrom flask_bootstrap import Bootstrap\nfrom flask_restless import APIManager  # pylint: disable=import-error\nimport click\nimport structlog\n\nfrom google_images_download.forms import IndexForm, FindImageForm\nfrom google_images_download import models, pagination, admin, api, sha256\n\n\napp = Flask(__name__)\nlog = structlog.getLogger(__name__)\n\n\nclass ImageURLSingleView(BaseView):\n    @expose('/')\n    def index(self):\n        \"\"\"View for single image url.\"\"\"\n        search_url = request.args.get('u', None)\n        entry = models.ImageURL.query.filter_by(url=search_url).one_or_none()\n        return self.render(\n            'google_images_download/image_url_view.html', entry=entry, search_url=search_url)\n\n\n# @app.route('/')\ndef index():\n    \"\"\"Get Index page.\"\"\"\n    form = IndexForm(request.args)\n    search_query = form.query.data\n    limit = form.limit.data\n    entry = None\n    render_template_kwargs = {'form': form, 'entry': entry}\n    page = form.page.data if form.page.data else 1\n    if not search_query:\n        return render_template('index.html', **render_template_kwargs)\n\n    sq_m, _ = models.SearchQuery.get_or_create_from_query(search_query, page - 1)\n    if not sq_m.match_results:\n        sq_m.get_match_results()\n    app.logger.debug(\n        '%s match(s) found for [%s] page:%s', len(sq_m.match_results), sq_m.query, page)\n    entry = sq_m\n\n    entry_match_results = entry.match_results\n    if limit and limit < len(entry.match_results):\n        entry_match_results = entry.match_results[:limit]\n    render_template_kwargs['entry_match_results'] = entry_match_results\n\n    def return_page_url(page_input):\n        \"\"\"Return page url.\"\"\"\n        url_query = parse_qs(urlparse(request.url).query)\n        url_query['page'] = [page_input]\n        new_query = urlencode(url_query, True)\n        parsed_url = urlparse(url_for('index'))\n        parsed_url = parsed_url._replace(query=new_query)\n        return parsed_url.geturl()\n\n    app.logger.debug('Limit: %s', limit)\n    render_template_kwargs.update({\n        'form': form, 'entry': entry,\n        'pagination': pagination.Pagination(page, return_page_url),\n        'limit': limit\n    })\n    return render_template('index.html', **render_template_kwargs)\n\n\n@app.route('/t/<path:filename>')\ndef thumbnail(filename):\n    \"\"\"Thumbnail url.\"\"\"\n    return send_from_directory(models.DEFAULT_THUMB_FOLDER, filename)\n\n\ndef shell_context():\n    \"\"\"Return shell context.\"\"\"\n    app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///:memory:'\n    app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n    models.db.init_app(app)\n    return {'app': app, 'db': models.db, 'models': models, }\n\n\ndef create_app(script_info=None):  # pylint: disable=unused-argument\n    \"\"\"Create app.\"\"\"\n    app.shell_context_processor(shell_context)\n    if not app.debug:\n        directory = 'log'\n        if not os.path.exists(directory):\n            os.makedirs(directory)\n        default_log_file = os.path.join(\n            directory, 'google_images_download_server.log')\n        file_handler = TimedRotatingFileHandler(\n            default_log_file, 'midnight')\n        file_handler.setLevel(logging.WARNING)\n        file_handler.setFormatter(\n            logging.Formatter('<%(asctime)s> <%(levelname)s> %(message)s'))\n        app.logger.addHandler(file_handler)\n\n    app_admin = Admin(app, name='google image download', template_mode='bootstrap3')\n    app_admin.add_view(ModelView(models.SearchQuery, models.db.session))\n    app_admin.add_view(ModelView(models.MatchResult, models.db.session))\n    app_admin.add_view(ModelView(models.ImageURL, models.db.session))\n    Bootstrap(app)\n    return app\n\n\nclass FromFileSearchImageView(BaseView):\n    @expose('/')\n    def index(self):\n        form = FindImageForm(request.args)\n        file_path = form.file_path.data\n        url = form.url.data\n        search_type = form.search_type.data\n        disable_cache = form.disable_cache.data\n        render_template_kwargs = {'entry': None, 'form': form}\n        file_exist = os.path.isfile(file_path) if file_path is not None else False\n        empty_response = self.render(\n            'google_images_download/from_file_search_page.html', **render_template_kwargs)\n\n        def get_entry(kwargs):\n            entry = None\n            try:\n                entry, created = api.get_or_create_page_search_image(**kwargs)\n                if created or disable_cache:\n                    models.db.session.add(entry)\n                    models.db.session.commit()\n            except Exception as err:\n                msg = '{} raised:{}'.format(type(err), err)\n                flash(msg, 'danger')\n                app.logger.debug(msg)\n            return entry\n\n        if not file_path and not url:\n            return empty_response\n        if url:\n            kwargs = {'url': url, 'search_type': search_type, 'disable_cache': disable_cache}\n            entry = get_entry(kwargs)\n            if not entry:\n                return empty_response\n        elif not file_path or not file_exist:\n            if not file_exist:\n                msg = 'File not exist: {}'.format(file_path)\n                app.logger.debug(msg)\n                flash(msg, 'danger')\n            return empty_response\n        else:\n            with tempfile.NamedTemporaryFile() as temp:\n                shutil.copyfile(file_path, temp.name)\n                kwargs = {\n                    'file_path': temp.name,\n                    'search_type': search_type,\n                    'disable_cache': disable_cache\n                }\n                entry = get_entry(kwargs)\n                if not entry:\n                    return empty_response\n        app.logger.debug('kwargs: %s', kwargs)\n        app.logger.debug('search type:%s match results:%s', search_type, len(entry.match_results))\n        app.logger.debug('URL:%s', request.url)\n        render_template_kwargs['entry'] = entry\n        return self.render(\n            'google_images_download/from_file_search_page.html', **render_template_kwargs)\n\n\n@click.group()\ndef cli():\n    \"\"\"CLI command.\"\"\"\n    pass\n\n\n@cli.command()\ndef check_thumbnails():\n    # get all thumbnail files and checksum\n    def_thumb_folder = os.path.join(user_data_dir('google_images_download', 'hardikvasa'), 'thumb')  # NOQA\n    thumb_folder = def_thumb_folder\n    listdir_res = [\n        {'basename': x, 'path': os.path.join(thumb_folder, x)}\n        for x in os.listdir(def_thumb_folder)\n        if os.path.isfile(os.path.join(thumb_folder, x))\n    ]\n    filtered_ff = []\n    for item in listdir_res:\n        old_checksum = os.path.splitext(item['basename'])[0]\n        checksum = sha256.sha256_checksum(os.path.join(thumb_folder, item['basename']))\n        new_basename = checksum + '.jpg'\n        new_path = os.path.join(thumb_folder, new_basename)\n        if checksum != old_checksum:\n            # move thumbnail\n            shutil.move(item['path'], new_path)\n            log.info('Move thumbnail', src=old_checksum, dst=checksum)\n        filtered_ff.append(\n            {'basename': new_basename, 'path': new_path, 'checksum': checksum})\n\n    app.config['DEBUG'] = True\n    app.config['LOGGER_HANDLER_POLICY'] = 'debug'\n    app.config['SECRET_KEY'] = os.getenv('DDG_SERVER_SECRET_KEY') or \\\n        os.urandom(24)\n    app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///gid_debug.db'\n    app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n    app.config['WTF_CSRF_ENABLED'] = False\n    models.db.init_app(app)\n    app.app_context().push()\n    models.db.create_all()\n    new_model_sets = [\n        api.get_or_create_image_file_with_thumbnail(x['path']) for x in filtered_ff]\n    models.db.session.add_all([x[0] for x in new_model_sets])\n    models.db.session.commit()\n\n\n@cli.command()\n@click.option(\"-h\", \"--host\", default=\"127.0.0.1\", type=str)\n@click.option(\"-p\", \"--port\", default=5000, type=int)\n@click.option(\"-d\", \"--debug\", is_flag=True)\n@click.option(\"-r\", \"--reloader\", is_flag=True)\n@click.option(\"-t\", \"--threaded\", is_flag=True)\ndef run(host='127.0.0.1', port=5000, debug=False, reloader=False, threaded=False):\n    \"\"\"Run the application server.\"\"\"\n    if reloader:\n        app.jinja_env.auto_reload = True\n        app.config[\"TEMPLATES_AUTO_RELOAD\"] = True\n\n    # logging\n    directory = 'log'\n    if not os.path.exists(directory):\n        os.makedirs(directory)\n    default_log_file = os.path.join(\n        directory, 'google_images_download_server.log')\n    file_handler = TimedRotatingFileHandler(\n        default_log_file, 'midnight')\n    file_handler.setLevel(logging.WARNING)\n    file_handler.setFormatter(\n        logging.Formatter('<%(asctime)s> <%(levelname)s> %(message)s'))\n    app.logger.addHandler(file_handler)\n\n    if debug:\n        app.config['DEBUG'] = True\n        app.config['LOGGER_HANDLER_POLICY'] = 'debug'\n        app.config['SECRET_KEY'] = os.getenv('DDG_SERVER_SECRET_KEY') or \\\n            os.urandom(24)\n        app.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///gid_debug.db'\n        app.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\n        app.config['WTF_CSRF_ENABLED'] = False\n        models.db.init_app(app)\n        app.app_context().push()\n        models.db.create_all()\n        logging.basicConfig(level=logging.DEBUG)\n\n    api_manager = APIManager(app, flask_sqlalchemy_db=models.db)\n    api_manager.create_api(models.SearchQuery, methods=['GET'])\n    api_manager.create_api(models.MatchResult, methods=['GET'])\n\n    admin_templ = 'google_images_download/index.html'\n    app_admin = Admin(\n        app, name='Google images download', template_mode='bootstrap3',\n        index_view=admin.HomeView(name='Home', template=admin_templ, url='/'))\n    app_admin.add_view(FromFileSearchImageView(name='Image Search', endpoint='f'))\n    app_admin.add_view(ImageURLSingleView(name='Image Viewer', endpoint='u'))\n    app_admin.add_view(admin.SearchQueryView(models.SearchQuery, models.db.session))\n    app_admin.add_view(admin.MatchResultView(models.MatchResult, models.db.session))\n    app_admin.add_view(admin.JSONDataView(models.JSONData, models.db.session))\n    app_admin.add_view(admin.ImageURLView(models.ImageURL, models.db.session))\n    app_admin.add_view(admin.TagView(models.Tag, models.db.session))\n    app_admin.add_view(admin.ImageFileView(models.ImageFile, models.db.session))\n    app_admin.add_view(admin.SearchImageView(models.SearchImage, models.db.session))\n    app_admin.add_view(admin.SearchImagePageView(models.SearchImagePage, models.db.session))\n    app_admin.add_view(admin.TextMatchView(models.TextMatch, models.db.session))\n    app_admin.add_view(admin.MainSimilarResultView(models.MainSimilarResult, models.db.session))\n    model_list = []  # NOTE: may be used later\n    for model_item in model_list:\n        app_admin.add_view(ModelView(model_item, models.db.session))\n    Bootstrap(app)\n\n    app.run(\n        host=host, port=port,\n        debug=debug, use_debugger=debug,\n        use_reloader=reloader,\n        threaded=threaded\n    )\n\n\nif __name__ == '__main__':\n    cli()\n","sub_path":"google_images_download/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":11430,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"508939699","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 13 12:59:17 2016\n\n@author: danieljunior\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random, math\n\n# Fixed seed to make the results repeatable\nrandom.seed(0)\n# Pretty matrix outputs\nnp.set_printoptions(precision=3, threshold=1000, edgeitems=5, linewidth=80, suppress=True)\n\ndef linear_gradient_descent(X, y, w):\n    w = X.T.dot(X.dot(w)-y)\n    return w\n\n\nN = 5                                              # Size of the data set\nX = np.array([[0.0], [1.0], [2.0], [3.0], [4.0]])  # Inputs, shape: N x 1\ny = np.array([10.5, 5.0, 3.0, 2.5, 1.0])     \n\nN_test = 100\nX_test = np.linspace(0.0, 4.0, N_test).reshape((N_test, 1))\ny_test = np.linspace(7.0, -5.0, N_test) + 2*np.random.randn(N_test)\n\n#plt.plot(X[:, 0], y, 'bo', label='Training')\n#plt.plot(X_test[:, 0], y_test, \"g.\", label='Test')\n#plt.xlabel('X'); plt.ylabel('y'); plt.legend();\n\n\n#tenho um conjunto de treinamento pequeno, então preciso gerar mais exemeplos a\n#partir do conjunto inicial\n# Add bias to each row/instance:\nX_bias = np.hstack((X, np.ones((N, 1))))\nX_test_bias = np.hstack((X_test, np.ones((N_test, 1))))\n\n#começo com um vetor de pesos aleatório\nw = np.random.random(X_bias[0].size)\nw_0 = []\nw_1 = []\n\nalpha = 0.01 #learning rate\n\n#learning loop\nfor i in range(50):\n        print(w)\n        w_0 += [w[0]]\n        w_1 += [w[1]]\n        w = w - alpha * linear_gradient_descent(X_bias, y , w)\n\n#como o peso foi se ajustando\nplt.plot(w_0, w_1, \"bo--\")\n\n#como é a reta de predição (modelo aproximado)\nplt.plot(X_bias[:, 0], y, \"o\")\nplt.plot(X_bias[:, 0], X_bias.dot(w), \"m-\", linewidth=2)\nplt.xlabel(\"x\"); plt.ylabel(\"y\"); plt.title(\"Model: $y = %.2f x + %.2f$\" % tuple(w));\n","sub_path":"Regressão Linear/gradientDescent.py","file_name":"gradientDescent.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"579241261","text":"\nfrom __future__ import print_function, division\n\nfrom keras.datasets import cifar10\nfrom keras.layers.merge import _Merge\nfrom keras.layers import Input, Dense, Reshape, Flatten, Dropout\nfrom keras.layers import BatchNormalization, Activation, ZeroPadding2D, MaxPooling2D, Concatenate\nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.layers.convolutional import UpSampling2D, Conv2D, Conv2DTranspose\nfrom keras.models import Sequential, Model\nfrom keras.optimizers import RMSprop\nfrom functools import partial\n\nimport keras.backend as K\n\nimport matplotlib.pyplot as plt\n\nimport sys\n\nimport numpy as np\n\nPATH_SAVE = '../output/images/wgan_gp/mnist/'\nPATH_MODEL = '../output/model/gan/'\n\nBATCH = 64\n\nclass RandomWeightedAverage(_Merge):\n    \"\"\"Provides a (random) weighted average between real and generated image samples\"\"\"\n    def _merge_function(self, inputs):\n        alpha = K.random_uniform((BATCH, 1, 1, 1))\n        return (alpha * inputs[0]) + ((1 - alpha) * inputs[1])\n\nclass WGANGP():\n    def __init__(self, used_save=False, filenames=None):\n        self.img_rows = 32\n        self.img_cols = 32\n        self.channels = 3\n        self.img_shape = (self.img_rows, self.img_cols, self.channels)\n        self.latent_dim = 64\n\n        # Following parameter and optimizer set as recommended in paper\n        self.n_critic = 5\n        optimizer = RMSprop(lr=0.00005)\n\n        # Build the generator and critic\n        self.generator = self.build_generator()\n        if used_save:\n            assert filenames!=None, \"Provide a file name please.\"\n            self.generator.load_weights(filenames[0])\n\n        self.critic = self.build_critic()\n        if used_save:\n            self.critic.load_weights(filenames[1])\n\n        #-------------------------------\n        # Construct Computational Graph\n        #       for the Critic\n        #-------------------------------\n\n        # Freeze generator's layers while training critic\n        self.generator.trainable = False\n\n        # Image input (real sample)\n        real_img = Input(shape=self.img_shape)\n\n        # Noise input\n        z_disc = Input(shape=(self.latent_dim,))\n        # Generate image based of noise (fake sample)\n        fake_img = self.generator(z_disc)\n\n        # Discriminator determines validity of the real and fake images\n        fake = self.critic(fake_img)\n        valid = self.critic(real_img)\n\n        # Construct weighted average between real and fake images\n        interpolated_img = RandomWeightedAverage()([real_img, fake_img])\n        # Determine validity of weighted sample\n        validity_interpolated = self.critic(interpolated_img)\n\n        # Use Python partial to provide loss function with additional\n        # 'averaged_samples' argument\n        partial_gp_loss = partial(self.gradient_penalty_loss,\n                          averaged_samples=interpolated_img)\n        partial_gp_loss.__name__ = 'gradient_penalty' # Keras requires function names\n\n        self.critic_model = Model(inputs=[real_img, z_disc],\n                            outputs=[valid, fake, validity_interpolated])\n        self.critic_model.compile(loss=[self.wasserstein_loss,\n                                              self.wasserstein_loss,\n                                              partial_gp_loss],\n                                        optimizer=optimizer,\n                                        loss_weights=[1, 1, 10])\n        \n        print(\"Computational Graph for Discriminator built!\")\n        #-------------------------------\n        # Construct Computational Graph\n        #         for Generator\n        #-------------------------------\n\n        # For the generator we freeze the critic's layers\n        self.critic.trainable = False\n        self.generator.trainable = True\n\n        # Sampled noise for input to generator\n        z_gen = Input(shape=(self.latent_dim,))\n        # Generate images based of noise\n        img = self.generator(z_gen)\n        # Discriminator determines validity\n        valid = self.critic(img)\n        # Defines generator model\n        self.generator_model = Model(z_gen, valid)\n        self.generator_model.compile(loss=self.wasserstein_loss, optimizer=optimizer)\n\n        print(\"Computational Graph for Generator built!\")\n\n\n    def gradient_penalty_loss(self, y_true, y_pred, averaged_samples):\n        \"\"\"\n        Computes gradient penalty based on prediction and weighted real / fake samples\n        \"\"\"\n        gradients = K.gradients(y_pred, averaged_samples)[0]\n        # compute the euclidean norm by squaring ...\n        gradients_sqr = K.square(gradients)\n        #   ... summing over the rows ...\n        gradients_sqr_sum = K.sum(gradients_sqr,\n                                  axis=np.arange(1, len(gradients_sqr.shape)))\n        #   ... and sqrt\n        gradient_l2_norm = K.sqrt(gradients_sqr_sum)\n        # compute lambda * (1 - ||grad||)^2 still for each single sample\n        gradient_penalty = K.square(1 - gradient_l2_norm)\n        # return the mean as loss over all the batch samples\n        return K.mean(gradient_penalty)\n\n\n    def wasserstein_loss(self, y_true, y_pred):\n        return K.mean(y_true * y_pred)\n\n    def identity_inception_block_down(self, inputs, output_filters=[64,64,64,64], kernels=[3,5,7], strides=1):\n        assert len(kernels)==len(output_filters)-1, \"[Error] Not the appropriate number of filters.\"\n\n        x1 = Conv2D(output_filters[0], 1, use_bias=False)(inputs)\n        x1 = MaxPooling2D(strides, padding='same')(x1)\n\n        concat = [x1]\n\n        for i in range(len(kernels)):\n            x = Conv2D(output_filters[i+1], 1, use_bias=False)(inputs)\n            x = Conv2D(output_filters[i+1], kernels[i], use_bias=False, strides=strides, padding='same')(x)\n            x = BatchNormalization(momentum=0.8)(x)\n            x = LeakyReLU(alpha=0.2)(x)\n            concat += [x]\n\n        return Concatenate()(concat)\n\n    def identity_inception_block_up(self, inputs, output_filters=[64,64,64,64], kernels=[3,5,7], strides=1):\n        assert len(kernels)==len(output_filters)-1, \"[Error] Not the appropriate number of filters.\"\n\n        x1 = Conv2D(output_filters[0], 1, use_bias=False)(inputs)\n        x1 = UpSampling2D(strides)(x1)\n\n        concat = [x1]\n\n        for i in range(len(kernels)):\n            x = Conv2D(output_filters[i+1], 1, use_bias=False)(inputs)\n            x = Conv2DTranspose(output_filters[i+1], kernels[i], use_bias=False, strides=strides, padding='same')(x)\n            x = BatchNormalization(momentum=0.8)(x)\n            x = LeakyReLU(alpha=0.2)(x)\n            concat += [x]\n\n        return Concatenate()(concat)\n\n    def conv_inception_block_down(self, inputs, output_filters=[64,64,64,64], kernels=[3,3,5,7], strides=1, padding='same'):\n        assert len(kernels)==len(output_filters), \"[Error] Not the appropriate number of filters: %i kernels and %i filters.\" % (len(kernels), len(output_filters))\n\n        concat = []\n        for i in range(len(kernels)):\n            x = Conv2D(output_filters[i], 1, use_bias=False)(inputs)\n            x = Conv2D(output_filters[i], kernels[i], use_bias=False, strides=strides, padding=padding)(x)\n            x = BatchNormalization(momentum=0.8)(x)\n            x = LeakyReLU(alpha=0.2)(x)\n\n            # if kernels[i]>3:\n            #     print(x.shape)\n            #     x = Conv2D(output_filters[i], kernels[i]-2, use_bias=False)(x)\n            #     x = BatchNormalization(momentum=0.8)(x)\n            #     x = LeakyReLU(alpha=0.2)(x)\n            concat += [x]\n        \n        x = Concatenate()(concat)\n        x = BatchNormalization(momentum=0.8)(x)\n        x = LeakyReLU(alpha=0.2)(x)\n        return x\n\n    def conv_inception_block_up(self, inputs, output_filters=[64,64,64,64], kernels=[3,3,5,7], strides=1, padding='same'):\n        assert len(kernels)==len(output_filters), \"[Error] Not the appropriate number of filters: %i kernels and %i filters.\" % (len(kernels), len(output_filters))\n\n        concat = []\n        for i in range(len(kernels)):\n            x = Conv2D(output_filters[i], 1, use_bias=False)(inputs)\n            x = Conv2DTranspose(output_filters[i], kernels[i], use_bias=False, strides=strides, padding=padding)(x)\n            x = BatchNormalization(momentum=0.8)(x)\n            x = LeakyReLU(alpha=0.2)(x)\n\n            # if kernels[i]>=3:\n            #     x = Conv2D(output_filters[i], kernels[i]-2, use_bias=False)(x)\n            concat += [x]\n\n        x = Concatenate()(concat)\n        x = BatchNormalization(momentum=0.8)(x)\n        x = LeakyReLU(alpha=0.2)(x)\n        return x\n\n\n    def build_generator(self):\n\n        # noise = Input(shape=(self.latent_dim,))\n\n        # x = Reshape((1,1,self.latent_dim))(noise)\n\n        # x = Conv2DTranspose(self.latent_dim,4,use_bias=False)(x)\n        # x = BatchNormalization(momentum=0.8)(x)\n        # x = LeakyReLU(alpha=0.2)(x)\n\n        # x = self.conv_inception_block_down(x, [64]*4, [3]*4, 1)\n        # x = self.conv_inception_block_up(x, [64]*4, [3]*4, 2)\n\n        # x = self.conv_inception_block_down(x, [32]*4, [3]*4, 1)\n        # x = self.conv_inception_block_up(x, [32]*4, [3]*4, 2)\n\n        # x = self.conv_inception_block_down(x, [16]*4, [3]*4, 1)\n        # x = self.conv_inception_block_up(x, [16]*4, [3]*4, 2)\n\n        # x = Conv2D(self.channels,(3,3),padding='same')(x)\n        # img = Activation('linear')(x)\n        \n        # model = Model(noise, img)\n\n        # model.summary()\n\n        # return Model(noise, img)\n\n        model = Sequential()\n\n        # model.add(Dense(128 * 4 * 4, activation=\"relu\", input_dim=self.latent_dim))\n        model.add(Reshape((1, 1, self.latent_dim)))\n\n        model.add(Conv2DTranspose(128, kernel_size=4))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n        model.add(Conv2D(128, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n\n        model.add(UpSampling2D())\n\n        model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n        model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n        \n        model.add(UpSampling2D())\n\n        model.add(Conv2D(32, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n        model.add(Conv2D(32, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n\n        model.add(UpSampling2D())\n\n        model.add(Conv2D(16, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n        model.add(Conv2D(16, kernel_size=3, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(Activation(\"relu\"))\n\n        model.add(Conv2D(self.channels, kernel_size=1, padding=\"same\"))\n        model.add(Activation(\"linear\"))\n\n        noise = Input(shape=(self.latent_dim,))\n        img = model(noise)\n\n        model.summary()\n\n        return Model(noise, img)\n\n    def build_critic(self):\n\n        model = Sequential()\n\n        model.add(Conv2D(16, kernel_size=3, strides=2, input_shape=self.img_shape, padding=\"same\"))\n        model.add(LeakyReLU(alpha=0.2))\n        model.add(Dropout(0.25))\n        model.add(Conv2D(32, kernel_size=3, strides=2, padding=\"same\"))\n        model.add(ZeroPadding2D(padding=((0,1),(0,1))))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(LeakyReLU(alpha=0.2))\n        model.add(Dropout(0.25))\n        model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(LeakyReLU(alpha=0.2))\n        model.add(Dropout(0.25))\n        model.add(Conv2D(128, kernel_size=2, strides=1, padding=\"same\"))\n        model.add(BatchNormalization(momentum=0.8))\n        model.add(LeakyReLU(alpha=0.2))\n        model.add(Dropout(0.25))\n        model.add(Flatten())\n        model.add(Dense(1))\n\n\n        # model.add(Conv2D(16, kernel_size=1, strides=2, input_shape=self.img_shape, padding=\"same\"))\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Conv2D(16, kernel_size=3, strides=1, padding=\"same\"))\n        # model.add(BatchNormalization())\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Conv2D(32, kernel_size=3, strides=2, padding=\"same\"))\n        # model.add(BatchNormalization())\n        # model.add(LeakyReLU(alpha=0.2))\n\n\n        # model.add(Conv2D(32, kernel_size=3, strides=1, padding=\"same\"))\n        # # model.add(BatchNormalization(momentum=0.8))\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n        # model.add(BatchNormalization())\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Conv2D(64, kernel_size=3, strides=1, padding=\"same\"))\n        # # model.add(BatchNormalization(momentum=0.8))\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Conv2D(128, kernel_size=3, strides=1, padding=\"same\"))\n        # # model.add(BatchNormalization(momentum=0.8))\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Conv2D(128, kernel_size=4, strides=1))\n        # model.add(BatchNormalization())\n        # model.add(LeakyReLU(alpha=0.2))\n\n        # model.add(Flatten())\n        # model.add(Dense(1))\n\n        # model.add(Conv2D(16, kernel_size=1, padding=\"same\", input_shape=self.img_shape))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n\n        # model.add(Conv2D(16, kernel_size=3, padding=\"same\"))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n        # model.add(Conv2D(32, kernel_size=3, strides=2, padding=\"same\"))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n\n        # model.add(Conv2D(32, kernel_size=3, padding=\"same\"))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n        # model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n\n        # model.add(Conv2D(32, kernel_size=3, padding=\"same\"))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n        # model.add(Conv2D(64, kernel_size=3, strides=2, padding=\"same\"))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n\n        # model.add(Conv2D(32, kernel_size=4))\n        # model.add(BatchNormalization(momentum=0.8))\n        # model.add(Activation(\"relu\"))\n\n        # model.add(Flatten())\n        # model.add(Dense(1))\n        # model.add(Activation(\"linear\"))\n\n        model.summary()\n\n        img = Input(shape=self.img_shape)\n        validity = model(img)\n\n        return Model(img, validity)\n\n    def train(self, epochs, batch_size, sample_interval=50):\n\n        # Load the dataset\n        (X_train, _), (_, _) = cifar10.load_data()\n\n        # Rescale -1 to 1\n        X_train = (X_train.astype(np.float32) - 127.5) / 127.5\n        # X_train = np.expand_dims(X_train, axis=3)\n\n        # Adversarial ground truths\n        valid = -np.ones((batch_size, 1))\n        fake =  np.ones((batch_size, 1))\n        dummy = np.zeros((batch_size, 1)) # Dummy gt for gradient penalty\n\n        print(\"Start training!\")\n\n        for epoch in range(epochs):\n\n            for _ in range(self.n_critic):\n\n                # ---------------------\n                #  Train Discriminator\n                # ---------------------\n\n                # Select a random batch of images\n                idx = np.random.randint(0, X_train.shape[0], batch_size)\n                imgs = X_train[idx]\n                # Sample generator input\n                noise = np.random.normal(0, 1, (batch_size, self.latent_dim))\n\n                # print(\"IS it really fuckkkkkkkkk!\")\n                # Train the critic\n                d_loss = self.critic_model.train_on_batch([imgs, noise],\n                                                                [valid, fake, dummy])\n                # print(\"Epoch :\"+str(epoch))\n\n            # ---------------------\n            #  Train Generator\n            # ---------------------\n\n            g_loss = self.generator_model.train_on_batch(noise, valid)\n\n            # Plot the progress\n            print (\"%d [D loss: %f] [G loss: %f]\" % (epoch, d_loss[0], g_loss))\n\n            # If at save interval => save generated image samples\n            if epoch % sample_interval == 0:\n                self.sample_images(epoch)\n            if epoch > 2000 and epoch % (sample_interval*4) == 0:\n                self.generator.save_weights(PATH_MODEL+'wgan_gp_cifar10.gen.'+str(epoch)+'.h5')\n                self.critic.save_weights(PATH_MODEL+'wgan_gp_cifar10.cri.'+str(epoch)+'.h5')\n\n    def sample_images(self, epoch):\n        r, c = 5, 5\n        noise = np.random.normal(0, 1, (r * c, self.latent_dim))\n        gen_imgs = self.generator.predict(noise)\n\n        # Rescale images 0 - 1\n        gen_imgs = 0.5 * gen_imgs + 0.5\n\n        fig, axs = plt.subplots(r, c)\n        cnt = 0\n        for i in range(r):\n            for j in range(c):\n                axs[i,j].imshow(gen_imgs[cnt, :,:,:])\n                axs[i,j].axis('off')\n                cnt += 1\n        fig.savefig(PATH_SAVE+\"mnist_%d.png\" % epoch)\n        plt.close()\n\n\nif __name__ == '__main__':\n    wgan = WGANGP()\n    wgan.train(epochs=30000, batch_size=BATCH, sample_interval=100)\n","sub_path":"GAN-dev/wgan_gp_cifar10.py","file_name":"wgan_gp_cifar10.py","file_ext":"py","file_size_in_byte":17673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"309537437","text":"import copy\r\nimport time\r\nimport requests\r\nimport html2text\r\nfrom lxml import html\r\nfrom lxml import etree\r\n\r\nmsg_key = \"xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\" # 去 http://sc.ftqq.com/3.version 注册一个\r\ntypes = (\"xyxw\", \"xyhd\", \"xwdt\")\r\ntypes_name = (\"学院新闻\", \"学院活动\", \"学院通知\")\r\nmy_headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36'}\r\n\r\ndef get(mtype):\r\n    if mtype in (0, 1, 2):\r\n        url = \"http://sse.tongji.edu.cn/data/list/\" + types[mtype]\r\n    else:\r\n        return []\r\n\r\n    r = requests.get(url, headers = my_headers)\r\n    my_html = etree.HTML(r.text)\r\n    names = my_html.xpath(\"//ul[@class='data-list']/li[position()]/a/text()\")\r\n    hrefs = my_html.xpath(\"//ul[@class='data-list']/li[position()]/a/@href\")\r\n    dates = my_html.xpath(\"//ul[@class='data-list']/li[position()]/span/text()\")\r\n    news = []\r\n    temp = {}\r\n    for i in range(len(names)):\r\n        temp['from'] = types_name[mtype]\r\n        temp['name'] = names[i].replace('\\r', '').replace('\\n', '').replace(' ', '')\r\n        temp['href'] = \"http://sse.tongji.edu.cn\" + hrefs[i]\r\n        temp['date'] = dates[i].replace('\\r', '').replace('\\n', '').replace(' ', '')\r\n        news.append(copy.deepcopy(temp))\r\n    return news\r\n\r\ndef send(new):\r\n    title = \"[{}]__有新消息啦!\".format(new['from'])\r\n    content = get_str(new['href'])\r\n    my_data = {\"text\" : title, \"desp\" : content}\r\n    msg_host = \"https://sc.ftqq.com/{}.send\".format(msg_key)\r\n    requests.post(msg_host, data = my_data)\r\n\r\ndef get_str(url):\r\n    r = requests.get(url, headers = my_headers)\r\n    my_html = etree.HTML(r.text)\r\n    title = my_html.xpath(\"//div[@class='view-title']/h1/text()\")[0]\r\n    date = my_html.xpath(\"//div[@class='view-info']/span[1]/text()\")[0]\r\n    views = my_html.xpath(\"//div[@class='view-info']/span[2]/text()\")[0]\r\n    content_html = html.tostring(my_html.xpath(\"//div[@class='view-cnt']\")[0]).decode('utf-8')\r\n    content_str = html2text.html2text(content_html).replace('](', '](http://sse.tongji.edu.cn')\r\n    string = \"#{}\\n\\n{}\\t{}\\n\\n\\n\\n##文章预览:\\n\\n\\n\\n{}[阅读原文]({})\".format(title, date, views, content_str, url)\r\n    print(string)\r\n    return string\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    news = [get(0), get(1), get(2)]\r\n    while 1:\r\n        time.sleep(10800)\r\n        temp = [get(0), get(1), get(2)]\r\n        for i in range(3):\r\n            if temp[i] != news[i]:\r\n                for j in temp[i]:\r\n                    if j not in news[i]:\r\n                        send(j)\r\n                news[i] = temp[i]\r\n        temp = []    \r\n    \r\n\r\n    \r\n","sub_path":"TongjiSSE-main.py","file_name":"TongjiSSE-main.py","file_ext":"py","file_size_in_byte":2664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"609544429","text":"import gym\nimport rospy\nimport roslaunch\nimport time\nimport numpy as np\nfrom gym import utils, spaces\nfrom gym_gazebo.envs import gazebo_env\nfrom geometry_msgs.msg import Twist\nfrom std_srvs.srv import Empty\nfrom sensor_msgs.msg import LaserScan\nfrom gym.utils import seeding\nimport copy\nimport random\n\nfrom gazebo_msgs.srv import SpawnModel, DeleteModel\nfrom geometry_msgs.msg import Pose\n\n# ROS 2\n# import rclpy\n# from rclpy.qos import QoSProfile, qos_profile_sensor_data\n# from trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint # Used for publishing scara joint angles.\n# from control_msgs.msg import JointTrajectoryControllerState\n# from std_msgs.msg import String\n\nfrom trajectory_msgs.msg import JointTrajectory, JointTrajectoryPoint\nfrom control_msgs.msg import JointTrajectoryControllerState\nfrom baselines.agent.scara_arm.tree_urdf import treeFromFile # For KDL Jacobians\nfrom PyKDL import Jacobian, Chain, ChainJntToJacSolver, JntArray # For KDL Jacobians\n\n\n# from custom baselines repository\nfrom baselines.agent.utility.general_utils import forward_kinematics, get_ee_points, rotation_from_matrix, \\\n    get_rotation_matrix,quaternion_from_matrix # For getting points and velocities.\n\nclass MSG_INVALID_JOINT_NAMES_DIFFER(Exception):\n    \"\"\"Error object exclusively raised by _process_observations.\"\"\"\n    pass\n\n\nclass GazeboModularScara3DOFObstaclesv0Env(gazebo_env.GazeboEnv):\n    \"\"\"\n    This environment present a modular SCARA robot with a range finder at its\n    end pointing towards the workspace of the robot. The goal of this environment is\n    defined to reach the center of the \"H\" or the \"O\" from the \"H-ROS\" logo within the worspace.\n    This environment uses `slowness=1` and matches the delay between actions/observations\n    to this value (slowness). In other words, actions are taken at \"1/slowness\" rate.\n\n    Reward is determined ... (TODO: describe the heuristic or reward calculation method)\n    \"\"\"\n    def __init__(self):\n        \"\"\"\n        Initialize the SCARA environemnt\n            NOTE: This environment uses ROS and interfaces.\n\n            TODO: port everything to ROS 2 natively\n        \"\"\"\n        # Launch the simulation with the given launchfile name\n        gazebo_env.GazeboEnv.__init__(self, \"ModularScara3_v0.launch\")\n\n        # TODO: cleanup this variables, remove the ones that aren't used\n        # class variables\n        self._observation_msg = None\n        self.scale = None  # must be set from elsewhere based on observations\n        self.bias = None\n        self.x_idx = None\n        self.obs = None\n        self.reward = None\n        self.done = None\n        self.reward_dist = None\n        self.reward_ctrl = None\n        self.action_space = None\n        self.max_episode_steps = 1000 # limit the max episode step\n        # class variable that iterates to accounts for number of steps per episode\n        self.iterator = 0\n        # default to seconds\n        self.slowness = 1\n        self.slowness_unit = 'sec'\n\n        #############################\n        #   Environment hyperparams\n        #############################\n        # target, where should the agent reach\n        # EE_POS_TGT = np.asmatrix([0.3325683, 0.0657366, 0.3746]) # center of O\n        EE_POS_TGT = np.asmatrix([0.3305805, -0.1326121, 0.3746]) # center of the H\n        EE_ROT_TGT = np.asmatrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n        EE_POINTS = np.asmatrix([[0, 0, 0]])\n        EE_VELOCITIES = np.asmatrix([[0, 0, 0]])\n        # Initial joint position\n        INITIAL_JOINTS = np.array([0., 0., 0.])\n        # Used to initialize the robot, #TODO, clarify this more\n        STEP_COUNT = 2  # Typically 100.\n\n        # make sure that the slowness is set properly!! In case we forget them defaults to seconds.\n        # if self.slowness is None:\n        #     slowness = 1\n        #\n        # else:\n        #     slowness = self.slowness\n\n\n\n        # slowness = 100000000 # 1 is real life simulation\n        # slowness = 1 # use >10 for running trained network in the simulation\n\n        # Topics for the robot publisher and subscriber.\n        JOINT_PUBLISHER = '/scara_controller/command'\n        JOINT_SUBSCRIBER = '/scara_controller/state'\n        # joint names:\n        MOTOR1_JOINT = 'motor1'\n        MOTOR2_JOINT = 'motor2'\n        MOTOR3_JOINT = 'motor3'\n        # Set constants for links\n        WORLD = \"world\"\n        BASE = 'scara_e1_base_link'\n        BASE_MOTOR = 'scara_e1_base_motor'\n        #\n        SCARA_MOTOR1 = 'scara_e1_motor1'\n        SCARA_INSIDE_MOTOR1 = 'scara_e1_motor1_inside'\n        SCARA_SUPPORT_MOTOR1 = 'scara_e1_motor1_support'\n        SCARA_BAR_MOTOR1 = 'scara_e1_bar1'\n        SCARA_FIXBAR_MOTOR1 = 'scara_e1_fixbar1'\n        #\n        SCARA_MOTOR2 = 'scara_e1_motor2'\n        SCARA_INSIDE_MOTOR2 = 'scara_e1_motor2_inside'\n        SCARA_SUPPORT_MOTOR2 = 'scara_e1_motor2_support'\n        SCARA_BAR_MOTOR2 = 'scara_e1_bar2'\n        SCARA_FIXBAR_MOTOR2 = 'scara_e1_fixbar2'\n        #\n        SCARA_MOTOR3 = 'scara_e1_motor3'\n        SCARA_INSIDE_MOTOR3 = 'scara_e1_motor3_inside'\n        SCARA_SUPPORT_MOTOR3 = 'scara_e1_motor3_support'\n        SCARA_BAR_MOTOR3 = 'scara_e1_bar3'\n        SCARA_FIXBAR_MOTOR3 = 'scara_e1_fixbar3'\n        #\n        SCARA_RANGEFINDER = 'scara_e1_rangefinder'\n        EE_LINK = 'ee_link'\n        JOINT_ORDER = [MOTOR1_JOINT, MOTOR2_JOINT, MOTOR3_JOINT]\n        LINK_NAMES = [BASE, BASE_MOTOR,\n                      SCARA_MOTOR1, SCARA_INSIDE_MOTOR1, SCARA_SUPPORT_MOTOR1, SCARA_BAR_MOTOR1, SCARA_FIXBAR_MOTOR1,\n                      SCARA_MOTOR2, SCARA_INSIDE_MOTOR2, SCARA_SUPPORT_MOTOR2, SCARA_BAR_MOTOR2, SCARA_FIXBAR_MOTOR2,\n                      SCARA_MOTOR3, SCARA_INSIDE_MOTOR3, SCARA_SUPPORT_MOTOR3,\n                      EE_LINK]\n\n        reset_condition = {\n            'initial_positions': INITIAL_JOINTS,\n             'initial_velocities': []\n        }\n        #############################\n\n        # TODO: fix this and make it relative\n        # Set the path of the corresponding URDF file from \"assets\"\n        URDF_PATH = \"/home/erle/scara_e1_description/urdf/scara_e1_3joints.urdf\"\n\n        m_joint_order = copy.deepcopy(JOINT_ORDER)\n        m_link_names = copy.deepcopy(LINK_NAMES)\n        m_joint_publishers = copy.deepcopy(JOINT_PUBLISHER)\n        m_joint_subscribers = copy.deepcopy(JOINT_SUBSCRIBER)\n        ee_pos_tgt = EE_POS_TGT\n        ee_rot_tgt = EE_ROT_TGT\n        # Initialize target end effector position\n        ee_tgt = np.ndarray.flatten(get_ee_points(EE_POINTS, ee_pos_tgt, ee_rot_tgt).T)\n\n        self.realgoal = ee_tgt\n\n        self.environment = {\n            'T': STEP_COUNT,\n            'ee_points_tgt': ee_tgt,\n            'joint_order': m_joint_order,\n            'link_names': m_link_names,\n            # 'slowness': slowness,\n            'reset_conditions': reset_condition,\n            'tree_path': URDF_PATH,\n            'joint_publisher': m_joint_publishers,\n            'joint_subscriber': m_joint_subscribers,\n            'end_effector_points': EE_POINTS,\n            'end_effector_velocities': EE_VELOCITIES,\n            # 'num_samples': SAMPLE_COUNT,\n        }\n\n        # self.spec = {'timestep_limit': 5, 'reward_threshold':  950.0,}\n\n        # Subscribe to the appropriate topics, taking into account the particular robot\n        # ROS 1 implementation\n        self._pub = rospy.Publisher('/scara_controller/command', JointTrajectory)\n        self._sub = rospy.Subscriber('/scara_controller/state', JointTrajectoryControllerState, self.observation_callback)\n\n        # Initialize a tree structure from the robot urdf.\n        #   note that the xacro of the urdf is updated by hand.\n        # The urdf must be compiled.\n        _, self.ur_tree = treeFromFile(self.environment['tree_path'])\n        # Retrieve a chain structure between the base and the start of the end effector.\n        self.scara_chain = self.ur_tree.getChain(self.environment['link_names'][0], self.environment['link_names'][-1])\n        # print(\"nr of jnts: \", self.scara_chain.getNrOfJoints())\n        # Initialize a KDL Jacobian solver from the chain.\n        self.jac_solver = ChainJntToJacSolver(self.scara_chain)\n        #print(self.jac_solver)\n        self._observations_stale = [False for _ in range(1)]\n        #print(\"after observations stale\")\n        self._currently_resetting = [False for _ in range(1)]\n        self.reset_joint_angles = [None for _ in range(1)]\n\n        # TODO review with Risto, we might need the first observation for calling step()\n        # # taken from mujoco in OpenAi how to initialize observation space and action space.\n        # observation, _reward, done, _info = self.step(np.zeros(self.scara_chain.getNrOfJoints()))\n        # assert not done\n        # self.obs_dim = observation.size\n        self.obs_dim = self.scara_chain.getNrOfJoints() + 6 # hardcode it for now\n        # # print(observation, _reward)\n\n        # # Here idially we should find the control range of the robot. Unfortunatelly in ROS/KDL there is nothing like this.\n        # # I have tested this with the mujoco enviroment and the output is always same low[-1.,-1.], high[1.,1.]\n        # #bounds = self.model.actuator_ctrlrange.copy()\n        low = -np.pi/2.0 * np.ones(self.scara_chain.getNrOfJoints())\n        high = np.pi/2.0 * np.ones(self.scara_chain.getNrOfJoints())\n        # print(\"Action spaces: \", low, high)\n        self.action_space = spaces.Box(low, high)\n        high = np.inf*np.ones(self.obs_dim)\n        low = -high\n        self.observation_space = spaces.Box(low, high)\n\n        # self.action_space = spaces.Discrete(3) #F,L,R\n        # self.reward_range = (-np.inf, np.inf)\n\n        # Gazebo specific services to start/stop its behavior and\n        # facilitate the overall RL environment\n        self.unpause = rospy.ServiceProxy('/gazebo/unpause_physics', Empty)\n        self.pause = rospy.ServiceProxy('/gazebo/pause_physics', Empty)\n        self.reset_proxy = rospy.ServiceProxy('/gazebo/reset_simulation', Empty)\n        self.add_model = rospy.ServiceProxy('/gazebo/spawn_urdf_model', SpawnModel)\n        self.remove_model = rospy.ServiceProxy('/gazebo/delete_model', DeleteModel)\n\n        model_xml = \"<?xml version=\\\"1.0\\\"?> \\\n                    <robot name=\\\"myfirst\\\"> \\\n                      <link name=\\\"world\\\"> \\\n                      </link>\\\n                      <link name=\\\"cylinder0\\\">\\\n                        <visual>\\\n                          <geometry>\\\n                            <sphere radius=\\\"0.01\\\"/>\\\n                          </geometry>\\\n                          <origin xyz=\\\"0 0 0\\\"/>\\\n                          <material name=\\\"rojotransparente\\\">\\\n                              <ambient>0.5 0.5 1.0 0.1</ambient>\\\n                              <diffuse>0.5 0.5 1.0 0.1</diffuse>\\\n                          </material>\\\n                        </visual>\\\n                        <inertial>\\\n                          <mass value=\\\"5.0\\\"/>\\\n                          <inertia ixx=\\\"1.0\\\" ixy=\\\"0.0\\\" ixz=\\\"0.0\\\" iyy=\\\"1.0\\\" iyz=\\\"0.0\\\" izz=\\\"1.0\\\"/>\\\n                        </inertial>\\\n                      </link>\\\n                      <joint name=\\\"world_to_base\\\" type=\\\"fixed\\\"> \\\n                        <origin xyz=\\\"0 0 0\\\" rpy=\\\"0 0 0\\\"/>\\\n                        <parent link=\\\"world\\\"/>\\\n                        <child link=\\\"cylinder0\\\"/>\\\n                      </joint>\\\n                      <gazebo reference=\\\"cylinder0\\\">\\\n                        <material>Gazebo/GreenTransparent</material>\\\n                      </gazebo>\\\n                    </robot>\"\n        robot_namespace = \"\"\n        pose = Pose()\n        pose.position.x = EE_POS_TGT[0,0];\n        pose.position.y = EE_POS_TGT[0,1];\n        pose.position.z = 0.055#EE_POS_TGT[0,2];\n        pose.orientation.x = 0;\n        pose.orientation.y= 0;\n        pose.orientation.z = 0;\n        pose.orientation.w = 0;\n        reference_frame = \"\"\n        rospy.wait_for_service('/gazebo/spawn_urdf_model')\n        self.add_model(model_name=\"target\",\n                        model_xml=model_xml,\n                        robot_namespace=\"\",\n                        initial_pose=pose,\n                        reference_frame=\"\")\n\n        # Seed the environment\n        self._seed()\n\n        # self.max_steps_episode = 1000\n    def init_time(self, slowness =1, slowness_unit='sec'):\n            self.slowness = slowness\n            self.slowness_unit = slowness_unit\n            print(\"slowness: \", self.slowness)\n            print(\"slowness_unit: \", self.slowness_unit, \"type of variable: \", type(slowness_unit))\n\n    def randomizeCorrect(self):\n        print(\"calling randomize correct\")\n\n        # EE_POS_TGT_1 = np.asmatrix([0.3325683, 0.0657366, 0.3746]) # center of O\n        EE_POS_TGT_2 = np.asmatrix([0.3305805, -0.1326121, 0.3746]) # center of the H\n        EE_ROT_TGT = np.asmatrix([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n        EE_POINTS = np.asmatrix([[0, 0, 0]])\n\n        ee_pos_tgt_1 = EE_POS_TGT_1\n        ee_pos_tgt_2 = EE_POS_TGT_2\n\n        # leave rotation target same since in scara we do not have rotation of the end-effector\n        ee_rot_tgt = EE_ROT_TGT\n\n        # Initialize target end effector position\n        # ee_tgt = np.ndarray.flatten(get_ee_points(EE_POINTS, ee_pos_tgt, ee_rot_tgt).T)\n\n        target1 = np.ndarray.flatten(get_ee_points(EE_POINTS, ee_pos_tgt_1, ee_rot_tgt).T)\n        target2 = np.ndarray.flatten(get_ee_points(EE_POINTS, ee_pos_tgt_2, ee_rot_tgt).T)\n\n        \"\"\"\n        This is for initial test only, we need to change this in the future to be more realistic.\n        E.g. covered target -> go to other target. This could be implemented for example with vision.\n        \"\"\"\n        self.realgoal = target1 if np.random.uniform() < 0.5 else target2\n        print(\"randomizeCorrect realgoal: \", self.realgoal)\n        #self.realgoal = #ee_tgt = np.ndarray.flatten(get_ee_points(EE_POINTS, ee_pos_tgt, ee_rot_tgt).T)#np.array([self.np_random.choice([0, 1, 2, 3])])\n        # 0 = obstacle. 1 = no obstacle.\n        # self.realgoal = 0\n        # EE_POS_TGT = np.asmatrix([0.3325683, 0.0657366, 0.4868]) # center of O\n        # EE_POS_TGT = np.asmatrix([0.3305805, -0.1326121, 0.4868]) # center of the H\n\n\n    def observation_callback(self, message):\n        \"\"\"\n        Callback method for the subscriber of JointTrajectoryControllerState\n        \"\"\"\n        self._observation_msg =  message\n\n    def get_trajectory_message(self, action, robot_id=0):\n        \"\"\"\n        Helper function.\n        Wraps an action vector of joint angles into a JointTrajectory message.\n        The velocities, accelerations, and effort do not control the arm motion\n        \"\"\"\n        # Set up a trajectory message to publish.\n        action_msg = JointTrajectory()\n        action_msg.joint_names = self.environment['joint_order']\n        # Create a point to tell the robot to move to.\n        target = JointTrajectoryPoint()\n        action_float = [float(i) for i in action]\n        target.positions = action_float\n        # These times determine the speed at which the robot moves:\n        # it tries to reach the specified target position in 'slowness' time.\n        if (self.slowness_unit == 'sec') or (self.slowness_unit is None):\n            target.time_from_start.secs = self.slowness\n        elif (self.slowness_unit == 'nsec'):\n            target.time_from_start.nsecs = self.slowness\n        else:\n            print(\"Unrecognized unit. Please use sec or nsec.\")\n        # target.time_from_start.nsecs = self.environment['slowness']\n        # Package the single point into a trajectory of points with length 1.\n        action_msg.points = [target]\n        return action_msg\n\n    def process_observations(self, message, agent, robot_id=0):\n        \"\"\"\n        Helper fuinction to convert a ROS message to joint angles and velocities.\n        Check for and handle the case where a message is either malformed\n        or contains joint values in an order different from that expected observation_callback\n        in hyperparams['joint_order']\n        \"\"\"\n        if not message:\n            print(\"Message is empty\");\n            # return None\n        else:\n            # # Check if joint values are in the expected order and size.\n            if message.joint_names != agent['joint_order']:\n                # Check that the message is of same size as the expected message.\n                if len(message.joint_names) != len(agent['joint_order']):\n                    raise MSG_INVALID_JOINT_NAMES_DIFFER\n\n                # Check that all the expected joint values are present in a message.\n                if not all(map(lambda x,y: x in y, message.joint_names,\n                    [self._valid_joint_set[robot_id] for _ in range(len(message.joint_names))])):\n                    raise MSG_INVALID_JOINT_NAMES_DIFFER\n                    print(\"Joints differ\")\n            return np.array(message.actual.positions) # + message.actual.velocities\n\n    def get_jacobians(self, state, robot_id=0):\n        \"\"\"\n        Produce a Jacobian from the urdf that maps from joint angles to x, y, z.\n        This makes a 6x6 matrix from 6 joint angles to x, y, z and 3 angles.\n        The angles are roll, pitch, and yaw (not Euler angles) and are not needed.\n        Returns a repackaged Jacobian that is 3x6.\n        \"\"\"\n        # Initialize a Jacobian for self.scara_chain.getNrOfJoints() joint angles by 3 cartesian coords and 3 orientation angles\n        jacobian = Jacobian(self.scara_chain.getNrOfJoints())\n        # Initialize a joint array for the present self.scara_chain.getNrOfJoints() joint angles.\n        angles = JntArray(self.scara_chain.getNrOfJoints())\n        # Construct the joint array from the most recent joint angles.\n        for i in range(self.scara_chain.getNrOfJoints()):\n            angles[i] = state[i]\n        # Update the jacobian by solving for the given angles.observation_callback\n        self.jac_solver.JntToJac(angles, jacobian)\n        # Initialize a numpy array to store the Jacobian.\n        J = np.array([[jacobian[i, j] for j in range(jacobian.columns())] for i in range(jacobian.rows())])\n        # Only want the cartesian position, not Roll, Pitch, Yaw (RPY) Angles\n        ee_jacobians = J\n        return ee_jacobians\n\n    def get_ee_points_jacobians(self, ref_jacobian, ee_points, ref_rot):\n        \"\"\"\n        Get the jacobians of the points on a link given the jacobian for that link's origin\n        :param ref_jacobian: 6 x 6 numpy array, jacobian for the link's origin\n        :param ee_points: N x 3 numpy array, points' coordinates on the link's coordinate system\n        :param ref_rot: 3 x 3 numpy array, rotational matrix for the link's coordinate system\n        :return: 3N x 6 Jac_trans, each 3 x 6 numpy array is the Jacobian[:3, :] for that point\n                 3N x 6 Jac_rot, each 3 x 6 numpy array is the Jacobian[3:, :] for that point\n        \"\"\"\n        ee_points = np.asarray(ee_points)\n        ref_jacobians_trans = ref_jacobian[:3, :]\n        ref_jacobians_rot = ref_jacobian[3:, :]\n        end_effector_points_rot = np.expand_dims(ref_rot.dot(ee_points.T).T, axis=1)\n        ee_points_jac_trans = np.tile(ref_jacobians_trans, (ee_points.shape[0], 1)) + \\\n                                        np.cross(ref_jacobians_rot.T, end_effector_points_rot).transpose(\n                                            (0, 2, 1)).reshape(-1, self.scara_chain.getNrOfJoints())\n        ee_points_jac_rot = np.tile(ref_jacobians_rot, (ee_points.shape[0], 1))\n        return ee_points_jac_trans, ee_points_jac_rot\n\n    def get_ee_points_velocities(self, ref_jacobian, ee_points, ref_rot, joint_velocities):\n        \"\"\"\n        Get the velocities of the points on a link\n        :param ref_jacobian: 6 x 6 numpy array, jacobian for the link's origin\n        :param ee_points: N x 3 numpy array, points' coordinates on the link's coordinate system\n        :param ref_rot: 3 x 3 numpy array, rotational matrix for the link's coordinate system\n        :param joint_velocities: 1 x 6 numpy array, joint velocities\n        :return: 3N numpy array, velocities of each point\n        \"\"\"\n        ref_jacobians_trans = ref_jacobian[:3, :]\n        ref_jacobians_rot = ref_jacobian[3:, :]\n        ee_velocities_trans = np.dot(ref_jacobians_trans, joint_velocities)\n        ee_velocities_rot = np.dot(ref_jacobians_rot, joint_velocities)\n        ee_velocities = ee_velocities_trans + np.cross(ee_velocities_rot.reshape(1, 3),\n                                                       ref_rot.dot(ee_points.T).T)\n        return ee_velocities.reshape(-1)\n\n    def take_observation(self):\n        \"\"\"\n        Take observation from the environment and return it.\n        TODO: define return type\n        \"\"\"\n        # Take an observation\n        # done = False\n\n        obs_message = self._observation_msg\n        if obs_message is None:\n            # print(\"last_observations is empty\")\n            return None\n\n        # Collect the end effector points and velocities in\n        # cartesian coordinates for the process_observationsstate.\n        # Collect the present joint angles and velocities from ROS for the state.\n        last_observations = self.process_observations(obs_message, self.environment)\n        # # # Get Jacobians from present joint angles and KDL trees\n        # # # The Jacobians consist of a 6x6 matrix getting its from from\n        # # # (# joint angles) x (len[x, y, z] + len[roll, pitch, yaw])\n        ee_link_jacobians = self.get_jacobians(last_observations)\n        if self.environment['link_names'][-1] is None:\n            print(\"End link is empty!!\")\n            return None\n        else:\n            # print(self.environment['link_names'][-1])\n            trans, rot = forward_kinematics(self.scara_chain,\n                                        self.environment['link_names'],\n                                        last_observations[:self.scara_chain.getNrOfJoints()],\n                                        base_link=self.environment['link_names'][0],\n                                        end_link=self.environment['link_names'][-1])\n            # #\n            rotation_matrix = np.eye(4)\n            rotation_matrix[:3, :3] = rot\n            rotation_matrix[:3, 3] = trans\n            # angle, dir, _ = rotation_from_matrix(rotation_matrix)\n            # #\n            # current_quaternion = np.array([angle]+dir.tolist())#\n\n            # I need this calculations for the new reward function, need to send them back to the run scara or calculate them here\n            current_quaternion = quaternion_from_matrix(rotation_matrix)\n            current_ee_tgt = np.ndarray.flatten(get_ee_points(self.environment['end_effector_points'],\n                                                              trans,\n                                                              rot).T)\n            ee_points = current_ee_tgt - self.realgoal#self.environment['ee_points_tgt']\n            ee_points_jac_trans, _ = self.get_ee_points_jacobians(ee_link_jacobians,\n                                                                   self.environment['end_effector_points'],\n                                                                   rot)\n            ee_velocities = self.get_ee_points_velocities(ee_link_jacobians,\n                                                           self.environment['end_effector_points'],\n                                                           rot,\n                                                           last_observations)\n\n            # Concatenate the information that defines the robot state\n            # vector, typically denoted asrobot_id 'x'.\n            state = np.r_[np.reshape(last_observations, -1),\n                          np.reshape(ee_points, -1),\n                          np.reshape(ee_velocities, -1),]\n\n            return np.r_[np.reshape(last_observations, -1),\n                          np.reshape(ee_points, -1),\n                          np.reshape(ee_velocities, -1),]\n\n    def rmse_func(self, ee_points):\n        \"\"\"\n        Computes the Residual Mean Square Error of the difference between current and desired end-effector position\n        \"\"\"\n        rmse = np.sqrt(np.mean(np.square(ee_points), dtype=np.float32))\n        return rmse\n\n    def _seed(self, seed=None):\n        self.np_random, seed = seeding.np_random(seed)\n        return [seed]\n\n    def step(self, action):\n        \"\"\"\n        Implement the environment step abstraction. Execute action and returns:\n            - reward\n            - done (status)\n            - action\n            - observation\n            - dictionary (#TODO clarify)\n        \"\"\"\n        self.iterator+=1\n        # # Pause simulation\n        # rospy.wait_for_service('/gazebo/pause_physics')\n        # try:\n        #     #resp_pause = pause.call()\n        #     self.pause()\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/pause_physics service call failed\")\n\n        # Take an observation\n        # TODO: program this better, check that ob is not None, etc.\n        # self.ob = take_observation()\n\n        # self.reward_dist = self.rmse_func(self.ob[3:6])\n        # # print(\"reward_dist :\", self.reward_dist)\n        # self.reward = 1 - self.reward_dist # Make the reward increase as the distance decreases\n        #\n        # # Calculate if the env has been solved\n        # done = bool(abs(self.reward_dist) < 0.005)\n\n        # reward as in the enviroment for 4DOF commented out\n        self.reward_dist = -self.rmse_func(self.ob[self.scara_chain.getNrOfJoints():(self.scara_chain.getNrOfJoints()+3)])\n\n        # here we want to fetch the positions of the end-effector which are nr_dof:nr_dof+3\n        if(self.rmse_func(self.ob[self.scara_chain.getNrOfJoints():(self.scara_chain.getNrOfJoints()+3)])<0.005):\n            self.reward = 1 - self.rmse_func(self.ob[self.scara_chain.getNrOfJoints():(self.scara_chain.getNrOfJoints()+3)]) # Make the reward increase as the distance decreases\n            print(\"Reward is: \", self.reward)\n        else:\n            self.reward = self.reward_dist\n\n        # print(\"reward: \", self.reward)\n        # print(\"rmse_func: \", self.rmse_func(ee_points))\n\n        # # enviroment V2 reward\n        # self.reward_dist = self.rmse_func(self.ob[3:6])\n        # # print(\"reward_dist :\", self.reward_dist)\n        # self.reward = 1 - self.reward_dist # Make the reward increase as the distance decreases\n\n        # Calculate if the env has been solved\n        done = (bool(abs(self.reward_dist) < 0.005)) or (self.iterator > self.max_episode_steps)\n\n        # # Unpause simulation\n        # rospy.wait_for_service('/gazebo/unpause_physics')\n        # try:\n        #     self.unpause()\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/unpause_physics service call failed\")\n\n        # Execute \"action\"\n        # if rclpy.ok(): # ROS 2 code\n        self._pub.publish(self.get_trajectory_message(action[:self.scara_chain.getNrOfJoints()]))\n\n        #TODO: wait until action gets executed\n        # When adding this the algorithm does not converge\n        # time.sleep(int(self.environment['slowness']))\n        # time.sleep(int(self.environment['slowness'])/1000000000) # using nanoseconds\n\n        # # Pause simulation\n        # rospy.wait_for_service('/gazebo/pause_physics')\n        # try:\n        #     #resp_pause = pause.call()\n        #     self.pause()\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/pause_physics service call failed\")\n\n        # # Take an observation\n        # TODO: program this better, check that ob is not None, etc.\n        self.ob = self.take_observation()\n        while(self.ob is None):\n            self.ob = self.take_observation()\n        # print(\"in step, action: \", action[:3])\n        # print(\"in step, observation: \", self.ob[:3])\n\n        # Return the corresponding observations, rewards, etc.\n        # TODO, understand better what's the last object to return\n        return self.ob, self.reward, done, {}\n\n    def reset(self):\n        # \"\"\"\n        # Reset the agent for a particular experiment condition.\n        # \"\"\"\n        # # Resets the state of the environment and returns an initial observation.\n        # rospy.wait_for_service('/gazebo/reset_simulation')\n        # try:\n        #     #reset_proxy.call()\n        #     self.reset_proxy()\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/reset_simulation service call failed\")\n        #.,\n        # # Unpause simulation\n        # rospy.wait_for_service('/gazebo/unpause_physics')\n        # try:\n        #     self.unpause()\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/unpause_physics service call failed\")\n\n        # Go to initial position and wait until it arrives there\n        # self._pub.publish(self.get_trajectory_message(self.environment['reset_conditions']['initial_positions']))\n        # time.sleep(int(self.environment['slowness']))\n        # time.sleep(int(self.environment['slowness'])/1000000000) # using nanoseconds\n\n        # # Pause the simulation\n        # rospy.wait_for_service('/gazebo/pause_physics')\n        # try:\n        #     self.pause()\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/pause_physics service call failed\")\n\n        # try:\n        #     self.remove_model(model_name=\"obstacle\")\n        # except (rospy.ServiceException) as e:\n        #     print (\"/gazebo/spawn_urdf_model service call failed\")\n\n        rospy.wait_for_service('/gazebo/spawn_urdf_model')\n                            #   <material name=\\\"green\\\">\\\n                            #     <color rgba=\\\"0 0.8 .8 1\\\"/>\\\n                            #   </material>\\\n\n        try:\n            model_xml = \"<?xml version=\\\"1.0\\\"?> \\\n                        <robot name=\\\"myfirst\\\"> \\\n                          <link name=\\\"world\\\"> \\\n                          </link>\\\n                          <link name=\\\"cylinder0\\\">\\\n                            <visual>\\\n                              <geometry>\\\n                                <cylinder length=\\\"0.6\\\" radius=\\\"0.01\\\"/>\\\n                              </geometry>\\\n                              <origin xyz=\\\"0 0 0\\\"/>\\\n                              <material name=\\\"blue\\\">\\\n                                  <ambient>0.5 0.5 1.0 0.1</ambient>\\\n                                  <diffuse>0.5 0.5 1.0 0.1</diffuse>\\\n                              </material>\\\n                            </visual>\\\n                            <inertial>\\\n                              <mass value=\\\"5.0\\\"/>\\\n                              <inertia ixx=\\\"1.0\\\" ixy=\\\"0.0\\\" ixz=\\\"0.0\\\" iyy=\\\"1.0\\\" iyz=\\\"0.0\\\" izz=\\\"1.0\\\"/>\\\n                            </inertial>\\\n                            <collision>\\\n                              <geometry>\\\n                                <cylinder length=\\\"0.6\\\" radius=\\\"0.01\\\"/>\\\n                              </geometry>\\\n                            </collision>\\\n                          </link>\\\n                          <joint name=\\\"world_to_base\\\" type=\\\"fixed\\\"> \\\n                            <origin xyz=\\\"0 0 0.3\\\" rpy=\\\"0 0 0\\\"/>\\\n                            <parent link=\\\"world\\\"/>\\\n                            <child link=\\\"cylinder0\\\"/>\\\n                          </joint>\\\n                          <gazebo reference=\\\"cylinder0\\\">\\\n                            <material>Gazebo/Blue</material>\\\n                          </gazebo>\\\n                        </robot>\"\n            robot_namespace = \"\"\n            pose = Pose()\n            number = random.randint(0, 4)\n            if number == 1:\n                pose.position.x = 0.25;\n                pose.position.y = 0.07;\n            if number == 2:\n                pose.position.x = 0.25;\n                pose.position.y = -0.07;\n            if number == 3:\n                pose.position.x = 0.23;\n                pose.position.y = -0.087;\n            if number == 4:\n                pose.position.x = 0.23;\n                pose.position.y = 0.087;\n            pose.position.z = 0.0;\n            pose.orientation.x = 0;\n            pose.orientation.y= 0;\n            pose.orientation.z = 0;\n            pose.orientation.w = 0;\n            reference_frame = \"\"\n            if number > -1:\n              self.add_model(model_name=\"obstacle\",\n                            model_xml=model_xml,\n                            robot_namespace=robot_namespace,\n                            initial_pose=pose,\n                            reference_frame=\"\")\n        except (rospy.ServiceException) as e:\n            print (\"/gazebo/spawn_urdf_model service call failed\")\n\n        self.iterator = 0\n        msg = self.get_trajectory_message(self.environment['reset_conditions']['initial_positions'])\n        msg.points[0].time_from_start.secs = 0\n        msg.points[0].time_from_start.nsecs = 10000\n        self._pub.publish(msg)\n\n        # time.sleep(int(self.environment['slowness'])) # using seconds\n        # time.sleep(int(self.environment['slowness'])/1000000000) # using nanoseconds\n        if (self.slowness_unit == 'sec') or (self.slowness_unit is None):\n            time.sleep(int(self.slowness))\n        elif(self.slowness_unit == 'nsec'):\n            time.sleep(int(self.slowness/1000000000)) # using nanoseconds\n        else:\n            print(\"Unrecognized unit. Please use sec or nsec.\")\n\n        # Take an observation\n        self.ob = self.take_observation()\n        while(self.ob is None):\n            self.ob = self.take_observation()\n\n        # Return the corresponding observation\n        return self.ob\n","sub_path":"educational-resources/robotics/gym-gazebo-master/gym_gazebo/envs/modular_scara/gazebo_modular_scara_3dof_obstacles_v0.py","file_name":"gazebo_modular_scara_3dof_obstacles_v0.py","file_ext":"py","file_size_in_byte":33632,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"493713081","text":"#for stock and file export \n\nimport sqlite3\nimport pandas as pd\nimport os.path\nfrom tkinter import *\nfrom tkinter import messagebox\nimport datetime\nimport getstoredata as gstrdata\nfrom tkinter import ttk\n\ndef exportfile():\n    exbox=Toplevel()\n    exbox.title(\"Export window\")\n    photo=PhotoImage(file='exportwindow.png')\n    LT=Label(exbox,image=photo)\n    LT.pack(side='top',pady=20)\n    def stock(): # for stock export to Excel\n        tod=str(datetime.date.today().strftime(\"%d%m\"))\n        b=str(datetime.datetime.now().time().strftime(\"%H%M\"))\n        fname=\"stockfile-\"+tod+\"-\"+b+\".xlsx\"\n        con=sqlite3.connect(\"MSW.db\")\n        df=pd.read_sql_query(\"SELECT * from stock\",con)\n        writer=pd.ExcelWriter(fname)\n        df.to_excel(writer,'sheet1','B1',index=False)\n        writer.save()\n        con.close()\n        mes=fname+\" has been created successfully!!\"\n        messagebox.showinfo(parent=exbox,title=\"Success\",message=mes)\n        return\n    def sale(): # for sales export to Excel\n        if os.path.isfile(\"salefile.xlsx\"):\n            os.remove(\"salefile.xlsx\")\n        top=Toplevel()\n        top.geometry(\"500x500\")\n        def intval(ins,maxlen,value):\n            try:\n                val=int(value)    \n                maxlen=int(maxlen)\n                if ins=='1':\n                    if not ((isinstance(val,int) and len(value)<maxlen)):\n                        return False\n                return True\n            except:\n                if not len(value)==0:\n                    return False\n                return True\n        def block(ins):    \n            if ins=='1' or ins=='0':\n                return False\n            elif ins=='-1':\n                return True\n        Lit=Label(top,text=\"Item type\")\n        Lit.grid(row=1,column=5)\n        Ei=ttk.Combobox(top,width=20,values=gstrdata.getitemtype(),validate='key')\n        Ei.grid(row=1,column=6)\n        Ei['validatecommand']=(Ei.register(block),'%d')\n        Ld=Label(top,text=\"DD\")\n        Ld.grid(row=0,column=2)\n        Lm=Label(top,text=\"MM\")\n        Lm.grid(row=0,column=3)\n        Ly=Label(top,text='YYYY')\n        Ly.grid(row=0,column=4)\n        Lf=Label(top,text=\"Start Date\")\n        Lf.grid(row=1,column=1)\n        Esd=Entry(top,text=\"start date dd\",bd=2,validate=\"key\",width=2)\n        Esd.grid(row=1,column=2)\n        Esd[\"validatecommand\"]=(Esd.register(intval),'%d',3,'%P')\n        Esm=Entry(top,text=\"start date mm\",bd=2,validate=\"key\",width=2)\n        Esm.grid(row=1,column=3)\n        Esm[\"validatecommand\"]=(Esm.register(intval),'%d',3,'%P')\n        Esy=Entry(top,text=\"start date yyyy\",bd=2,validate=\"key\",width=4)\n        Esy.grid(row=1,column=4)\n        Esy[\"validatecommand\"]=(Esy.register(intval),'%d',5,'%P')\n        Le=Label(top,text=\"End Date\")\n        Le.grid(row=2,column=1)\n        Eed=Entry(top,text=\"End date dd\",bd=2,validate=\"key\",width=2)\n        Eed.grid(row=2,column=2)\n        Eed[\"validatecommand\"]=(Eed.register(intval),'%d',3,'%P')\n        Eem=Entry(top,text=\"End date mm\",bd=2,validate=\"key\",width=2)\n        Eem.grid(row=2,column=3)\n        Eem[\"validatecommand\"]=(Eem.register(intval),'%d',3,'%P')\n        Eey=Entry(top,text=\"End date yyyy\",bd=2,validate=\"key\",width=4)\n        Eey.grid(row=2,column=4)\n        Eey[\"validatecommand\"]=(Eey.register(intval),'%d',5,'%P')\n        def sub():\n            a=Esd.get() and Esm.get() and Esy.get() and Eed.get() and Eem.get() and Eey.get()\n            if a:\n                sd=int(Esd.get())\n                sm=int(Esm.get())\n                sy=int(Esy.get())\n                ed=int(Eed.get())\n                em=int(Eem.get())\n                ey=int(Eey.get())\n                try:\n                    stdate=datetime.date(sy,sm,sd)#start date\n                    endate=datetime.date(ey,em,ed)#end date\n                    if stdate>datetime.date.today() or endate>datetime.date.today() or stdate>endate:\n                        print (1/0)\n                    con=sqlite3.connect(\"MSW.db\")\n                    cur=con.execute(\"select bill,date from sales\")\n                    tel=cur.fetchall()\n                    lis=[]\n                    def reverse(s):\n                        rv=''\n                        for l in s:\n                            rv=l+rv\n                        return rv\n                    for i in tel:\n                        ref=0\n                        yr=''\n                        mn=''\n                        da=''\n                        item=i[1]\n                        billn=i[0]\n                        for j in item:\n                            if ref==0:\n                                if j!='-':\n                                    da=j+da\n                                else:\n                                    ref=1\n                            elif ref==1:\n                                if j!='-':\n                                    mn=j+mn\n                                else:\n                                    ref=2\n                            else:\n                                if ref==2 and j!='-':\n                                    yr=j+yr\n                        da=int(reverse(da))\n                        mn=int(reverse(mn))\n                        yr=int(reverse(yr))\n                        dat=datetime.date(yr,mn,da)\n                        if stdate<=dat<=endate and billn not in lis:\n                            lis.append(billn)\n                    std=str(stdate.strftime(\"%d%m\"))\n                    endte=str(endate.strftime(\"%d%m\"))\n                    entime=str(datetime.datetime.now().time().strftime(\"%H%M\"))\n                    fname=\"salesfile \"+std+\"-\"+endte+\"-\"+entime+\".xlsx\"\n                    itsel=Ei.get()\n                    con.execute(\"DROP TABLE IF EXISTS temp\")\n                    con.execute(\"DROP TABLE IF EXISTS temp1\")\n                    con.commit()\n                    sql=\"CREATE TABLE IF NOT EXISTS temp AS SELECT Itemcode,Description,Qty,utype,HSN,Price,CGST,SGST,discount,cost,AadharNumber,CustomerDetail,Bill,Date,Return,Itemtype FROM sales WHERE bill IN ({seq})\".format(seq=','.join(['?']*len(lis)))\n                    con.execute(sql,lis)\n                    if itsel=='':\n                        sql=\"CREATE TABLE IF NOT EXISTS temp1 AS SELECT Itemcode,Description,Qty,utype,HSN,Price,CGST,SGST,discount,cost,AadharNumber,CustomerDetail,Bill,Date,Return,Itemtype FROM temp WHERE itemtype is '%s'\"%(itsel)\n                        con.execute(sql)\n                        df=pd.read_sql_query(\"SELECT * from temp1\",con)\n                    else:\n                        df=pd.read_sql_query(\"SELECT * from temp\",con)\n                    writer=pd.ExcelWriter(fname)\n                    df.to_excel(writer,'sheet1','B1',index=False)\n                    writer.save()\n                    mes=fname+\"has been successfully created!!!\"\n                    messagebox.showinfo(parent=top,title=\"Success\",message=mes)\n                    con.execute(\"DROP TABLE IF EXISTS temp\")\n                    con.execute(\"DROP TABLE IF EXISTS temp1\")\n                    con.commit()\n                    con.close()\n                    Esd.delete(0,'end')\n                    Esm.delete(0,'end')\n                    Esy.delete(0,'end')\n                    Eed.delete(0,'end')\n                    Eem.delete(0,'end')\n                    Eey.delete(0,'end')\n                    top.destroy()\n                except:\n                    messagebox.showinfo(parent=top,title=\"Error\",message=\"Wrong date!!!\")\n                    top.lift()\n            else:\n                messagebox.showinfo(parent=top,title=\"Error\",message=\"Enter all values\")\n                top.lift()\n                    \n            return\n        But=Button(top,text=\"Submit\",command=sub)\n        But.grid(row=3,column=5,padx=10,pady=10)\n    \n    Bst=Button(exbox,text=\"Export Stock Data\",command=stock)\n    Bst.pack(side='top',pady=10)\n    Bsa=Button(exbox,text=\"Export Sales Data\",command=sale)\n    Bsa.pack(side='top',pady=10)\n    def ex():\n        exbox.destroy()\n        return\n    exbut=Button(exbox,text=\"Exit\",command=ex)\n    exbut.pack(side='top',pady=50)\n    \n    exbox.mainloop()\n\n#exportfile()\n","sub_path":"fileexport.py","file_name":"fileexport.py","file_ext":"py","file_size_in_byte":8155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"439230221","text":"#!/usr/bin/env python3\n\nfrom subprojects.simlib.format import filter_subdirs, format_sources, simlib_sources\nimport sys\n\ndef sim_sources(srcdir):\n    return list(filter_subdirs(srcdir, [\n        'src/',\n        'src/include/sim/',\n        'src/job_server/',\n        'src/job_server/job_handlers/',\n        'src/lib/',\n        'src/sim_merger/',\n        'src/web_interface/',\n        'test/'\n    ], ['c', 'cc', 'h', 'hh'], [\n    ]))\n\nif __name__ == '__main__':\n    sources = sim_sources(sys.argv[1]) + simlib_sources(sys.argv[1] + '/subprojects/simlib')\n    print('files to format:', len(sources))\n    format_sources(sources)\n","sub_path":"format.py","file_name":"format.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"502136809","text":"from collections import namedtuple\n\nTarget = namedtuple('Target', ['owner', 'repo', 'branch', 'package_name', 'deps'])\n\ndef load_config(fname):\n    sources = []\n\n    with open(fname) as fp:\n        for line in fp:\n            if '#' in line:\n                line = line[:line.index('#')]\n            line = line.strip()\n            if not line:\n                continue\n            if line[0] == '!':\n                line = line[1:]\n                is_package = False\n            else:\n                is_package = True\n\n            if '->' in line:\n                source, deps = line.split('->')\n                source = source.strip()\n                deps = deps.strip()\n                deps = [dep.strip() for dep in deps.split(',')]\n            else:\n                source = line\n                deps = []\n\n            namespace, repo = source.split('/')\n            if is_package:\n                if ':' in repo:\n                    repo, package_name = repo.split(':')\n                else:\n                    package_name = repo\n            else:\n                package_name = None\n            sources.append(Target(\n                owner=namespace,\n                repo=repo,\n                branch='master',\n                package_name=package_name,\n                deps=deps))\n\n    return sources\n","sub_path":"ci-image/scripts/repos.py","file_name":"repos.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"572583509","text":"from Automata.automata import Automaton\nimport sys\nimport copy\n\n\"\"\"\nThis program converts a Non-Deterministic Finite\nAutomaton (NFA) to a Deterministic Finite Automaton \n(DFA).\n\"\"\"\n\ndef set_equal(a, b):\n\t# Verify if both sets are equal\n\treturn len(a - b)==0 and len(b - a)==0\n\ndef search_set(mapping, aux):\n\tfor state in mapping:\n\t\tif set_equal(mapping[state], aux):\n\t\t\treturn state\n\treturn None\n\ndef build_dfa(nfa, state_prefix=\"DFA\"):\n\t# Init DFA (\"Deterministic Finite Automaton\")\n\tdfa = Automaton(\n\t\talphabet=copy.deepcopy(nfa.alphabet),\n\t\ttransit_matrix={},\n\t\tinitial_state=None, \n\t\tfinal_state=set())\n\n\t# Initial configuration of the resultant automaton\n\tinitial_state_name = state_prefix + \"0\"\n\tdfa.initial_state = initial_state_name\n\tlist_to_proc = [initial_state_name]\n\tmapping = {initial_state_name : {nfa.initial_state}}\n\n\twhile len(list_to_proc):\n\t\tcur_state = list_to_proc.pop(0)\n\t\tdfa.transit_matrix[cur_state] = {}\n\n\t\tfor c in dfa.alphabet:\n\t\t\taux = set()\n\n\t\t\tfor nfa_state in mapping[cur_state]:\n\t\t\t\taux.update(nfa.transit_matrix[nfa_state][c])\n\n\t\t\tif aux:\n\t\t\t\tif search_set(mapping, aux) is None:\n\t\t\t\t\tnew_state_name = state_prefix + str(len(mapping))\n\t\t\t\t\tmapping[new_state_name] = aux\n\t\t\t\t\tlist_to_proc.append(new_state_name)\n\t\t\t\t\ttransit_name = new_state_name\n\t\t\t\t\tif nfa.final_state.intersection(aux):\n\t\t\t\t\t\tdfa.final_state.update({transit_name})\n\t\t\t\telse:\n\t\t\t\t\ttransit_name = search_set(mapping, aux)\n\t\t\telse:\n\t\t\t\ttransit_name = set()\n\n\t\t\tdfa.transit_matrix[cur_state][c] = transit_name\n\n\treturn dfa\t\n\nif __name__ == \"__main__\":\n\tif len(sys.argv) < 2:\n\t\tprint(\"usage:\", sys.argv[0], \n\t\t\t\"<nfa filepath> [output formated (0/1) - default to 0]\")\n\t\texit(1)\n\n\ttry:\n\t\toutput_formated = int(sys.argv[2])\n\texcept:\n\t\toutput_formated = False\n\n\tnfa = Automaton(sys.argv[1])\n\tdfa = build_dfa(nfa)\n\n\tif not output_formated:\n\t\tprint(\"NFA (Non-deterministic Finite Automaton):\")\n\t\tnfa.print()\n\t\tprint()\n\n\t\tprint(\"DFA (Deterministic Finite Automaton):\")\n\t\tdfa.print()\n\telse:\n\t\tdfa.gen_input_file()\n","sub_path":"automata/nfa-to-dfa.py","file_name":"nfa-to-dfa.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"293902452","text":"\n\n#calss header\nclass _ELECTROCUTE():\n\tdef __init__(self,): \n\t\tself.name = \"ELECTROCUTE\"\n\t\tself.definitions = [u'to kill someone by causing electricity to flow through their body: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_electrocute.py","file_name":"_electrocute.py","file_ext":"py","file_size_in_byte":356,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"154308895","text":"from sgppOkushiri import okushiriStorage\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport time\n# This is for verifying the parallely computed resutls\n# from raijin are equal to my locally serially computed resutls\n\ndim = 4\nnumTimeSteps = 451\ngridResolution = 128\nstorage = okushiriStorage(dim, numTimeSteps, gridResolution)\nqoi = 'all'\npar = [0., 0., 2., 2.]\ny, y_g5, y_g7, y_g9, y_bc = storage.eval(par, qoi, usePrecalc=True)\nplt.plot(range(len(y)), y, '-C0', label='y precalc')\nplt.plot(range(len(y_g5)), y_g5, '-C1', label='y_g5 precalc')\nplt.plot(range(len(y_g7)), y_g7, '-C2', label='y_g7 precalc')\nplt.plot(range(len(y_g9)), y_g9, '-C3', label='y_g9 precalc')\nplt.plot(range(len(y_bc)), y_bc, '-C4', label='y_bc precalc')\n\nstart = time.time()\ncalc_y, calc_y_g5, calc_y_g7, calc_y_g9, calc_y_bc = storage.eval(\n    par, qoi, usePrecalc=False)\nprint(f\"calculation took {time.time()-start}s\")\n\nprint(f\"diff y: {np.linalg.norm(calc_y - y)}\")\nprint(f\"diff y_g5: {np.linalg.norm(calc_y_g5 - y_g5)}\")\nprint(f\"diff y_g7: {np.linalg.norm(calc_y_g7 - y_g7)}\")\nprint(f\"diff y_g9: {np.linalg.norm(calc_y_g9 - y_g9)}\")\nprint(f\"diff y_bc: {np.linalg.norm(calc_y_bc - y_bc)}\")\n\nplt.plot(range(len(calc_y)), calc_y, '+C0', label='y new')\nplt.plot(range(len(calc_y_g5)), calc_y_g5, '+C1', label='y_g5 new')\nplt.plot(range(len(calc_y_g7)), calc_y_g7, '+C2', label='y_g7 new')\nplt.plot(range(len(calc_y_g9)), calc_y_g9, '+C3', label='y_g9 new')\nplt.plot(range(len(calc_y_bc)), calc_y_bc, '+C4', label='y_bc new')\n\n\nstorage.cleanUp()\nplt.legend()\nplt.show()\n","sub_path":"testOkushiri.py","file_name":"testOkushiri.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"610515488","text":"import gensim\nfrom gensim.models import Doc2Vec\nfrom gensim.models.doc2vec import TaggedDocument\nimport string\nfrom src.util import *\n\nclass Word2VecProvider(object):\n    \"\"\"\n    Interface to provide word2vec interface access on top of gensim\n    http://zablo.net/blog/post/twitter-sentiment-analysis-python-scikit-word2vec-nltk-xgboost\n    \"\"\"\n    word2vec = None\n    dimensions = 0\n\n    def load(self, path_to_word2vec):\n        \"\"\"\n        Load a word2vec embeddings file\n        :param path_to_word2vec: path to the file\n        :return: Nothing\n        \"\"\"\n        self.word2vec = gensim.models.KeyedVectors.load_word2vec_format(path_to_word2vec, binary=False)\n        self.word2vec.init_sims(replace=True)\n        self.dimensions = self.word2vec.vector_size\n\n    def get_vector(self, word):\n        \"\"\"\n        Get the vector associated to a given word. None if the word is not in vocabulary.\n        :param word: word to get the vector\n        :return: vector if word in vocab\n        \"\"\"\n        if word not in self.word2vec.vocab:\n            return None\n        return self.word2vec.syn0norm[self.word2vec.vocab[word].index]\n\n    def get_similarity(self, word1, word2):\n        \"\"\"\n        Get the cosine similarity between two words. None if one of them not in the vocabulary.\n        :param word1: first word\n        :param word2: second word\n        :return: similarity score\n        \"\"\"\n        if word1 not in self.word2vec.vocab or word2 not in self.word2vec.vocab:\n            return None\n\n        return self.word2vec.similarity(word1, word2)\n\n\ndef process_for_doc2vec(tweets, labels, pos_start=0, neu_start=0, neg_start=0):\n    \"\"\"\n    Change format of tweets and labels in annotated labels (positive_0, positive_1, neutral_0...) and list of tokens\n    Necessary for TaggedDocument from gensim\n    :param tweets: list of strings (tweets)\n    :param labels: list of strings (labels)\n    :param pos_start: a potential initial index for labeling (useful in order to stack several sources for training)\n    :param neu_start: a potential initial index for labeling (useful in order to stack several sources for training)\n    :param neg_start: a potential initial index for labeling (useful in order to stack several sources for training)\n    :return:\n    \"\"\"\n    positives, neutrals, negatives = [], [], []\n    for i, t in enumerate(zip(tweets, labels)):\n            if t[1] == 'positive':\n                positives.append(t)\n            if t[1] == 'neutral':\n                neutrals.append(t)\n            if t[1] == 'negative':\n                 negatives.append(t)\n    pos, neu, neg = [], [], []\n\n    for i,t in enumerate(positives):\n        tup = (t[0], t[1] + \"_\" + str(pos_start + i))\n        pos.append(tup)\n    for i,t in enumerate(neutrals):\n        tup = (t[0], t[1] + \"_\" + str(neu_start + i))\n        neu.append(tup)\n    for i,t in enumerate(negatives):\n        tup = (t[0], t[1] + \"_\" + str(neg_start + i))\n        neg.append(tup)\n    tweets = [t[0] for t in pos+neu+neg]\n    labels = [t[1] for t in pos+neu+neg]\n\n    strip_tweets = []\n    sentences = []\n    translator = str.maketrans('', '', string.punctuation)\n    for t in tweets:\n        strip_tweets.append(t.translate(translator).lower())\n    for t in strip_tweets:\n        words = t.split(' ')\n        sentences.append(words)\n    return sentences, labels\n\nclass LabeledLineSentence(object):\n    \"\"\"\n    Iterator class to browse documents and labels to construct a Doc2Vec model\n    \"\"\"\n    def __init__(self, doc_list, labels_list):\n       self.labels_list = labels_list\n       self.doc_list = doc_list\n    def __iter__(self):\n        \"\"\"\n        Iterate method that yields TaggedDocuments to train a model\n        :return: TaggedDocument iterator\n        \"\"\"\n        for idx, doc in enumerate(self.doc_list):\n            yield TaggedDocument(words=doc,tags=[self.labels_list[idx]])\n\ndef doc2vec_train(tweets, labels, min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=7, epochs=10):\n    \"\"\"\n    Train a doc2vec over the tweets and labels\n    :param tweets: list of strings (tweets)\n    :param labels: list of strings (labels)\n    :param min_count: ignore all words with total frequency lower than this\n    :param window: maximum distance between the predicted word and context words used for prediction within a document\n    :param size: dimensionality of the feature vectors\n    :param sample: threshold for configuring which higher-frequency words are randomly downsampled\n    :param negative:  if > 0, negative sampling will be used, the int for negative specifies how many “noise words”\n    should be drawn (usually between 5-20). Default is 5. If set to 0, no negative sampling is used\n    :param workers:  worker threads to train the model\n    :param epochs: number of epochs in training\n    :return: trained model\n    \"\"\"\n\n    model = Doc2Vec(min_count=min_count, window=window, size=size, sample=sample, negative=negative, workers=workers)\n    try:\n        model = Doc2Vec.load('doc2vec.pkl')\n    except FileNotFoundError:\n        print(\"No model saved. Training a new model on train data, and save to doc2vec.pkl\")\n        tweets, labels = process_for_doc2vec(tweets, labels)\n        labeled_line_sentences = LabeledLineSentence(tweets, labels)\n        model.build_vocab(labeled_line_sentences)\n        model.train(labeled_line_sentences, total_examples=len(list(labeled_line_sentences)), epochs=epochs)\n        try:\n            model.save('doc2vec.pkl')\n        except (pickle.PicklingError, FileNotFoundError) as e:\n            print(\"Model trained but could not be saved\")\n    return model\n\n\n# class Doc2VecExtractor(BaseEstimator, TransformerMixin):\n#     def __init__(self):\n#         self.labels = None\n#\n#     def transform(self, doc, y=None):\n#         sentences, labels = process_for_doc2vec(doc, self.labels)\n#         it = LabeledLineSentence(sentences, labels)\n#         model = Doc2Vec(min_count=1, window=10, size=100, sample=1e-4, negative=5, workers=7)\n#         model.build_vocab(it)\n#         model.train(it, total_examples=len(list(it)), epochs=10)\n#         train_arrays = np.zeros((len(labels), 100))\n#\n#         for i, label in enumerate(labels):\n#             train_arrays[i] = model.docvecs[label]\n#         print(train_arrays.shape)\n#         return train_arrays\n#\n#     def fit(self, X, y=None):\n#         self.labels = y\n#         return self","sub_path":"src/embeddings.py","file_name":"embeddings.py","file_ext":"py","file_size_in_byte":6383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"585347921","text":"from keras.models import Sequential\nfrom keras.layers.wrappers import TimeDistributed\nfrom keras.layers import Convolution2D,Activation,MaxPooling2D,Flatten,Dense,Embedding,GRU,RepeatVector,Merge\n\nmax_caption_len = 16\nvocab_size = 10000\n\n# first, let's define an image model that\n# will encode pictures into 128-dimensional vectors.\n# it should be initialized with pre-trained weights.\nimage_model = Sequential()\nimage_model.add(Convolution2D(32, 3, 3, border_mode='valid', input_shape=(3, 100, 100)))\nimage_model.add(Activation('relu'))\nimage_model.add(Convolution2D(32, 3, 3))\nimage_model.add(Activation('relu'))\nimage_model.add(MaxPooling2D(pool_size=(2, 2)))\n\nimage_model.add(Convolution2D(64, 3, 3, border_mode='valid'))\nimage_model.add(Activation('relu'))\nimage_model.add(Convolution2D(64, 3, 3))\nimage_model.add(Activation('relu'))\nimage_model.add(MaxPooling2D(pool_size=(2, 2)))\n\nimage_model.add(Flatten())\nimage_model.add(Dense(128))\n\n# let's load the weights from a save file.\nimage_model.load_weights('weight_file.h5')\n\n# next, let's define a RNN model that encodes sequences of words\n# into sequences of 128-dimensional word vectors.\nlanguage_model = Sequential()\nlanguage_model.add(Embedding(vocab_size, 256, input_length=max_caption_len))\nlanguage_model.add(GRU(output_dim=128, return_sequences=True))\nlanguage_model.add(TimeDistributed(Dense(128)))\n\n# let's repeat the image vector to turn it into a sequence.\nimage_model.add(RepeatVector(max_caption_len))\n\n# the output of both models will be tensors of shape (samples, max_caption_len, 128).\n# let's concatenate these 2 vector sequences.\nmodel = Sequential()\nmodel.add(Merge([image_model, language_model], mode='concat', concat_axis=-1))\n# let's encode this vector sequence into a single vector\nmodel.add(GRU(256, return_sequences=False))\n# which will be used to compute a probability\n# distribution over what the next word in the caption should be!\nmodel.add(Dense(vocab_size))\nmodel.add(Activation('softmax'))\n\nmodel.compile(loss='categorical_crossentropy', optimizer='rmsprop')\n\n# \"images\" is a numpy float array of shape (nb_samples, nb_channels=3, width, height).\n# \"captions\" is a numpy integer array of shape (nb_samples, max_caption_len)\n# containing word index sequences representing partial captions.\n# \"next_words\" is a numpy float array of shape (nb_samples, vocab_size)\n# containing a categorical encoding (0s and 1s) of the next word in the corresponding\n# partial caption.\n\n\n\n# model.fit([images, partial_captions], next_words, batch_size=16, nb_epoch=100)","sub_path":"keras-cnn-lstm/ImageCap.py","file_name":"ImageCap.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"623154178","text":"from __future__ import division\nfrom visual import *\n\n#Setup\nscene.height=640\nscene.width=640\nr = 1\ng = vector(0,0,-9.8)\nball = sphere(pos=(0,0,5),radius=r)\nball.ta = vector(0,0,0)\nball.aa = 0\nball.tv = vector(0,0,0)\nball.av = 0\nball.angle = 0\n\nwhile 1:\n    rate(100)\n    ball.ta = ball.ta + g\n    ball.tv += ball.ta\n    ball.pos += ball.tv\n    ball.av += ball.aa\n    ball.angle += ball.va\n    while (0 <= ball.angle and 360<bal.angle):\n        if ball.angle >= 360:\n            ball.angle -= 360\n        if ball.angle < 0:\n            ball.angle += 360\n    \n\n","sub_path":"spring.py","file_name":"spring.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"187580793","text":"import numpy as np\n\nfrom .base_transform import BaseTransform\n\n\nclass Centralize(BaseTransform):\n    r\"\"\"Centralize the input data to zero-centered. \n    \n    Let :math:`x_1, \\dots, x_N` be :math:`N` samples, the centralization does the following:\n    \n    .. math::\n        \\hat{x}_i = x_i - \\frac{1}{N}\\sum_{j=1}^{N} x_j, \\forall i\\in \\{ 1, \\dots, N \\}\n    \n    .. warning::\n    \n        The mean is calculated over the first dimension. This allows to deal with batched data with\n        shape ``[N, ...]`` where ``N`` is the batch size. However, be careful when you want to centralize\n        a multidimensional array with mean over all elements. This is not supported. \n    \n    Example::\n    \n        >>> centralize = Centralize()\n        >>> centralize([1, 2, 3, 4])\n        array([-1.5, -0.5,  0.5,  1.5], dtype=float32)\n        \n        >>> centralize([[1, 3], [2, 11]])\n        array([[-0.5, -4. ],\n               [ 0.5,  4. ]], dtype=float32)\n   \n    \"\"\"\n    def __call__(self, x, mean=None):\n        r\"\"\"Centralize the input data. \n        \n        Args:\n            x (object): input data\n            mean (ndarray): If not ``None``, then use this specific mean to centralize the input. \n            \n        Returns\n        -------\n        out : ndarray\n            centralized data\n        \"\"\"\n        assert not np.isscalar(x), 'does not support scalar value !'\n        \n        # Convert input to ndarray\n        x = self.to_numpy(x, np.float32)\n        \n        # Get mean\n        if mean is None:  # compute the mean\n            # keepdims=True very important ! otherwise wrong value\n            mean = x.mean(0, keepdims=True)  # over first dimension e.g. batch dim\n        else:  # use provided mean\n            mean = np.asarray(mean).astype(x.dtype)\n            \n        # Centralize the data\n        out = x - mean\n        \n        return out\n","sub_path":"lagom/core/transform/centralize.py","file_name":"centralize.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"293731957","text":"# -*- coding: utf-8 -*-\n# 参考廖雪峰大神的博客：\n# https://www.liaoxuefeng.com/wiki/0014316089557264a6b348958f449949df42a6d3a2e542c000/0014320027235877860c87af5544f25a8deeb55141d60c5000\nimport random\nfrom PIL import Image, ImageDraw, ImageFont, ImageFilter\n\n\nchar_list = []\nfor i in range(97, 123):\n    char_list.append(chr(i))\nfor i in range(65, 91):\n    char_list.append(chr(i))\nfor i in range(48, 58):\n    char_list.append(chr(i))\n\n\ndef random_char():\n    global char_list\n    return random.choice(char_list)\n\n\ndef random_color():\n    return random.randint(64, 255), random.randint(64, 255), random.randint(64, 255)\n\n\ndef random_color2():\n    return random.randint(32, 127), random.randint(32, 127), random.randint(32, 127)\n\n\nwidth = 240\nheight = 60\nimage = Image.new('RGB', (width, height), (255, 255, 255))\nfont = ImageFont.truetype('arial.ttf', 36)\ndraw = ImageDraw.Draw(image)\n\nfor x in range(width):\n    for y in range(height):\n        draw.point((x, y), fill=random_color())\n\nfor t in range(4):\n    draw.text((60*t+10, 10), random_char(), font=font, fill=random_color2())\n\nimage = image.filter(ImageFilter.BLUR)\nimage.save('captcha.jpg', 'jpeg')\n","sub_path":"008生成验证码/captacha code.py","file_name":"captacha code.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"102655868","text":"#!/usr/bin/python\n# -*- coding:UTF-8 -*-\n# -----------------------------------------------------------------------#\n# File Name: setup\n# Author: Junyi Li\n# Mail: 4ljy@163.com\n# Created Time: 2021-01-30\n# Description: \n# -----------------------------------------------------------------------#\n\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport setuptools\n\nfrom setuptools import setup\nfrom setuptools.command.develop import develop\nfrom setuptools.command.install import install\nfrom subprocess import call\n\nwith open('README.md', 'r') as f:\n    long_description = f.read()\n\n\nclass Installation(install):\n    def run(self):\n        call(['pip install -r requirements.txt --no-clean'], shell=True)\n        install.run(self)\n\n\nsetuptools.setup(\n    name='cnlp',\n    version='0.1.0',\n    author='Junyi Li',\n    author_email='ljyduke@gmail.com',\n    description='Python toolkit for Chinese Language Processing.',\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    url='https://github.com/DukeEnglish/cnlp',\n    include_package_data=True,\n    packages=setuptools.find_packages(exclude=('test*', )),\n    classifiers=[\n        'Programming Language :: Python :: 3',\n        'License :: OSI Approved :: MIT License',\n        'Operating System :: OS Independent'],\n    cmdclass={'install': Installation})\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1407,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"133477390","text":"from telebot import types\nimport telebot\n\nbot = telebot.TeleBot('КОПИЯ ИСХОДНИКА')\n\n\n\n\n\n\ndef webAppKeyboard(): #создание клавиатуры с webapp кнопкой\n   keyboard = types.ReplyKeyboardMarkup(resize_keyboard=True) #создаем клавиатуру\n\n   webAppBim = types.WebAppInfo(\"https://games.mihailgok.ru\")\n   webAppEdm = types.WebAppInfo(\"https://games.mihailgok.ru\")\n   webAppCloud = types.WebAppInfo(\"https://www.emiia.ru/osm/indoor9.html#18.31/55.977272/37.17427/62.4/45\")\n   webAppData = types.WebAppInfo(\"https://games.mihailgok.ru\")\n   one_butt = types.KeyboardButton(text=\"ТИМ (BIM)\", web_app=webAppBim)\n   two_butt = types.KeyboardButton(text=\"ЭДО (EDM)\", web_app=webAppEdm)\n   three_butt = types.KeyboardButton(text=\"CLOUD EMIIA.AI\", web_app=webAppCloud)\n   four_butt = types.KeyboardButton(text=\"DATA ANALYSIS\", web_app=webAppData)\n   keyboard.add(one_butt, two_butt)\n   keyboard.add(three_butt, four_butt)\n\n   return keyboard #возвращаем клавиатуру\n\n\n\n\n\n\n\n\n\n@bot.message_handler(commands=['start']) #обрабатываем команду старт\ndef start_fun(message):\n   bot.send_message( message.chat.id, 'Привет, я бот для проверки телеграмм webapps!)\\nЗапустить тестовые страницы можно нажав на кнопки.', parse_mode=\"Markdown\", reply_markup=webAppKeyboard()) #отправляем сообщение с нужной клавиатурой\n\n\n@bot.message_handler(content_types=\"text\")\ndef new_mes(message):\n   start_fun(message)\n\n\n@bot.message_handler(content_types=\"web_app_data\") #получаем отправленные данные\ndef answer(webAppMes):\n   print(webAppMes) #вся информация о сообщении\n   print(webAppMes.web_app_data.data) #конкретно то что мы передали в бота\n   bot.send_message(webAppMes.chat.id, f\"получили инофрмацию из веб-приложения: {webAppMes.web_app_data.data}\")\n   #отправляем сообщение в ответ на отправку данных из веб-приложения\n\nif __name__ == '__main__':\n   bot.infinity_polling()\n","sub_path":"tel/py/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"521697020","text":"######################################################\n# Helper function to extract text from movie posters #\n######################################################\n\nimport sys\nimport cv2\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image, ImageEnhance, ImageFilter\nfrom pytesseract import image_to_string\n\n\n\nwd = \"C:/Users/Qing/Documents/GitHub/Fall2016-proj5-grp9\"\n\n\n\ninputpath = wd + '/data/test_images'\nfrom os import listdir\nfrom os.path import isfile, join\nname_list = [f for f in listdir(inputpath) if isfile(join(inputpath, f)) and f.endswith('.jpg')]\n\nfor i in range(1,len(name_list)):\n    \n    test_id = name_list[i][0:len(name_list[i])-4]\n\n    # To count number of charaters and output one string\n    def ImageSting_count(img_path):\n        # Open image\n        im = Image.open(img_path)\n        ip = img_path\n        # Image enhancers\n        enhancer = ImageEnhance.Contrast(im)\n        im = enhancer.enhance(1)\n        # Extract string\n        text = pytesseract.image_to_string(im)\n        count = len(text)\n        return count, text, ip\n\n\n    def preprocess(gray):\n        sobel = cv2.Sobel(gray, cv2.CV_8U, 1, 0, ksize = 3)\n        # make it binary\n        ret, binary = cv2.threshold(sobel, 0, 255, cv2.THRESH_OTSU+cv2.THRESH_BINARY)\n\n        # Construct element\n        element1 = cv2.getStructuringElement(cv2.MORPH_RECT, (30, 9))\n        element2 = cv2.getStructuringElement(cv2.MORPH_RECT, (24, 6))\n\n        # remove details\n        dilation = cv2.dilate(binary, element2, iterations = 1)\n        erosion = cv2.erode(dilation, element1, iterations = 1)\n        dilation2 = cv2.dilate(erosion, element2, iterations = 3)\n\n        # save intermediate pictures \n        #cv2.imwrite(wd+\"/output/text_detection/\"+test_id+\"_binary.png\", binary)\n        #cv2.imwrite(wd+\"/output/text_detection/\"+test_id+\"_dilation.png\", dilation)\n        #cv2.imwrite(wd+\"/output/text_detection/\"+test_id+\"_erosion.png\", erosion) \n        #cv2.imwrite(wd+\"/output/text_detection/\"+test_id+\"_dilation2.png\", dilation2)\n\n        return dilation2\n\n    def findTextRegion(img):\n        region = []\n\n        # Find boxes\n        img2, contours, hierarchy = cv2.findContours(img, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n        for i in range(len(contours)):\n            cnt = contours[i]\n            # Area\n            area = cv2.contourArea(cnt) \n\n            if(area < 1000):\n                continue\n\n            epsilon = 0.001 * cv2.arcLength(cnt, True)\n            approx = cv2.approxPolyDP(cnt, epsilon, True)\n            rect = cv2.minAreaRect(cnt)\n            print(\"rect is: \")\n            print(rect)\n\n            # four corners\n            box = cv2.boxPoints(rect)\n            box = np.int0(box)\n\n            # calculate height and width\n            height = abs(box[0][1] - box[2][1])\n            width = abs(box[0][0] - box[2][0])\n\n            if(height > width * 1.2):\n                continue\n\n            region.append(box)\n\n        return region\n\n\n    def get_num_pixels(img):\n        width, height = Image.open(img).size\n        return width*height\n\n    def percentage_text(img):\n        region = []\n        barea = []\n        # Total amount of pixels\n        total_px = get_num_pixels(img)\n        # Gray\n        gray = cv2.imread(img,0)\n\n        # Shape\n        dilation = preprocess(gray)\n        # Find boxes\n        img2, contours, hierarchy = cv2.findContours(dilation, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n        for i in range(len(contours)):\n            cnt = contours[i]\n            # Area\n            area = cv2.contourArea(cnt) \n\n            if(area < 1000):\n                continue\n\n            epsilon = 0.001 * cv2.arcLength(cnt, True)\n            approx = cv2.approxPolyDP(cnt, epsilon, True)\n            rect = cv2.minAreaRect(cnt)\n            #print \"rect is: \"\n            #print rect\n\n            # four corners\n            box = cv2.boxPoints(rect)\n            box = np.int0(box)\n\n            # calculate height and width\n            height = abs(box[0][1] - box[2][1])\n            width = abs(box[0][0] - box[2][0])\n\n            if(height > width * 1.2):\n                continue\n            barea.append(height*width)\n\n        total_area = sum(barea)\n\n        a = pd.DataFrame([float(total_area)/float(total_px)])\n\n        a.to_csv('C:/Users/Qing/Documents/GitHub/Fall2016-proj5-grp9/output/test_detection'+\"\\\\\"+test_id+\"_text.csv\")\n\n    def detect(img):\n        # Gray\n        gray = cv2.imread(img,0)\n\n        # Shape\n        dilation = preprocess(gray)\n\n        # find text regions\n        region = findTextRegion(dilation)\n\n\n        d = cv2.imread(img)\n\n        # Draw lines to form boxes\n        for box in region:\n            cv2.drawContours(d, [box], 0, (0, 255, 0), 2)\n\n        #cv2.namedWindow(\"img\", cv2.WINDOW_NORMAL)\n        #cv2.imshow(\"img\", d)\n\n        cv2.imwrite('C:/Users/Qing/Documents/GitHub/Fall2016-proj5-grp9/output/test_detection'+\"\\\\\"+test_id+\"_contours.png\", d)\n\n        #cv2.waitKey(0)\n        #cv2.destroyAllWindows()\n\n    detect(img = wd+\"/data/test_images/\"+test_id+\".jpg\")\n    percentage_text(img = wd+\"/data/test_images/\"+test_id+\".jpg\")\n\n","sub_path":"lib/text_recognition.py","file_name":"text_recognition.py","file_ext":"py","file_size_in_byte":5127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"403604208","text":"import xlrd\r\nimport io\r\nimport json\r\n\r\nfrom utils import timeit\r\n\r\n\r\ndef extract_data_from_excel(file_name: str, sheet_name: str) -> xlrd.sheet.Sheet:\r\n    \"\"\"\r\n    Function to extract data from excel sheet. Excel sheet is read\r\n    using pandas. Function returns a pandas dataframe object.\r\n\r\n    Parameters\r\n    ----------\r\n    filename: string\r\n        Location of the excel file to be read.\r\n    sheet_name: str\r\n        Name of the sheet on the excel to be read from.\r\n\r\n    Returns\r\n    -------\r\n    dataframe: pd.DataFrame\r\n        Returns a dataframe.\r\n\r\n    \"\"\"\r\n    work_book = xlrd.open_workbook(file_name)\r\n    sheet = work_book.sheet_by_name(sheet_name)\r\n    return sheet\r\n\r\n\r\ndef transform_data(xlrd_sheet: xlrd.sheet.Sheet) -> io.StringIO:\r\n    \"\"\"\r\n    Function to transform the data into json and stores the json\r\n    in a string buffer. The data is written as a list of rows from the\r\n    excel sheet with the first row as headers. Returns the string buffer.\r\n\r\n    Parameters\r\n    ----------\r\n    data_frame: pd.DataFrame\r\n        Dataframe object of the excel file. Outputted from the \r\n        extract_data_from_excel function.\r\n\r\n    Returns\r\n    -------\r\n    string_buffer: StringIO\r\n        Dataframe is converted into json string, writtern into a string\r\n        buffer and returned.\r\n\r\n    \"\"\"\r\n    headers, col_idx = [], 0\r\n    while True:\r\n        try:\r\n            headers.append(xlrd_sheet.cell_value(0, col_idx))\r\n            col_idx += 1\r\n        except IndexError:\r\n            break\r\n\r\n    data, row_idx = [], 0\r\n    while True:\r\n        try:\r\n            data.append({\r\n                col: xlrd_sheet.cell_value(col_idx, row_idx) for col_idx, col in enumerate(headers)\r\n            })            \r\n            row_idx += 1\r\n        except IndexError:\r\n            break\r\n\r\n    return io.StringIO(json.dumps(data))\r\n\r\n\r\ndef load_data(data: io.StringIO, target_file: str) -> None:\r\n    \"\"\"\r\n    Function to write the input string buffer into target file.\r\n\r\n    Paramters\r\n    ---------\r\n    data: StringIO\r\n        String buffer of the json output. From transform_data function\r\n    target_file: str\r\n        Filename of the target file for the string buffer to be\r\n        written into\r\n\r\n    Returns\r\n    -------\r\n    None\r\n\r\n    \"\"\"\r\n    with open(target_file, 'w', encoding='utf-8') as file:\r\n        file.write(data.getvalue())\r\n\r\n\r\ndef lambda_handler(event, context):\r\n    try:\r\n        s3 = boto3.resource('s3')\r\n        bucket = s3.Bucket(event['source_bucket'])\r\n        bytes_buffer = io.BytesIO()\r\n    \r\n        filename = os.path.basename(event['source_key']).split('.')[0]\r\n        target_folder = '{}/{}-json.json'.format(event['target_bucket_folder'], filename)\r\n    \r\n        bucket.download_fileobj(Key=event['source_key'],\r\n                                Fileobj=bytes_buffer)\r\n    \r\n        data_frame = extract_data_from_excel(bytes_buffer,\r\n                                            sheet_name=event[\"sheet_name\"])\r\n        json_string_buffer = transform_data(data_frame)\r\n    \r\n        target_bucket = s3.Bucket(event['target_bucket'])\r\n        target_bucket.put_object(Body=json_string_buffer.getvalue(),\r\n                                 Key=target_folder)\r\n    except KeyError:\r\n        logger.info(event)\r\n        return {\r\n            'statusCode': 502,\r\n            'body': json.dumps(event)\r\n        }\r\n    return {\r\n        'statusCode': 200,\r\n        'body': json.dumps('File successfully written into {}'.format(target_folder))\r\n    }\r\n\r\n\r\n@timeit(300)\r\ndef time_test():\r\n    data_frame = extract_data_from_excel('C:\\\\Users\\\\Vishnu\\\\Downloads\\\\ISO10383_MIC.xls', 'MICs List by CC')\r\n    string_buffer = transform_data(data_frame)\r\n\r\n\r\nif __name__ == '__main__':\r\n    time_test()","sub_path":"xlrd_extract.py","file_name":"xlrd_extract.py","file_ext":"py","file_size_in_byte":3748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"110052298","text":"#-*-coding:utf-8-*-\nfrom functools import partial\nfrom threading import Timer  \nimport time\nimport socket\nimport math\nimport cv2\nimport sys\nimport os\nimport glob\nfrom PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nmtx=[[208.86166026,0,308.81133425]\n,[0,212.8767556,228.70653685]\n,[0,0 ,1]]\ndist=[[ 0.08926135 ,-0.08971012 , 0.00293254, -0.00170997,  0.01515266]]\nroi=(8, 35, 622, 415)\nnewcameramtx=[[179.62944031,0,305.93974179]\n,[0,183.76034546,233.45313844]\n,[0,0,1,]]\nmtx=np.array(mtx)\ndist=np.array(dist)\nnewcameramtx=np.array(newcameramtx)\n\ndef plotData():\n    plt.subplot(611)\n    plt.scatter(array_time,array_x1,c='b',marker='.')\n    plt.pause(0.0001)\n    plt.subplot(612)\n    plt.scatter(array_time,array_roll,c='y',marker='.')\n    plt.pause(0.0001)\n    plt.subplot(613)\n    plt.scatter(array_time,array_y1,c='b',marker='.')\n    plt.pause(0.0001)\n    plt.subplot(614)\n    plt.scatter(array_time,array_pitch,c='y',marker='.')\n    plt.pause(0.0001)\n    plt.subplot(615)\n    plt.scatter(array_time,array_h_cm,c='b',marker='.')\n    plt.pause(0.0001)\n    plt.subplot(616)\n    plt.scatter(array_time,array_thr,c='y',marker='.')\n    # plt.show()\n    plt.pause(0.0001)\ndef rec(x1,roll,y1,pitch,h_cm,thr,P,I,D,mytime):\n    global array_x1,array_roll,array_y1,array_pitch,array_h_cm,array_thr,array_P,array_D,array_I,array_time\n    array_x1.append(x1)\n    array_y1.append(y1)\n    array_roll.append(roll)\n    array_pitch.append(pitch)\n    array_h_cm.append(h_cm)\n    array_thr.append(thr)\n    array_P.append(P)\n    array_I.append(I)\n    array_D.append(D)    \n    array_time.append(mytime)\n\ndef find_circle(img_raw):\n    start=time.clock()\n    findFlag=False\n    dia=x=y=0\n    img_raw = cv2.undistort(img_raw, mtx, dist, None, newcameramtx)\n    grayimg=cv2.cvtColor(img_raw,cv2.COLOR_BGR2GRAY)\n    gauss=cv2.GaussianBlur(grayimg,(5,5),0)\n    ret,th3=cv2.threshold(gauss,0,255,cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)\n    _,contours,hierarchy=cv2.findContours(th3,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)\n    for i in range(len(hierarchy[0])):\n        insidenumber=hierarchy[0][i][3]\n        if insidenumber>-1:\n            if len(contours[i])<8 or len(contours[insidenumber])<8:\n                continue\n            ellipseSmall = cv2.fitEllipse(contours[i])\n            ellipseBig = cv2.fitEllipse(contours[insidenumber])\n            if ellipseBig[1][1]>1.5*ellipseBig[1][0] or ellipseSmall[1][1]>1.5*ellipseSmall[1][0]:\n                continue\n            if (ellipseBig[0][0]-ellipseSmall[0][0])>28 or (ellipseBig[0][1]-ellipseSmall[0][1])>28 or ellipseBig[1][1]>400:\n                continue\n            # print(\"long short\",ellipseBig[1][1]/ellipseBig[1][0])\n            prop=ellipseBig[1][1]/(ellipseSmall[1][1])\n            if prop>1.3 and prop<2.2:\n                # print(\"prop\",prop,len(contours[i]))\n                cv2.ellipse(img_raw,ellipseBig,(255),4)\n                cv2.ellipse(img_raw,ellipseSmall,(255),4)\n                x=int((ellipseBig[0][0]+ellipseSmall[0][0])/2)\n                y=int((ellipseBig[0][1]+ellipseSmall[0][1])/2)\n                findFlag=True\n                cv2.circle(img_raw,(x,y),3,(255),1)\n                # print(ellipseBig[0][0]-ellipseSmall[0][0],ellipseBig[0][1]-ellipseSmall[0][1])\n                # print(ellipseBig[1][1])\n                dia=(ellipseBig[1][1]+ellipseBig[1][0])/2\n    # cv2.imshow(\"circle\",img_raw)\n    end=time.clock()\n    # print(\"time=%s\"%(end-start))\n    return findFlag,dia,x,y,img_raw\ndef sendCtrl():  \n    global sendCtrlTimer,exitflag,rollTemp,pitchTemp,x1,y1,Pe,De,Ie,timeInt,h_cm\n    global roll,pitch,thr,yaw,mode,check,calcCMD,flagc,counterXY,counterStab\n    sendCtrlTimer = Timer(0.05, sendCtrl)  \n    if exitflag==0:\n        sendCtrlTimer.start()\n    check=yaw^pitch^roll^thr^mode\n    rec(x1,roll,y1,pitch,h_cm,thr,Pe,Ie,De,timeInt)   \n    calcCMD=ctrlHead+bytes([roll,pitch,thr,yaw,mode,check])+b'\\x99'\n    print(\"thr=%s,roll=%s,pitch=%s\"%(thr,roll,pitch))\n    s.sendto(calcCMD, ('192.168.0.1', 40000))\n    # print ('*sendCtrl%s,roll=%s,pitch=%s,thr=%s,yaw=%s,mode=%s' % (calcCMD,roll,pitch,thr,yaw,mode))\ndef heartbeat(hCMD):  \n    global heartbeatTimer,exitflag\n    heartbeatTimer = Timer(1.01, heartbeat)  \n    if exitflag==0:\n        heartbeatTimer.start()\n    s.sendto(heartbeatCMD, ('192.168.0.1', 40000))  \n    # print ('heartbeat%s' % hCMD) \ndef limiting(lim):\n    limm=int(lim)\n    if limm>=255:\n        limm=255\n    elif limm<=0:\n        limm=0\n    return limm\n\ndef CatchUsbVideo(window_name, camera_idx):\n    global roll,pitch,thr,yaw,mode,check,calcCMD,h_cmOld,flagc,counterXY,exitflag,counterStab,timeInt,x1,y1,Pe,De,Ie,timeInt,h_cm\n    global array_x1,array_roll,array_y1,array_pitch,array_h_cm,array_thr,array_P,array_D,array_I,array_time\n    dE=[0,0,0]\n    dEx=[0,0,0]\n    dEy=[0,0,0]\n    badCount=0\n    badCountMax=3\n    counti=0\n    countiMax=3\n    errorI=170\n    errorIx=0\n    errorIy=0\n    dError=0\n    x1dError=0\n    y1dError=0\n    h_cmOld=timeIntOld=0\n    x1Old=0\n    y1Old=0\n    flagInit=1#初始化标志，用于计算D\n    theta=-math.pi*(90-16)/180\n    theta=math. pi/2\n    P_pitchMax=0.12#0.2太小没反应，0.7太大一下子飞出去,0.45容易越界，0.32稳定个3、4秒，0.26慢慢...\n    P_rollMax=0.12#发散,0.2可以调节回来,0.1可以\n    D_roll=0.2\n    D_pitch=0.2\n    Ix=0.05\n    Iy=0.05\n    xyMax=300#水平方向最大值\n    hMax=300\n    coun=0#定期保存图片\n    h_ref=90\n    cv2.namedWindow(window_name)\n    \n    #视频来源，可以来自一段已存好的视频，也可以直接来自USB摄像头\n    cap = cv2.VideoCapture(camera_idx)        \n        \n    while cap.isOpened():\n        coun+=1\n        # if not coun%130:\n        #     ttt=time.clock()\n        #     plotData()\n        #     print(\"*************************\",time.clock()-ttt)\n        if not coun%50:\n            cv2.imwrite(str(time.clock()) + 'clock.jpg',img_raw) \n        start =time.clock()\n        ok, img_raw = cap.read() #读取一帧数据\n        timeInt=time.clock()\n        f,dia,x,y,img_2=find_circle(img_raw)#------------------图像处理\n        if f:\n            mode=1\n            h_cm=1976*2/dia\n            if h_cm<12:\n                print(\"**********************************************\",h_cm)\n                cv2.imwrite(str(time.clock()) + '.jpg',img_raw) \n            x1=19*2*((x-320)*math.cos(theta)-(-y+240)*math.sin(theta))/dia\n            y1=19*2*((x-320)*math.sin(theta)+(-y+240)*math.cos(theta))/dia\n            # print(\"x1=%s y1=%s\"%(x1,y1))\n            if abs(x1)>xyMax or abs(y1)>xyMax or h_cm >hMax or h_cm<5:#大小异常的xyh值\n                print(\"x1 y1 h\",x1,y1,h_cm)\n                cv2.imwrite(str(time.clock()) + 'WW.jpg',img_raw) \n                x1=0\n                y1=0\n                h_cm=h_ref\n                continue\n            # P=1.1#0.5\n            # I=0.15#0.2\n            # D=0.8#0.6\n            P=0.5\n            I=0.2\n            D=0.6\n            if flagInit:\n                timeIntOld=timeInt\n                h_cmOld=h_cm\n                x1Old=x1\n                y1Old=y1\n                flagInit=0\n                dE=[0,0,0]\n                dEx=[0,0,0]\n                dEy=[0,0,0]\n                # print(\"I&D init\")\n            else:\n                if abs(h_cmOld-h_cm)>10 or abs(x1-x1Old)>20 or abs(y1-y1Old)>20:#异常点检测到处理\n                    continue\n                dE[counti]=(h_cmOld-h_cm)/(timeInt-timeIntOld)\n                if abs(D*dE[counti])>50:\n                    dE[counti]=0\n                # dError=(h_cmOld-h_cm)/(timeInt-timeIntOld)\n                if dError>120:\n                    dError=120\n                elif dError<-120:\n                    dError=-120\n                dEx[counti]=(x1-x1Old)/(timeInt-timeIntOld)\n                if abs(D_roll*dEx[counti])>50:\n                    dEx[counti]=0\n                if x1dError>120:\n                    x1dError=120\n                elif x1dError<-120:\n                    x1dError=-120\n                dEy[counti]=(y1-y1Old)/(timeInt-timeIntOld)\n                if abs(D_pitch*dEy[counti])>50:\n                    dEy[counti]=0\n                if y1dError>120:\n                    y1dError=120\n                elif y1dError<-120:\n                    y1dError=-120\n                counti+=1\n                if counti==countiMax:\n                    counti=0\n                dError=sum(dE)/countiMax\n                x1dError=sum(dEx)/countiMax\n                y1dError=sum(dEy)/countiMax\n                errorI+=I*(h_ref-h_cm)*(timeInt-timeIntOld)\n                errorIx+=Ix*(x1)*(timeInt-timeIntOld)\n                errorIy+=Iy*(y1)*(timeInt-timeIntOld)\n                # print(\"errorI=%s,error=%s,I_gain=%s,time_gain=%s\"%(errorI,(h_ref-h_cm),I*(h_ref-h_cm)*(timeInt-timeIntOld),(timeInt-timeIntOld)))\n                if errorI>=255:\n                    errorI=255\n                elif errorI<=0:\n                    errorI=0\n                if errorIx>=120:\n                    errorIx=120\n                elif errorIx<=-120:\n                    errorIx=-120\n                if errorIy>=120:\n                    errorIy=120\n                elif errorIy<=-120:\n                    errorIy=-120\n                # print(\"fenzi=%s,fenmu=%s\"%((h_cmOld-h_cm),(timeInt-timeIntOld)))\n                timeIntOld=timeInt\n                h_cmOld=h_cm\n                y1Old=y1\n                x1Old=x1\n                # print(\"h_cmOld=%s,h_cm=%s,v=%s\"%(h_cmOld,h_cm,dError))\n            thr=int(round(P*(h_ref-h_cm)+errorI+D*dError))\n            Pe=P*(h_ref-h_cm)\n            Ie=errorI\n            De=D*dError\n            # print(\"Perror=%s,thr1=%s,dError=%s\"%(P*(h_ref-h_cm),thr,D*dError))\n            # print(\"Perror=%s,errorI=%s,dError=%s\"%(P*(h_ref-h_cm),errorI,D*dError))\n            if thr>=255:\n                thr=255\n            if thr<=0:\n                thr=0\n            roll=limiting(P_rollMax*x1+errorIx+D_roll*x1dError+128)\n            pitch=limiting(P_pitchMax*y1+errorIy+D_pitch*y1dError+128)\n\n            # print(\"thr2=%s\"%thr)\n            # rec(x1,roll,y1,pitch,h_cm,thr,P*(h_ref-h_cm),errorI,D*dError,timeInt)   \n            # check=yaw^pitch^roll^thr^mode\n            # calcCMD=ctrlHead+bytes([roll,pitch,thr,yaw,mode,check])+b'\\x99'\n        else:\n            badCount+=1\n            x1=0\n            y1=0\n            h_cm=h_ref+10\n            if badCount>=badCountMax:\n                badCount=0\n                roll=128\n                pitch=128\n                h_cmOld=timeIntOld=0\n                x1Old=0\n                y1Old=0\n                flagInit=1\n            print ('NULL')\n        #显示图像并等待10毫秒按键输入，输入‘q’退出程序\n        cv2.imshow(window_name,img_2)\n        c = cv2.waitKey(1)\n        if c & 0xFF == ord('q'):\n            calcCMD=ctrlHead2\n            plt.subplot(611)\n            plt.scatter(array_time,array_x1,c='b',marker='.')\n            plt.plot([0,timeInt],[0,0])\n            # plt.pause(0.0001)\n            plt.subplot(612)\n            plt.scatter(array_time,array_roll,c='y',marker='.')\n            # plt.pause(0.0001)\n            plt.subplot(613)\n            plt.scatter(array_time,array_y1,c='b',marker='.')\n            plt.plot([0,timeInt],[0,0])\n            # plt.pause(0.0001)\n            plt.subplot(614)\n            plt.scatter(array_time,array_pitch,c='y',marker='.')\n            # plt.pause(0.0001)\n            plt.subplot(615)\n            plt.scatter(array_time,array_h_cm,c='b',marker='.')\n            plt.plot([0,timeInt],[h_ref,h_ref])\n            # plt.pause(0.0001)\n            plt.subplot(616)\n            plt.scatter(array_time,array_thr,c='y',marker='.')\n            # plt.scatter(array_time,array_P,c='r',marker='.')\n            # plt.scatter(array_time,array_I,c='g',marker='.')\n            # plt.scatter(array_time,array_D,c='b',marker='.')\n            plt.show()\n            # plt.pause(0.0001)\n            # exitflag=1\n            break        \n        end =time.clock()\n        # print('Running time: %s Seconds'%(end-start))\n        # print('------------------------------------Now time: %s Seconds'%start)  \n    \n    #释放摄像头并销毁所有窗口\n    cap.release()\n    cv2.destroyAllWindows() \n\nctrlHead2=b'\\x63\\x63\\x0a\\x00\\x00\\x08\\x00\\x66\\x80\\x80\\x00\\x80\\x00\\x80\\x99'#待机\nctrlHold=b'\\x63\\x63\\x0a\\x00\\x00\\x08\\x00\\x66\\x80\\x80\\x80\\x80\\x01\\x01\\x99'#半油门\nctrlHeadDan=b'\\x63\\x63\\x0a\\x00\\x00\\x08\\x00\\x66\\x80\\x80\\x70\\x80\\x01\\xf1\\x99'#小半油门\nheartbeatCMD=b'\\x63\\x63\\x01\\x00\\x00\\x00\\x00'\nctrlHead=b'\\x63\\x63\\x0a\\x00\\x00\\x08\\x00\\x66'#控制指令头\narray_x1=[]\narray_roll=[]\narray_y1=[]\narray_pitch=[]\narray_h_cm=[]\narray_thr=[]\narray_P=[]\narray_D=[]\narray_I=[]\narray_time=[]\nyaw=pitch=roll=128\nmode=0\nthr=140\ntimeInt=0\nexitflag=0\nflagc=0#没用了的\ncounterXY=0#没用了的\nrollTemp=128\npitchTemp=128\ncounterStab=0\nx1=0\ny1=0\nPe=0\nDe=0\nIe=0\nh_cm=0\nimages = glob.glob('*.jpg')#删掉上次留下的jpg文件\nfor f in images:\n    if os.path.exists(f):\n        os.remove(f)\ncalcCMD=ctrlHead2#待机\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n# s.bind(('192.168.0.2',12345))\n# sendCtrl = partial(sendCtrl, calcCMD)\nsendCtrlTimer = Timer(0.05, sendCtrl)  \nsendCtrlTimer.start()\nheartbeat = partial(heartbeat, heartbeatCMD)\nheartbeatTimer = Timer(1.01, heartbeat) \nheartbeatTimer.start()\ntime.sleep(3)\n# mode=1\n# thr=128\n# calcCMD=ctrlHold#半油门\n# time.sleep(2)\n# mode=3\nCatchUsbVideo(\"camera\", 0)\n# while  True:\ntime.sleep(3)\nexitflag=1\n#     mode=0\n#     check=yaw^pitch^roll^thr^mode\n#     calcCMD=ctrlHead+bytes([roll,pitch,thr,yaw,mode,check])+b'\\x99'\n","sub_path":"combine.py","file_name":"combine.py","file_ext":"py","file_size_in_byte":13557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"565338585","text":"import scrapy\n\n\nclass JokesSpider(scrapy.Spider):\n    name='jokes'\n\n    start_urls=[\n        'http://www.laughfactory.com/jokes/family-jokes'\n    ]\n\n    def parse(self, response):\n        for joke in response.xpath(\"//div[@class='jokes']\"):\n            yield{'joke_text': joke.xpath(\"//div[@class='joke-text']/p\").extract_first()\n            }\n        \n        next_page=response.xpath(\"//li[@class='next']/a/@href\").extract_first()\n        if next_page is not None:\n            next_page_link=response.urljoin(next_page)\n            yield scrapy.Request(url=next_page_link, callback=self.parse)","sub_path":"jokes.py","file_name":"jokes.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"443273110","text":"#!/usr/bin/env python\n\n\"\"\"Utility classes.\"\"\"\n\nimport gzip\nimport hashlib\nimport io\nimport os\nimport shutil\nimport tarfile\nimport tempfile\nimport time\n\nfrom typing import Dict\n\nCHUNK_SIZE = int(os.environ.get(\"DSV_CHUNK_SIZE\", 1048576))\n\n\nclass FormattedSHA256(str):\n    \"\"\"A algorithm prefixed SHA256 hash value.\"\"\"\n\n    def __new__(cls, sha256: str):\n        if not sha256 or len(sha256) != 64:\n            raise ValueError(sha256)\n        obj = super().__new__(cls, \"sha256:{0}\".format(sha256))\n        obj.sha256 = sha256\n        return obj\n\n    @staticmethod\n    def parse(digest: str):\n        \"\"\"\n        Initializes a FormattedSHA256 from a given SHA256 digest value.\n\n        Args:\n            digest: A SHA256 digest value in form SHA256:<digest value>.\n\n        Returns:\n            The newly initialized object.\n        \"\"\"\n        if not digest or not digest.startswith(\"sha256:\") or len(digest) != 71:\n            raise ValueError(digest)\n        return FormattedSHA256(digest[7:])\n\n\ndef _chunk_file(file_in, file_out) -> Dict:\n    \"\"\"\n    Copies from one file to another in chunks.\n\n    Args:\n        file_in: The input file.\n        file_out: The output file.\n\n    Returns:\n        dict:\n            digest: The SHA256 digest value of the output file.\n            size: Size of the output file in bytes.\n    \"\"\"\n    size = 0\n    hasher = hashlib.sha256()\n    while True:\n        chunk = file_in.read(CHUNK_SIZE)\n        if not chunk:\n            break\n        file_out.write(chunk)\n        size += len(chunk)\n        hasher.update(chunk)\n    file_out.flush()\n\n    # Not all file object are native ...\n    try:\n        os.fsync(file_out.fileno())\n    # pylint: disable=bare-except\n    except:\n        ...\n\n    # Be kind, rewind ...\n    file_out.seek(0)\n\n    return {\"digest\": \"sha256:{0}\".format(hasher.hexdigest()), \"size\": size}\n\n\ndef must_be_equal(\n    expected, actual, msg: str = \"Actual value does not match expected value\"\n):\n    \"\"\"\n    Compares two values and raises an exception if they are not equal.\n\n    Args:\n        expected: The expected value.\n        actual: The actual value.\n        msg: Message describing the context of the comparison.\n    \"\"\"\n    if actual != expected:\n        raise RuntimeError(\"{0}: {1} != {2}\".format(msg, actual, expected))\n\n\ndef xellipsis(string: str) -> str:\n    \"\"\"\n    Reduces the length of a given string, if it is wider than the terminal width, inserting str_ellipsis.\n\n    Args:\n        string: The string to be reduced.\n\n    Returns:\n        A string with a maximum length of the terminal width.\n    \"\"\"\n    return str_ellipsis(string, os.environ.get(\"COLUMNS\", 80) / 2)\n\n\ndef str_ellipsis(string: str, max_length: int = 40) -> str:\n    \"\"\"\n    Reduces the length of a given string, if it is over a certain length, by inserting str_ellipsis.\n\n    Args:\n        string: The string to be reduced.\n        max_length: The maximum length of the string.\n\n    Returns:\n        A string with a maximum length of :param:max_length.\n    \"\"\"\n    if len(string) <= max_length:\n        return string\n    n_2 = int(max_length) / 2 - 3\n    n_1 = max_length - n_2 - 3\n\n    return \"{0}...{1}\".format(string[: int(n_1)], string[-int(n_2) :])\n\n\ndef formatted_digest(data: bytes) -> FormattedSHA256:\n    \"\"\"\n    Returns the algorithm prefixed digest value of given data.\n\n    Args:\n        data: The data for which to calculate the digest value.\n\n    Returns:\n        The digest value of the data in the format <hast type>:<digest value>.\n    \"\"\"\n    return FormattedSHA256(hashlib.sha256(data).hexdigest())\n\n\ndef copy_file(file_in, file_out) -> Dict:\n    \"\"\"\n    Copies from one file to another.\n\n    Args:\n        file_in: The input file.\n        file_out: The output file.\n\n    Returns:\n        dict: as defined by :func:~docker_sign_verify.Utils._chunk_file.\n    \"\"\"\n    return _chunk_file(file_in, file_out)\n\n\ndef read_file(path) -> bytes:\n    \"\"\"\n    Retrieves the entire contents of a file.\n\n    Args:\n        path: Absolute path of the file to be read.\n\n    Returns:\n        The file content.\n    \"\"\"\n    bytesio = io.BytesIO()\n    with path.open(mode=\"rb\") as file:\n        copy_file(file, bytesio)\n    return bytesio.read()\n\n\ndef write_file(path, content: bytes) -> Dict:\n    \"\"\"\n    Assigns the entire contents of a file.\n\n    Args:\n        path: Absolute path of the file to be assigned.\n        content: The content to be assigned.\n\n    Returns:\n        dict: as defined by :func:~docker_sign_verify.Utils.copy_file.\n    \"\"\"\n    bytesio = io.BytesIO(content)\n    with path.open(mode=\"wb\") as file:\n        return copy_file(bytesio, file)\n\n\ndef tar_mkdir(file_out, name: str):\n    \"\"\"\n    Creates an empty directory in a given tar archive on disk.\n\n    Args:\n        file_out: The output file.\n        name: Name of the directory to be created.\n    \"\"\"\n    with tarfile.TarFile(\"tar-archive\", \"a\", file_out) as tfile_out:\n        tarinfo = tfile_out.tarinfo()\n        tarinfo.mode = 0o0755\n        tarinfo.mtime = time.time()\n        tarinfo.name = name\n        tarinfo.tarfile = tfile_out\n        tarinfo.type = tarfile.DIRTYPE\n        tarinfo.uid = tarinfo.gid = 0\n        tarinfo.uname = tarinfo.gname = \"\"\n        tfile_out.addfile(tarinfo)\n\n\n# TODO: What is the type of \"content\"?\ndef tar_add_file(file_out, name: str, content):\n    \"\"\"\n    Creates a file from memory in a given tar archive on disk.\n\n    Args:\n        file_out: The output file.\n        name: The name of the file to be created\n        content: The corresponding file content.\n    \"\"\"\n    bytesio = io.BytesIO(content)\n    bytesio.seek(0)\n    with tarfile.TarFile(\"tar-archive\", \"a\", file_out) as tfile_out:\n        tarinfo = tfile_out.tarinfo()\n        tarinfo.mode = 0o0644\n        tarinfo.mtime = time.time()\n        tarinfo.name = name\n        tarinfo.tarfile = tfile_out\n        tarinfo.size = len(content)\n        tarinfo.uid = tarinfo.gid = 0\n        tarinfo.uname = tarinfo.gname = \"\"\n        tfile_out.addfile(tarinfo, bytesio)\n\n\ndef tar_delete_file(file_out, name: str):\n    \"\"\"\n    Removes a file from a given tar archive on disk.\n\n    Args:\n        file_out: The output file.\n        name: The name of the file to be removed.\n    \"\"\"\n    with tempfile.TemporaryFile() as tmp:\n        with tarfile.open(fileobj=file_out) as tfile_in:\n            with tarfile.open(fileobj=tmp, mode=\"w:\") as tfile_out:\n                for tarinfo in tfile_in:\n                    if tarinfo.name == name:\n                        continue\n\n                    if tarinfo.type == tarfile.DIRTYPE:\n                        tfile_out.addfile(tarinfo)\n                    else:\n                        handle = tfile_in.extractfile(tarinfo)\n                        tfile_out.addfile(tarinfo, handle)\n        file_out.seek(0)\n        tmp.seek(0)\n        shutil.copyfileobj(tmp, file_out)\n\n\ndef tar(file_out, name: str, file_in):\n    \"\"\"\n    Adds a file to a given tar archive on disk to disk.\n\n    Args:\n        file_out: The output file (tar).\n        name: The name of the file to be added.\n        file_in: The input file.\n    \"\"\"\n    with tarfile.TarFile(\"tar-archive\", \"a\", file_out) as tfile_out:\n        tarinfo = tfile_out.gettarinfo(None, name, file_in)\n        tarinfo.uid = tarinfo.gid = 0\n        tarinfo.uname = tarinfo.gname = \"\"\n        tarinfo.mode = 0o0644\n        tfile_out.addfile(tarinfo, file_in)\n\n\ndef untar(file_in, name: str, file_out):\n    \"\"\"\n    Extracts a file from a given tar archive.\n\n    Args:\n        file_in: The input file (tar).\n        name: The name of the file to be extracted.\n        file_out: The output file.\n    \"\"\"\n    result = None\n    with tarfile.open(fileobj=file_in) as tfile_in:\n        for tarinfo in tfile_in:\n            if tarinfo.name == name:\n                result = _chunk_file(tfile_in.extractfile(tarinfo), file_out)\n                break\n    return result\n\n\ndef file_exists_in_tar(file_in, name: str):\n    \"\"\"\n    Checks if a file exists in a given tar archive.\n\n    Args:\n        file_in: The input file (tar).\n        name: The name of the file for which to check existance.\n\n    Returns:\n        bool: True if the file exists, False otherwise.\n    \"\"\"\n    with tarfile.open(fileobj=file_in) as tfile_in:\n        for tarinfo in tfile_in:\n            if tarinfo.name == name:\n                return True\n    return False\n\n\ndef gunzip(file_in, file_out) -> Dict:\n    \"\"\"\n    Uncompresses a given gzip archive.\n\n    Args:\n        file_in: The input file (gz)\n        file_out: The output file.\n\n    Returns:\n        dict: as defined by :func:~docker_sign_verify.Utils._chunk_file.\n    \"\"\"\n    with gzip.GzipFile(fileobj=file_in, mode=\"rb\") as gfile_in:\n        return _chunk_file(gfile_in, file_out)\n","sub_path":"docker_sign_verify/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":8681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"600988983","text":"from inefficient import PrimePy\nfrom utility import timeResults, validate\nfrom eratosthenes import Eulers\n\npp = PrimePy(100_000)\npasses = 0\n\n# start_time = timeResults()\n# pp.reset()\n# pp.my_version()\n\n# time = timeResults(start_time)\n# print(len(pp.primeNumbers))\n# print(\n#     f\"Got result in {time}seconds and it was {validate(pp.maxNum, len(pp.primeNumbers))}.\"\n# )\n\n\ndef run_repeat():\n    max_nums = [100, 1000, 10_000, 100_000]\n    for number in max_nums:\n        pp.maxNum = number\n        print(f\"Run test for {number}\")\n\n        pp.timeResults()\n        pp.my_version()\n        time = pp.timeResults()\n\n        print(\n            f\"Got result in {time}seconds and found {len(pp.primeNumbers)} prime numbers and that is {validate(pp.maxNum, len(pp.primeNumbers))}.\"\n        )\n\n\n# pp.run_repeat()\n\nerato = Eulers(10_000_000)\nstart_time = timeResults()\nerato.run()\ntime = timeResults(start_time)\nprint(erato.countPrimes())\nprint(\n    f\"Got results in {time} seconds and it was {validate(erato.maxNum, erato.countPrimes())}\"\n)","sub_path":"Python/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"246748613","text":"\n\nimport re\nimport time\nimport os\nimport typing\n\nimport jk_typing\nimport jk_logging\nimport jk_prettyprintobj\n\nfrom thaniya_common.utils import IOContext\n\nfrom .EnumJobState import EnumJobState\nfrom .JobFile import JobFile\nfrom .Job import Job\n\n\n\n\n\n\n\n#\n# Represents a job loaded at runtime\n#\nclass JobQueue(jk_prettyprintobj.DumpMixin):\n\n\t################################################################################################################################\n\t## Constructor\n\t################################################################################################################################\n\n\t@jk_typing.checkFunctionSignature()\n\tdef __init__(self, ioContext:IOContext, dirPath:str, bResetJobStateOnLoad:bool, log:jk_logging.AbstractLogger):\n\t\tself.__ioContext = ioContext\n\n\t\tlog.notice(\"Job queue directory: \" + dirPath)\n\t\tself.__jobDirPath = dirPath\n\t\tassert os.path.isdir(dirPath)\n\n\t\tioContext.setDirMode(dirPath)\n\n\t\tself.__bResetJobStateOnLoad = bResetJobStateOnLoad\n\n\t\tself.__jobsByPriority = {}\n\t\tself.__jobsByID = {}\n\n\t\t# ----\n\n\t\tself.update(log)\n\t#\n\n\t################################################################################################################################\n\t## Public Properties\n\t################################################################################################################################\n\n\t#\n\t# Returns a list of all jobs.\n\t#\n\t@property\n\tdef jobs(self) -> typing.List[Job]:\n\t\treturn list(self.__jobsByID.values())\n\t#\n\n\t@property\n\tdef jobPriorities(self) -> typing.List[int]:\n\t\treturn sorted(self.__jobsByPriority.keys())\n\t#\n\n\t################################################################################################################################\n\t## Helper Methods\n\t################################################################################################################################\n\n\tdef ____addJob(self, job:Job):\n\t\tif job.jobID in self.__jobsByID:\n\t\t\treturn\n\n\t\tjobList = self.__jobsByPriority.get(job.jobPriority)\n\t\tif jobList is None:\n\t\t\tjobList = []\n\t\t\tself.__jobsByPriority[job.jobPriority] = jobList\n\t\tjobList.append(job)\n\n\t\tself.__jobsByID[job.jobID] = job\n\t#\n\n\tdef ____removeJob(self, job:Job):\n\t\tif job.jobID not in self.__jobsByID:\n\t\t\treturn\n\n\t\tjobList = self.__jobsByPriority.get(job.jobPriority)\n\t\tif jobList is None:\n\t\t\t# that's strange!\n\t\t\tpass\n\t\telse:\n\t\t\tjobList.remove(job)\n\n\t\tdel self.__jobsByID[job.jobID]\n\t#\n\n\tdef __createJobIDAndFilePath(self) -> str:\n\t\ttLast = -1\n\t\twhile True:\n\t\t\twhile True:\n\t\t\t\ttNow = int(time.time() * 1000000)\n\t\t\t\tif tNow != tLast:\n\t\t\t\t\tbreak\n\t\t\ttLast = tNow\n\t\t\tjobID = \"{:0>16x}\".format(tNow)\n\t\t\tjobFilePath = os.path.join(self.__jobDirPath, jobID + \"-job.json\")\n\t\t\tif not os.path.exists(jobFilePath):\n\t\t\t\treturn jobID, jobFilePath\n\t#\n\n\tdef _removeJobFromQueue(self, job:Job):\n\t\tself.____removeJob(job)\n\t#\n\n\tdef _dumpVarNames(self) -> list:\n\t\treturn [\n\t\t\t\"jobs\",\n\t\t\t\"jobPriorities\",\n\t\t]\n\t#\n\n\t################################################################################################################################\n\t## Public Methods\n\t################################################################################################################################\n\n\tdef getNextWaitingJob(self) -> typing.Union[Job,None]:\n\t\tfor jobPriority in sorted(self.__jobsByPriority.keys(), reverse=True):\t\t# highest to lowest priority\n\t\t\tjobList = self.__jobsByPriority[jobPriority]\n\t\t\tfor job in jobList:\n\t\t\t\tif job.state == EnumJobState.READY:\n\t\t\t\t\treturn job\n\t\treturn None\n\t#\n\n\t#\n\t# Create a new job\n\t#\n\t@jk_typing.checkFunctionSignature()\n\tdef scheduleJob(self, jobType:str, jobPriority:int, jobData:dict) -> Job:\n\t\tjobFile = JobFile.create(jobType, jobPriority, jobData)\n\t\tjobID, jobFilePath = self.__createJobIDAndFilePath()\n\t\tjobFile.writeToFile(jobFilePath)\n\n\t\tjob = Job(self, jobFile, jobID, jobFilePath)\n\n\t\tself.____addJob(job)\n\n\t\treturn job\n\t#\n\n\t\"\"\"\n\t#\n\t# Reload everything that exists, discard jobs that are gone, and load all missing job files from disk.\n\t#\n\tdef refresh(self, log:jk_logging.AbstractLogger):\n\t\tjobsToDelete = []\n\n\t\tfor job in self.__jobsByID.values():\n\t\t\tif not job.tryReload():\n\t\t\t\tjobsToDelete.append(job)\n\n\t\tfor job in jobsToDelete:\n\t\t\tdel self.__jobsByID[job.jobID]\n\t\t\tself.__jobsByPriority[job.jobPriority].remove(job)\n\n\t\tself.update(log)\n\t#\n\t\"\"\"\n\n\t#\n\t# Load all job files from disk that we do not yet know about\n\t#\n\tdef update(self, log:jk_logging.AbstractLogger):\n\t\tlog.notice(\"Scanning for jobs to load ...\")\n\n\t\t# select all job files we do not yet know about\n\t\tjobIDPathTuples = []\n\t\tfor fe in os.scandir(self.__jobDirPath):\n\t\t\tif fe.is_file():\n\t\t\t\tm = re.match(\"^(.+)-job.json$\", fe.name)\n\t\t\t\tif m:\n\t\t\t\t\tjobID = m.group(1)\n\t\t\t\t\tif jobID not in self.__jobsByID:\n\t\t\t\t\t\tjobIDPathTuples.append((jobID, fe.path))\n\n\t\t# order them in ascending order\n\t\tjobIDPathTuples = sorted(jobIDPathTuples, key=lambda x: x[0])\n\n\t\t# load all jobs\n\t\tfor jobID, jobFilePath in jobIDPathTuples:\n\t\t\tlog.notice(\"Loading job from file: \" + jobFilePath)\n\t\t\tjobFile = JobFile.loadFromFile(jobFilePath)\n\t\t\tjob = Job(self, jobFile, jobID, jobFilePath)\n\t\t\tif self.__bResetJobStateOnLoad:\n\t\t\t\tif job.state != EnumJobState.READY:\n\t\t\t\t\tlog.notice(\"Resetting job: \" + job.jobID)\n\t\t\t\t\tjob.state = EnumJobState.READY\n\t\t\t\t\tjob.storeProcessingState()\n\t\t\t\t\t# NOTE: We do NOT reset the processing state data. The processing state data might contain useful information.\n\t\t\tself.____addJob(job)\n\t#\n\n#\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"thaniya_server/src/thaniya_server/jobs/JobQueue.py","file_name":"JobQueue.py","file_ext":"py","file_size_in_byte":5449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"49471698","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    pip_services_runtime.cache.CacheEntry\n    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n    \n    Cache entry implementation\n    \n    :copyright: Digital Living Software Corp. 2015-2016, see AUTHORS for more details.\n    :license: MIT, see LICENSE for more details.\n\"\"\"\n\nimport time\n\nclass CacheEntry(object):\n    \"\"\"\n    Holds cached value for in-memory cache.\n    \"\"\"\n\n    created = None\n    key = None\n    value = None\n\n    def __init__(self, key, value):\n        \"\"\"\n        Creates instance of the cache entry.\n        \n        Args:\n            key: the unique key used to identify and locate the value.\n            value: the cached value.\n        \"\"\"\n        self.created = time.clock() * 1000\n        self.key = key\n        self.value = value\n\n    def set_value(self, value):\n        \"\"\"\n        Changes the cached value and updates creation time.\n        \n        Args:\n            value: the new cached value.\n\n        Returns: None\n        \"\"\"\n        self.value = value\n        self.created = time.clock() * 1000","sub_path":"pip_services_runtime/cache/CacheEntry.py","file_name":"CacheEntry.py","file_ext":"py","file_size_in_byte":1048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"346292127","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.7.9 (tags/v3.7.9:13c94747c7, Aug 17 2020, 18:58:18) [MSC v.1900 64 bit (AMD64)]\n# Embedded file name: T:\\InGame\\Gameplay\\Scripts\\Server\\plex\\plex_service.py\n# Compiled at: 2020-03-06 03:39:09\n# Size of source mod 2**32: 8464 bytes\nfrom plex.plex_enums import PlexBuildingType\nfrom sims4.geometry import Polygon\nfrom sims4.service_manager import Service\nimport build_buy, services, sims4.log\nlogger = sims4.log.Logger('PlexService', default_owner='tingyul')\n\nclass PlexService(Service):\n\n    def __init__(self):\n        self._zone_to_master_map = {}\n\n    def setup(self, gameplay_zone_data=None, save_slot_data=None):\n        persistence_service = services.get_persistence_service()\n        for zone_data in persistence_service.zone_proto_buffs_gen():\n            master_id = zone_data.master_zone_object_data_id\n            if master_id != 0:\n                zone_id = zone_data.zone_id\n                plex_id = zone_data.active_plex\n                self._zone_to_master_map[zone_id] = (master_id, plex_id)\n\n    def is_active_zone_a_plex(self):\n        return self.is_zone_a_plex(services.current_zone_id())\n\n    def is_zone_a_plex(self, zone_id):\n        return zone_id in self._zone_to_master_map\n\n    def is_zone_an_apartment(self, zone_id, consider_penthouse_an_apartment=True):\n        return self.is_zone_a_plex(zone_id) and (consider_penthouse_an_apartment or self.get_plex_building_type(zone_id) != PlexBuildingType.PENTHOUSE_PLEX)\n\n    def get_active_zone_plex_id(self):\n        zone_id = services.current_zone_id()\n        if zone_id in self._zone_to_master_map:\n            _, plex_id = self._zone_to_master_map[zone_id]\n            return plex_id\n\n    def get_master_zone_id(self, child_zone_id):\n        if child_zone_id in self._zone_to_master_map:\n            master_id, _ = self._zone_to_master_map[child_zone_id]\n            return master_id\n        return child_zone_id\n\n    def get_plex_building_type(self, zone_id):\n        persistence_service = services.get_persistence_service()\n        house_description_id = persistence_service.get_house_description_id(zone_id)\n        return PlexBuildingType(services.get_building_type(house_description_id))\n\n    def get_plex_zones_in_group(self, zone_id):\n        if zone_id in self._zone_to_master_map:\n            master_id, _ = self._zone_to_master_map[zone_id]\n            return frozenset((z for z, (m, _) in self._zone_to_master_map.items() if m == master_id))\n        return set()\n\n    def zone_to_master_map_gen(self):\n        yield from self._zone_to_master_map.items()\n        if False:\n            yield None\n\n    def get_plex_zone_at_position(self, world_position, level):\n        active_zone_id = services.current_zone_id()\n        if active_zone_id not in self._zone_to_master_map:\n            return\n        master_id, _ = self._zone_to_master_map[active_zone_id]\n        plex_id = build_buy.get_location_plex_id(world_position, level)\n        for zone_id, (other_master_id, other_plex_id) in self._zone_to_master_map.items():\n            if master_id == other_master_id and plex_id == other_plex_id:\n                return zone_id\n\n    def is_position_in_common_area_or_active_plex(self, world_position, level):\n        if not services.current_zone().lot.is_position_on_lot(world_position, level):\n            return False\n        plex_zone_id = self.get_plex_zone_at_position(world_position, level)\n        if plex_zone_id is None or plex_zone_id == services.current_zone_id():\n            return True\n        return False\n\n    def get_plex_polygons(self, level):\n        zone_id = services.current_zone_id()\n        if zone_id not in self._zone_to_master_map:\n            logger.error(\"Can't get polygons for a non-plex: {}\", zone_id)\n            return []\n        _, plex_id = self._zone_to_master_map[zone_id]\n        blocks = build_buy.get_plex_outline(plex_id, level)\n        polygons = []\n        for block in blocks:\n            logger.assert_log(len(block) == 1, 'Plex has cutouts. get_plex_polygons needs to be updated. Zone: {}, Level: {}', zone_id, level)\n            vertices = list(reversed(block[0]))\n            polygon = Polygon(vertices)\n            polygons.append(polygon)\n\n        return polygons","sub_path":"Scripts/simulation/plex/plex_service.py","file_name":"plex_service.py","file_ext":"py","file_size_in_byte":4279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"492435437","text":"import smbus\nimport RPi.GPIO as GPIO\nfrom time import sleep\n\nGPIO.setmode(GPIO.BCM)\n\nbus = smbus.SMBus(1)\n\n#activity detect config\nbus.write_byte_data(0x53,0x24,10)\nint_act = 0x10\nintEnable = bus.read_byte_data(0x53,0x2E)\nbus.write_byte_data(0x53,0x2E, intEnable | int_act)\n\n#inactivity detect config\nbus.write_byte_data(0x53,0x25,10)\nbus.write_byte_data(0x53,0x26,1)\nint_inact = 0x08\nintEnable = bus.read_byte_data(0x53,0x2E)\nbus.write_byte_data(0x53,0x2E, intEnable | int_inact)\n\ndef my_callback(channel):\n    print('AAA')\n\n#GPIO.add_event_detect(4, GPIO.FALLING, callback = my_callback, bouncetime = 300)\n\nwhile True:\n    inter = bus.read_byte_data(0x53,0x30)\n    if((inter&0x10)==0x10):\n        print(\"ACT detected\\n\")\n    if((inter&0x08)==0x08):\n        print(\"INACT detected\\n\")\n\n","sub_path":"_i2c_adxl_act_old.py","file_name":"_i2c_adxl_act_old.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"505507409","text":"import json\nimport sys\n\ncountry_path = sys.argv[1]\ncity_path = sys.argv[2]\ncountryLang_path = sys.argv[3]\n\nwith open(country_path) as f:\n    country_data = json.load(f)\n\nwith open(city_path) as f:\n    city_data = json.load(f)\n\nwith open(countryLang_path) as f:\n    countryLang_data = json.load(f)\n\n\ndef findCountriesCapitals():\n    \"\"\"Find names of countries and their capital cities for all countries in “North America”\"\"\"\n    data = {}\n    for country in country_data:\n        if country['Continent'] == 'North America':\n            capital_id = country['Capital']\n            for city in city_data:\n                if city['ID'] == capital_id:\n                    data[country['Name']] = city['Name']\n    data = dict(sorted(data.items()))\n\n    for k,v in data.items():\n        str1 = k\n        str1 += ', '\n        str1 += v\n        print(str1)\n    return data\n\nfindCountriesCapitals()","sub_path":"hw/homework1/Matthew_Brennan/python-A.py","file_name":"python-A.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"506888425","text":"from urllib import request\nimport re\nimport os\nimport threading\nfrom atexit import register\nfrom time import ctime\nimport time\n\n# 会重复两次\n_re_page_url = re.compile(r'<a href=\"(.*?)\" target=\"_blank\">')\n_re_img = re.compile(r'<li><img alt=\".*?\" src=\"(.*?)\" /></a></li>')\n\n# name取列表最后一个\n_re_page_name = re.compile(r'<h1>(.*?)</h1>')\n\n\nclass LesMao(object):\n    def __init__(self, url):\n        self.url = url\n\n    def make_url(self, page):\n        return self.url + str(page) + '.html'\n\n    def get_full_url_list(self, page_id):\n        url = self.make_url(page_id)\n        page = request.urlopen(url).read().decode()\n        page_list = _re_page_url.findall(page)\n        return list(set(page_list))\n\n    def re_img_list_and_page_name(self, url):\n        img_url_list = []\n        # 先弄第一页\n        page = request.urlopen(url).read().decode()\n        page_name = _re_page_name.findall(page)[-1]\n        img_url = _re_img.findall(page)\n        img_url_list += img_url\n        page_num = 5\n        print('开始加载 %s' % page_name)\n        # 再弄后面的\n        for index in range(2, page_num + 1):\n            # 'http://www.lesmao.com/thread-15032-1-1.html'\n            _re_temp = re.compile(r'(http://www\\.lesmao\\.com/thread-\\d*?)-\\d*?-\\d*?\\.html')\n            u = _re_temp.findall(url)[0] + '-%s-%s' % (index, index) + '.html'\n            page = request.urlopen(u).read().decode()\n            img_url = _re_img.findall(page)\n            try:\n                img_url_list += img_url\n            except IndexError:\n                pass\n        return img_url_list, page_name\n\n    def mk_dir(self, page_name):\n        if os.path.exists('%s' % page_name):\n            print(\"已经爬过 %s\" % page_name)\n            return False\n        else:\n            print('创建 %s' % page_name)\n\n            # 文件名字中可能会出现 '／'影响文件夹创建 再想想好办法 -------------------------------\n            os.mkdir(('%s' % page_name)[:16])\n            return True\n\n    def pull_img(self, img_url_list, page_name):\n        i = 0\n        for img_url in img_url_list:\n            print('%s 正在努力加载 %s img-%s ' % (threading.current_thread().name, page_name, i))\n            with open(os.path.join(('%s' % page_name)[:16], str(i)) + '.jpg', 'wb') as file:\n                file.write(request.urlopen(img_url).read())\n            i += 1\n\n    def get_one(self, one_url):\n        img_url_list, page_name = self.re_img_list_and_page_name(one_url)\n        if self.mk_dir(page_name):\n            t = threading.Thread(target=self.pull_img, args=(img_url_list, page_name))\n            print('%s 开始为老爷工作～～' % threading.current_thread().name)\n            t.start()\n        else:\n            pass\n\n    def run(self, start, end):\n        print('显示加载后可能会因为网速 有20秒左右没反应 请稍等(不要着急 我试了 没bug的)')\n        for i in range(start, end + 1):\n            full_url_list = self.get_full_url_list(i)\n            for one_url in full_url_list:\n                t = threading.Thread(target=self.get_one, args=(one_url,))\n                t.start()\n\n@register\ndef _atexit():\n    print('报告长官 完成任务 %s' % ctime())\n\nif __name__ == '__main__':\n    url = 'http://www.lesmao.com/forum-43-'\n    l = LesMao(url)\n    l.run(7, 9)\n","sub_path":"crawers/crawers for pic/1 level/lesmao/spider.py","file_name":"spider.py","file_ext":"py","file_size_in_byte":3335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"49484781","text":"# -*- coding: utf-8 -*-\nimport torch\nimport torch.nn as nn\nfrom transformers import BertTokenizer, BertModel,BertConfig,AutoModel,AutoConfig\nimport os\nclass tkitRetransformer:\n    \"\"\"\n    tkitRetransformer\n    用来修改transformers模型\n    \n    \"\"\"\n    def __init__(self,from_pretrained,tokenizer):\n        \"\"\"[模块说明]\n        \"\"\"\n        MODEL_NAME=from_pretrained\n        self.tokenizer=tokenizer\n\n\n        self.config = AutoConfig.from_pretrained(MODEL_NAME)\n        # tokenizer = BertTokenizer.from_pretrained(tokenizer_MODEL_NAME)\n\n        self.model = AutoModel.from_pretrained(MODEL_NAME)\n        pass\n    def edit(self):\n        \"\"\"Edit\n        基本的模型编辑示例\n\n        \"\"\"\n        self.config.position_embedding_type=\"relative_key_query\"\n        self.config.vocab_size=tokenizer.vocab_size\n        self.config.type_vocab_size=100\n        # tokenizer\n        self.model.embeddings.word_embeddings=nn.Embedding(self.tokenizer.vocab_size, self.model.embeddings.word_embeddings.embedding_dim, padding_idx=self.tokenizer.pad_token_id)\n        # 修改嵌入类型\n        # help(model.embeddings.token_type_embeddings)\n\n        self.model.embeddings.token_type_embeddings=nn.Embedding(self.config.type_vocab_size, self.model.embeddings.token_type_embeddings.embedding_dim)\n    def save(self, path='./model'):\n        \"\"\"Save the model\n\n        path：为保存模型的目录\n\n        \"\"\"\n        self.config.save_pretrained(path)\n        self.tokenizer.save_pretrained(path)\n        PATH=os.path.join(path,\"pytorch_model.bin\")\n        torch.save(self.model.state_dict(), PATH)\n        print(\"model save to:\",path)\n\n\nif __name__ == \"__main__\":\n    print(\"测试保存模型\")\n    MODEL_NAME=\"uer/chinese_roberta_L-2_H-512\"\n    tokenizer_MODEL_NAME=\"clue/roberta_chinese_clue_tiny\"\n    tokenizer = BertTokenizer.from_pretrained(tokenizer_MODEL_NAME)\n    trt=tkitRetransformer(MODEL_NAME,tokenizer)\n    trt.save()","sub_path":"tkitRetransformer/tkitRetransformer.py","file_name":"tkitRetransformer.py","file_ext":"py","file_size_in_byte":1938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"354519819","text":"from PyQt4 import QtCore, QtGui\nfrom MainWindows import Ui_LyBrowser\nfrom MyPageClass import MyPageClass\n\nclass MyMainWindows(QtGui.QMainWindow):\n    BrowserHeader=\"Lybrowser\"\n    def __init__(self, parent=None):\n        super(MyMainWindows, self).__init__(parent)\n        self.ui = Ui_LyBrowser()\n        self.ui.setupUi(self)\n        self.setWindowFlags(QtCore.Qt.CustomizeWindowHint|QtCore.Qt.WindowSystemMenuHint)\n        \n        self.thread=MyThread()\n        self.thread.render()\n        \n        self.new_flag()\n        \n        QtCore.QObject.connect(self.ui.menu_smallest,QtCore.SIGNAL(\"triggered()\"),self.showMinimized)\n        QtCore.QObject.connect(self.ui.menu_bigest,QtCore.SIGNAL(\"triggered()\"),self.showMaximized)\n        QtCore.QObject.connect(self.ui.Flags,QtCore.SIGNAL(\"tabCloseRequested (int)\"),self.close_tab)\n        QtCore.QObject.connect(self.ui.menu_exit,QtCore.SIGNAL(\"triggered()\"),self.close)\n        QtCore.QObject.connect(self.ui.menu_new_flag,QtCore.SIGNAL(\"triggered()\"),self.new_flag)\n        QtCore.QObject.connect(self.ui.menu_close_flag,QtCore.SIGNAL(\"triggered()\"),self.close_current)\n        QtCore.QObject.connect(self.thread,QtCore.SIGNAL(\"PerSecond()\"), self.time_change)\n        QtCore.QObject.connect(self.ui.Flags,QtCore.SIGNAL(\"currentChanged(int)\"), self.main_title_change)\n    \n    def new_flag(self):\n        Cur_index=self.ui.Flags.currentIndex()\n        \n        New_page=MyPageClass()\n        self.ui.Flags.insertTab(Cur_index+1, New_page,New_page.get_title())\n        \n        New_index=self.ui.Flags.indexOf(New_page)\n        self.tab_icon_change(New_page)\n        self.ui.Flags.setCurrentIndex(New_index)\n        New_page.ui.webView.setFocus()\n        \n        \n        QtCore.QObject.connect(self.ui.Flags.widget(New_index),QtCore.SIGNAL(\"tab_new_flag()\"),self.new_flag)\n        QtCore.QObject.connect(self.ui.Flags.widget(New_index),QtCore.SIGNAL(\"tab_title_change(QObject*)\"), self.tab_title_change)\n        QtCore.QObject.connect(self.ui.Flags.widget(New_index),QtCore.SIGNAL(\"tab_icon_change(QObject*)\"), self.tab_icon_change)\n        QtCore.QObject.connect(self.ui.Flags.widget(New_index),QtCore.SIGNAL(\"hover_change(QString&)\"),self.hover_change)\n        QtCore.QMetaObject.connectSlotsByName(self)\n        \n        \n        \n    def tab_title_change(self,Widget_title_change):\n        self.ui.Flags.setTabText(self.ui.Flags.indexOf(Widget_title_change),Widget_title_change.get_title())\n        self.main_title_change(self.ui.Flags.currentIndex())\n            \n    def tab_icon_change(self,widget_icon_change):\n        self.ui.Flags.setTabIcon(self.ui.Flags.indexOf(widget_icon_change),widget_icon_change.get_icon())\n    \n        \n    def time_change(self):\n        self.ui.CurTime.setTime(QtCore.QTime.currentTime())\n    \n    def main_title_change(self,CurIndex):\n        if self.ui.Flags.widget(CurIndex)!=None:\n            self.ui.CurTitle.setText(self.ui.Flags.widget(self.ui.Flags.currentIndex()).get_whole_title())\n    \n    def close_current(self):\n        current_index=self.ui.Flags.currentIndex()\n        current_widget=self.ui.Flags.currentWidget()\n        self.ui.Flags.removeTab(current_index)\n        current_widget.close()\n        \n    def close_tab(self,index):\n        close_widget=self.ui.Flags.widget(index)\n        self.ui.Flags.removeTab(index)\n        close_widget.close()\n    \n    def hover_change(self,new_hover):\n        self.ui.CurHoverUrl.setText(new_hover)\n        \n        \nclass MyThread(QtCore.QThread):\n    def __int__(self,parent=None):\n        QtCore.QThread.__init__(self, parent)\n        self.exiting = False\n\n    def __del__(self):\n        self.exiting = True\n        self.wait()\n    \n    def render(self):\n        self.start()\n    \n    def run(self):\n        while 1:\n            self.emit(QtCore.SIGNAL(\"PerSecond()\"))\n            self.sleep(1)\n        ","sub_path":"MainWindowsClass.py","file_name":"MainWindowsClass.py","file_ext":"py","file_size_in_byte":3854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"171779089","text":"#!/usr/bin/env python\n\nimport copy\nimport json\nimport os\nimport pytest\nimport re\nimport logging\n# from urllib.parse import urlparse\n\nfrom blackduck.HubRestApi import HubInstance\nfrom unittest.mock import MagicMock  # , mock_open, patch\n\n\nlogging.basicConfig()\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\n\ndef my_open(file_):\n    \"\"\" Allows the use of pytest on both an installed version of this module\n    and as src before installation. That is: allow the following:\n     - run `python -m pytest` outside `test/' which will find and run the\n       test_*.py file and test against the local `blackduck/` source\n     - run `pytest` in or outside `test/` which will load the test_*.py file\n       and test against the installed version of blackduck\n    \"\"\"\n    try:\n        return open(os.path.join('test', file_))\n    except Exception:\n        return open(file_)\n\n\nfake_hub_host = \"https://my-hub-host\"\n\npolicies_json_file = my_open('policies.json')\npolicies = json.loads(policies_json_file.read())\nassert 'items' in policies\ntest_policy = policies['items'][0]\n\n\ndef return_auth_token(api_token):\n    if api_token:\n        return (\"the-token\", \"the-csrf-token\")\n    else:\n        return (\"the-token\", None)\n\n\ninvalid_bearer_token = \"anInvalidTokenValue\"\ninvalid_csrf_token = \"anInvalidCSRFTokenValue\"\nmade_up_api_token = \"theMadeUpAPIToken\"\n\n\ndef setup_function(function):\n    # Remove .restconfig file before running any test\n    # try:\n    #     os.remove(HubInstance.configfile)\n    # except OSError:\n    #     pass\n    pass\n\n\ndef teardown_function(function):\n    # Remove .restconfig file after running any test\n    # try:\n    #     os.remove(HubInstance.configfile)\n    # except OSError:\n    #     pass\n    pass\n\n\n@pytest.fixture()\ndef mock_hub_instance(requests_mock):\n    requests_mock.post(\n        \"{}/j_spring_security_check\".format(fake_hub_host),\n        headers={\"Set-Cookie\": f'AUTHORIZATION_BEARER={invalid_bearer_token}; '\n                               'Path=/; secure; Secure; HttpOnly'.format()},\n        status_code=204\n    )\n    requests_mock.get(\n        \"{}/api/current-version\".format(fake_hub_host),\n        json={\n            \"version\": \"2018.11.0\",\n            \"_meta\": {\n                \"allow\": [\n                    \"GET\"\n                ],\n                \"href\": \"{}/api/current-version\".format(fake_hub_host)\n            }\n        }\n    )\n    yield HubInstance(fake_hub_host, \"a_username\", \"a_password\")\n\n\n@pytest.fixture()\ndef mock_hub_instance_using_api_token(requests_mock):\n    requests_mock.post(\n        \"https://my-hub-host/api/tokens/authenticate\",\n        content=json.dumps({\n            'bearerToken': invalid_bearer_token}).encode('utf-8'),\n        headers={\n                'X-CSRF-TOKEN': invalid_csrf_token,\n                'Content-Type': 'application/json'\n            }\n    )\n    requests_mock.get(\n        \"{}/api/current-version\".format(fake_hub_host),\n        json={\n            \"version\": \"2018.11.0\",\n            \"_meta\": {\n                \"allow\": [\n                    \"GET\"\n                ],\n                \"href\": \"{}/api/current-version\".format(fake_hub_host)\n            }\n        }\n    )\n\n    yield HubInstance(fake_hub_host, api_token=made_up_api_token)\n\n\n@pytest.fixture()\ndef policy_info_json():\n    with my_open('policies.json') as policies_json_file:\n        yield json.loads(policies_json_file.read())\n\n\n@pytest.fixture()\ndef a_test_policy():\n    yield test_policy\n\n\n@pytest.fixture()\ndef a_test_policy_for_create_or_update():\n    # a_policy_for_creating_or_updating = dict(\n    #     (attr, test_policy[attr]) for attr in\n    #     ['name', 'description', 'enabled', 'overridable', 'expression',\n    #      'severity'] if attr in test_policy)\n    # yield a_policy_for_creating_or_updating\n    yield test_policy\n\n\n@pytest.fixture()\ndef test_vulnerability_info(requests_mock):\n    with my_open('sample-vulnerability.json') as sample_vulnerability_file:\n        yield json.loads(sample_vulnerability_file.read())\n\n\ndef test_get_major_version(requests_mock):\n    requests_mock.post(\n        \"{}/j_spring_security_check\".format(fake_hub_host),\n        headers={\"Set-Cookie\": 'AUTHORIZATION_BEARER={invalid_bearer_token}; '\n                               'Path=/; secure; Secure; HttpOnly'},\n        status_code=204\n    )\n    for version in [\"2018.11.0\", \"5.0.2\", \"4.8.3\", \"3.7.2\"]:\n        expected = version.split(\".\")[0]\n        requests_mock.get(\n            \"{}/api/current-version\".format(fake_hub_host),\n            json={\n                \"version\": \"{}\".format(version),\n                \"_meta\": {\n                    \"allow\": [\n                        \"GET\"\n                    ],\n                    \"href\": \"{}/api/current-version\".format(fake_hub_host)\n                }\n            }\n        )\n        hub = HubInstance(fake_hub_host, \"a_username\", \"a_password\")\n        assert hub.bd_major_version == expected\n\n\ndef test_get_headers(mock_hub_instance):\n    # somewhat contrived, but it does execute all the paths\n    # TODO: better way to test this one?\n    #\n    the_api_token = \"fake-api-token\"\n    the_csrf_token = \"fake-csrf-token\"\n    the_token = \"fake-bearer-token\"\n    mock_hub_instance.config['api_token'] = the_api_token\n    mock_hub_instance.csrf_token = the_csrf_token\n    mock_hub_instance.token = the_token\n\n    assert mock_hub_instance.get_headers() == {\n                'X-CSRF-TOKEN': the_csrf_token,\n                'Authorization': \"Bearer {}\".format(the_token),\n                'Content-Type': 'application/json'}\n\n    del mock_hub_instance.config['api_token']\n    for bd_major_version in [\"2018\", \"5\", \"4\", \"3\"]:\n        if bd_major_version == \"3\":\n            expected = {\"Cookie\": mock_hub_instance.cookie}\n        else:\n            expected = {\"Authorization\": \"Bearer \" + mock_hub_instance.token}\n\n        mock_hub_instance.bd_major_version = bd_major_version\n        assert mock_hub_instance.get_headers() == expected\n\n\ndef test_get_policy_url(mock_hub_instance):\n    assert mock_hub_instance._get_policy_url() == fake_hub_host + \\\n        \"/api/policy-rules\"\n\n\ndef test_get_parameter_string(mock_hub_instance):\n    assert mock_hub_instance._get_parameter_string({\"limit\": \"100\"}) \\\n        == \"?limit=100\"\n    assert mock_hub_instance._get_parameter_string(\n        {\"limit\": \"100\", \"q\": \"name:my-name\"}) == \"?limit=100&q=name:my-name\"\n    assert mock_hub_instance._get_parameter_string(\n        {\"limit\": \"100\", \"sort\": \"updatedAt\"}) == \"?limit=100&sort=updatedAt\"\n\n\ndef test_hub_instance_username_password_for_auth(mock_hub_instance):\n    assert mock_hub_instance.get_headers() == \\\n        {\"Authorization\": f\"Bearer {invalid_bearer_token}\"}\n\n    assert 'api_token' not in mock_hub_instance.config\n    assert 'baseurl' in mock_hub_instance.config\n    assert 'username' in mock_hub_instance.config\n    assert 'password' in mock_hub_instance.config\n\n\ndef test_hub_instance_api_token_for_auth(mock_hub_instance_using_api_token):\n    assert mock_hub_instance_using_api_token.get_headers() == {\n                'X-CSRF-TOKEN': invalid_csrf_token,\n                'Authorization': 'Bearer {}'.format(invalid_bearer_token),\n                'Content-Type': 'application/json'}\n\n    assert 'api_token' in mock_hub_instance_using_api_token.config\n    assert 'baseurl' in mock_hub_instance_using_api_token.config\n    assert 'username' not in mock_hub_instance_using_api_token.config\n    assert 'password' not in mock_hub_instance_using_api_token.config\n\n\ndef test_get_policy_by_id(requests_mock, mock_hub_instance, a_test_policy):\n    requests_mock.get(fake_hub_host + \"/api/policy-rules/00000000-0000-0000-\"\n                                      \"0000-000000000001\", json=test_policy)\n    policy = mock_hub_instance.get_policy_by_id(\"00000000-0000-0000-0000-\"\n                                                \"000000000001\")\n    for key in a_test_policy.keys():\n        assert policy[key] == a_test_policy[key]\n\n\ndef test_get_policy_by_url(requests_mock, mock_hub_instance, a_test_policy):\n    requests_mock.get(fake_hub_host + \"/api/policy-rules/00000000-0000-0000-\"\n                                      \"0000-000000000001\", json=test_policy)\n    policy = mock_hub_instance.get_policy_by_url(\n        mock_hub_instance._get_policy_url() + \"/00000000-0000-0000-0000-\"\n                                              \"000000000001\")\n    for key in a_test_policy.keys():\n        assert policy[key] == a_test_policy[key]\n\n\ndef test_update_policy_by_id(\n    requests_mock,\n    mock_hub_instance,\n    a_test_policy,\n    a_test_policy_for_create_or_update\n):\n    policy_id = a_test_policy['_meta']['href'].split(\"/\")[-1]\n\n    requests_mock.put(fake_hub_host + \"/api/policy-rules/\" + policy_id,\n                      json=test_policy)\n    response = mock_hub_instance.update_policy_by_id(\n        policy_id, a_test_policy_for_create_or_update)\n    assert response.status_code == 200\n    assert response.json() == a_test_policy\n\n\ndef test_update_policy_by_url(\n    requests_mock,\n    mock_hub_instance,\n    a_test_policy,\n    a_test_policy_for_create_or_update\n):\n    policy_id = a_test_policy['_meta']['href'].split(\"/\")[-1]\n    policy_url = mock_hub_instance._get_policy_url() + \"/\" + policy_id\n\n    requests_mock.put(fake_hub_host + \"/api/policy-rules/\" + policy_id,\n                      json=test_policy)\n    response = mock_hub_instance.update_policy_by_url(\n        policy_url, a_test_policy_for_create_or_update)\n    assert response.status_code == 200\n    assert response.json() == a_test_policy\n\n\ndef test_create_policy(\n    requests_mock,\n    mock_hub_instance,\n    a_test_policy,\n    a_test_policy_for_create_or_update\n):\n    requests_mock.post(fake_hub_host + \"/api/policy-rules\",\n                       headers={\"location\": a_test_policy['_meta']['href']},\n                       status_code=201)\n    # print(json.dumps(a_test_policy_for_create_or_update))\n    new_policy_url = mock_hub_instance.create_policy(\n        a_test_policy_for_create_or_update)\n    assert new_policy_url == a_test_policy['_meta']['href']\n\n\ndef test_delete_policy_by_id(requests_mock, mock_hub_instance, a_test_policy):\n    policy_id = a_test_policy['_meta']['href'].split(\"/\")[-1]\n\n    requests_mock.delete(fake_hub_host + \"/api/policy-rules/\" +\n                         a_test_policy['_meta']['href'].split(\"/\")[-1],\n                         status_code=204)\n    response = mock_hub_instance.delete_policy_by_id(policy_id)\n    assert response.status_code == 204\n\n\ndef test_delete_policy_by_url(requests_mock, mock_hub_instance, a_test_policy):\n    policy_url = mock_hub_instance._get_policy_url() + \"/\" + \\\n        a_test_policy['_meta']['href'].split(\"/\")[-1]\n\n    requests_mock.delete(policy_url, status_code=204)\n    response = mock_hub_instance.delete_policy_by_url(policy_url)\n    assert response.status_code == 204\n\n\ndef test_get_vulnerability(\n    requests_mock,\n    mock_hub_instance,\n    test_vulnerability_info\n):\n    vulnerability_url = mock_hub_instance._get_vulnerabilities_url() + \\\n        \"/{}\".format(test_vulnerability_info['vulnerabilityName'])\n\n    requests_mock.get(vulnerability_url, json=test_vulnerability_info)\n    response_json = mock_hub_instance.get_vulnerabilities(\n        test_vulnerability_info['vulnerabilityName'])\n\n    assert response_json == test_vulnerability_info\n\n\ndef test_get_projects(requests_mock, mock_hub_instance):\n    url = mock_hub_instance.get_urlbase() + \"/api/projects\"\n    json_data = json.load(my_open('sample-projects.json'))\n    requests_mock.get(url, json=json_data)\n\n    # url and data from the sample-projects[-versions].json, used inside of\n    # Projects() for getting project version(s)\n    mock_urls = ('https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/65f272df-3a2a-4022-8811-a57e05e82f52/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/5e291539-9c59-43d9-9c0f-bf0be1ffd477/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/1e0f81ef-7e20-475d-b166-357efa3bbc4e/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/b1a467a2-3c77-4f67-9c44-d14f79e2229f/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/2ffe6a09-b51e-480f-a79a-6905ddc656af/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/079e691e-fa16-42d7-bfad-a2f807af95b5/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/755b4119-b3d6-43bd-9586-f5a36af19c6b/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/79bf8154-6008-40a9-9929-4c800ddac455/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/189db678-de6a-4eb8-b214-4f57de503e23/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/0c35bfcb-f3ee-4669-b79e-1a9dd1eca2aa/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/f8b1c2fc-e7ac-49f7-a884-c4f9d8db0e19/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/2f1371a5-d3de-4524-a0f0-71aafc401c69/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/585fa1b3-73e8-4657-8b70-954bc41a180b/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/7148f733-1119-403c-926a-28ae1c4a140c/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/a7d5fe9b-61e0-4a39-b11d-8cdf60d9038d/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/f0b39daa-e62c-43a4-8a87-f32d0fd3317e/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/06812072-37de-42b9-846d-bd92fe29f721/versions',\n                 'https://ec2-18-217-189-8.us-east-2.compute.amazonaws.com/'\n                 'api/projects/0a5f6122-e48e-4427-99af-40d749536cb4/versions')\n\n    mock_json_data = json.load(my_open('sample-project-versions.json'))\n    for url in mock_urls:\n        requests_mock.get(url, json=mock_json_data)\n\n    projects = mock_hub_instance.get_projects()\n    assert str(projects) == 'Projects<count=18>'\n\n    result = '''[Project<name=\"accelerator-initializer-ui\", id=UUID('65f272df-\n3a2a-4022-8811-a57e05e82f52'), versions=1>, Project<name=\"app1\", id=UUID('5e29\n1539-9c59-43d9-9c0f-bf0be1ffd477'), versions=1>, Project<name=\"app2\", id=UUID(\n'1e0f81ef-7e20-475d-b166-357efa3bbc4e'), versions=1>, Project<name=\"business-c\nredit-monitoring-DIV2\", id=UUID('b1a467a2-3c77-4f67-9c44-d14f79e2229f'), versi\nons=1>, Project<name=\"business-credit-scoring-DIV2\", id=UUID('2ffe6a09-b51e-48\n0f-a79a-6905ddc656af'), versions=1>, Project<name=\"combined1\", id=UUID('079e69\n1e-fa16-42d7-bfad-a2f807af95b5'), versions=1>, Project<name=\"combined2\", id=UU\nID('755b4119-b3d6-43bd-9586-f5a36af19c6b'), versions=1>, Project<name=\"consume\nr-credit-monitoring-DIV1\", id=UUID('79bf8154-6008-40a9-9929-4c800ddac455'), ve\nrsions=1>, Project<name=\"consumer-credit-scoring-DIV1\", id=UUID('189db678-de6a\n-4eb8-b214-4f57de503e23'), versions=1>, Project<name=\"lib\", id=UUID('0c35bfcb-\nf3ee-4669-b79e-1a9dd1eca2aa'), versions=1>, Project<name=\"struts-showcase-no-o\nptions-thread0\", id=UUID('f8b1c2fc-e7ac-49f7-a884-c4f9d8db0e19'), versions=1>,\n Project<name=\"struts-showcase-no-sig-scanner-thread0\", id=UUID('2f1371a5-d3de\n-4524-a0f0-71aafc401c69'), versions=1>, Project<name=\"struts-showcase-policy-a\nnd-risk-no-sig-scanner-thread0\", id=UUID('585fa1b3-73e8-4657-8b70-954bc41a180b\n'), versions=1>, Project<name=\"struts-showcase-policy-and-risk-thread0\", id=UU\nID('7148f733-1119-403c-926a-28ae1c4a140c'), versions=1>, Project<name=\"struts-\nshowcase-policy-check-no-sig-scanner-thread0\", id=UUID('a7d5fe9b-61e0-4a39-b11\nd-8cdf60d9038d'), versions=1>, Project<name=\"struts-showcase-policy-check-thre\nad0\", id=UUID('f0b39daa-e62c-43a4-8a87-f32d0fd3317e'), versions=1>, Project<na\nme=\"struts-showcase-risk-report-thread0\", id=UUID('06812072-37de-42b9-846d-bd9\n2fe29f721'), versions=1>, Project<name=\"struts2-showcase\", id=UUID('0a5f6122-e\n48e-4427-99af-40d749536cb4'), versions=1>]'''\n    result = result.replace('\\n', '')\n    assert str(list(projects)) == result\n\n\ndef test_get_projects_with_limit(requests_mock, mock_hub_instance):\n    url = mock_hub_instance.get_urlbase() + \"/api/projects?limit=20\"\n    json_data = json.load(my_open('sample-projects.json'))\n    requests_mock.get(url, json=json_data)\n    projects = mock_hub_instance._get_projects(limit=20)\n\n    assert json_data == projects\n    assert 'totalCount' in projects\n    assert projects['totalCount'] == 18\n\n\ndef test_get_projects_with_name_query(requests_mock, mock_hub_instance):\n    url = mock_hub_instance.get_urlbase() + \"/api/projects?q=name:accelerat\" \\\n                                            \"or-initializer-ui&limit=100\"\n    json_data = json.load(my_open('sample-projects-using-name-query.json'))\n    requests_mock.get(url, json=json_data)\n    projects = mock_hub_instance._get_projects(\n        parameters={'q': \"name:accelerator-initializer-ui\"})\n\n    assert json_data == projects\n    assert 'totalCount' in projects\n    assert projects['totalCount'] == 1\n\n\ndef test_get_project_versions(requests_mock, mock_hub_instance):\n    baseurl = mock_hub_instance.get_urlbase()\n    url = baseurl + \"/api/projects/65f272df-3a2a-4022-8811-a57e05e82f52/\" \\\n                    \"versions?limit=100\"\n    json_data = json.load(my_open('sample-project-versions.json'))\n    project_json_data = json.load(my_open('sample-project.json'))\n    # replace project URL with the right one to agree with our mocked URL above\n    project_json_data['_meta']['href'] = re.sub(\"https://.*/api\",\n                                                f\"{baseurl}/api\",\n                                                project_json_data['_meta']\n                                                ['href'])\n    requests_mock.get(url, json=json_data)\n    versions = mock_hub_instance.get_project_versions(project_json_data)\n\n    assert 'totalCount' in versions\n    assert versions['totalCount'] == 1\n\n\ndef test_get_project_versions_with_parameters(\n    requests_mock,\n    mock_hub_instance\n):\n    baseurl = mock_hub_instance.get_urlbase()\n    url = baseurl + \"/api/projects/65f272df-3a2a-4022-8811-a57e05e82f52/\" \\\n                    \"versions?limit=100&q=versionName:1.0\"\n    json_data = json.load(my_open('sample-project-versions.json'))\n    project_json_data = json.load(my_open('sample-project.json'))\n    # replace project URL with the right one to agree with our mocked URL above\n    project_json_data['_meta']['href'] = re.sub(\"https://.*/api\",\n                                                f\"{baseurl}/api\",\n                                                project_json_data['_meta']\n                                                ['href'])\n    requests_mock.get(url, json=json_data)\n    versions = mock_hub_instance.get_project_versions(\n        project_json_data,\n        parameters={'q': 'versionName:1.0'})\n\n    assert 'totalCount' in versions\n    assert versions['totalCount'] == 1\n    assert 'items' in versions\n\n\ndef test_delete_project_version_by_name():\n    # TODO: Write this test\n    pass\n\n\ndef test_get_users(requests_mock, mock_hub_instance):\n    baseurl = mock_hub_instance.get_urlbase()\n    url = baseurl + \"/api/users\"\n    user_json_data = json.load(my_open('users.json'))\n    requests_mock.get(url, json=user_json_data)\n    users = mock_hub_instance.get_users()\n\n    assert 'totalCount' in users\n    assert users['totalCount'] == 1  # there is one user in the sample data\n    assert 'items' in users\n\n\ndef test_create_user(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_get_user_by_id(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_get_user_by_url(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_update_user_by_id(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_update_user_by_url(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_delete_user_by_id(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_delete_user_by_url(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_get_project_by_name(requests_mock, mock_hub_instance):\n    url = mock_hub_instance.get_urlbase() + \"/api/projects\"\n    projects_json = json.load(my_open('sample-projects.json'))\n    project_name = \"accelerator-initializer-ui\"\n    requests_mock.get(url, json=projects_json)\n\n    project = mock_hub_instance.get_project_by_name(project_name)\n\n    assert project['name'] == project_name\n\n\ndef test_get_version_by_name(requests_mock, mock_hub_instance):\n    mock_hub_instance.get_project_versions = \\\n        MagicMock(return_value=json.load(\n            my_open('sample-project-versions.json')))\n\n    mock_project_obj = MagicMock()\n    version_name = \"1.0\"\n    version = mock_hub_instance.get_version_by_name(mock_project_obj,\n                                                    version_name)\n\n    assert version['versionName'] == version_name\n\n\ndef test_create_version_reports(requests_mock, mock_hub_instance):\n    pass\n\n\ndef test_create_version_notices_report(requests_mock, mock_hub_instance):\n    pass\n\n\n@pytest.fixture()\ndef unreviewed_snippet_json():\n    with my_open('unreviewed_snippet.json') as f:\n        yield json.load(f)[0]\n\n\ndef test_get_ignore_snippet_json(unreviewed_snippet_json, mock_hub_instance):\n    assert 'fileSnippetBomComponents' in unreviewed_snippet_json\n    assert unreviewed_snippet_json['fileSnippetBomComponents'][0]['ignored'] \\\n        is False\n\n    result = mock_hub_instance.get_ignore_snippet_json(unreviewed_snippet_json)\n\n    assert isinstance(result, list)\n    assert len(result) == 1\n    assert 'fileSnippetBomComponents' in result[0]\n    assert 'ignored' in result[0]['fileSnippetBomComponents'][0]\n    assert 'reviewStatus' in result[0]['fileSnippetBomComponents'][0]\n    assert result[0]['fileSnippetBomComponents'][0]['ignored'] is True\n    assert result[0]['fileSnippetBomComponents'][0]['reviewStatus'] \\\n        == 'NOT_REVIEWED'\n\n\ndef test_get_confirm_snippet_json(unreviewed_snippet_json, mock_hub_instance):\n    assert 'fileSnippetBomComponents' in unreviewed_snippet_json\n    assert unreviewed_snippet_json['fileSnippetBomComponents'][0]['ignored'] \\\n        is False\n\n    result = mock_hub_instance.get_confirm_snippet_json(\n        unreviewed_snippet_json)\n\n    assert isinstance(result, list)\n    assert len(result) == 1\n    assert 'fileSnippetBomComponents' in result[0]\n    assert 'ignored' in result[0]['fileSnippetBomComponents'][0]\n    assert 'reviewStatus' in result[0]['fileSnippetBomComponents'][0]\n    assert result[0]['fileSnippetBomComponents'][0]['ignored'] is False\n    assert result[0]['fileSnippetBomComponents'][0]['reviewStatus'] \\\n        == 'REVIEWED'\n\n\n@pytest.fixture()\ndef sample_snippet_match_json():\n    with my_open('sample-snippet-match.json') as f:\n        yield json.load(f)\n\n\ndef test_generate_new_match_selection(\n    sample_snippet_match_json,\n    mock_hub_instance\n):\n    assert 'fileSnippetBomComponents' in sample_snippet_match_json\n    assert len(sample_snippet_match_json['fileSnippetBomComponents']) == 1\n\n    # Copy the snippet component match data and delete the keys that are\n    # normally missing from a (vanilla) snippet component alternate match\n    new_component = copy.deepcopy(sample_snippet_match_json[\n        'fileSnippetBomComponents'][0])\n    del new_component['ignored']\n    del new_component['reviewStatus']\n    del new_component['versionBomComponentId']\n    new_component['release']['version'] = 'different-version'\n\n    # now verify that they are restored when we prepare to select/update using\n    # the alternate match\n    update_body = mock_hub_instance._generate_new_match_selection(\n        sample_snippet_match_json, new_component)\n\n    assert len(update_body) == 1\n    assert 'fileSnippetBomComponents' in update_body[0]\n    assert len(update_body[0]['fileSnippetBomComponents']) == 1\n    updated_component_info = update_body[0]['fileSnippetBomComponents'][0]\n    original_component_info = sample_snippet_match_json[\n        'fileSnippetBomComponents'][0]\n    for k in ['ignored', 'reviewStatus', 'versionBomComponentId']:\n        assert k in updated_component_info\n        assert updated_component_info[k] == original_component_info[k]\n\n\n@pytest.fixture()\ndef sample_bom_component_json():\n    with my_open('sample-bom-component.json') as f:\n        yield json.load(f)\n\n\ndef test_get_edit_snippet_json(\n    sample_snippet_match_json,\n    sample_bom_component_json,\n    mock_hub_instance\n):\n    assert 'component' in sample_bom_component_json\n    assert 'componentVersion' in sample_bom_component_json\n    assert 'fileSnippetBomComponents' in sample_snippet_match_json\n    assert len(sample_snippet_match_json['fileSnippetBomComponents']) == 1\n\n    new_component_id = sample_bom_component_json['component'].split(\"/\")[-1]\n    new_component_version_id = sample_bom_component_json[\n        'componentVersion'].split(\"/\")[-1]\n\n    new_snippet_bom_entry_l = mock_hub_instance.get_edit_snippet_json(\n        sample_snippet_match_json, sample_bom_component_json)\n\n    assert len(new_snippet_bom_entry_l) == 1\n    new_snippet_bom_entry = new_snippet_bom_entry_l[0]\n\n    assert new_component_id == new_snippet_bom_entry[\n        'fileSnippetBomComponents'][0]['project']['id']\n    assert new_component_version_id == new_snippet_bom_entry[\n        'fileSnippetBomComponents'][0]['release']['id']\n\n\n@pytest.fixture()\ndef no_roles_user():\n    with my_open('no-roles-user.json') as f:\n        yield json.load(f)\n\n\n@pytest.fixture()\ndef no_roles_roles():\n    with my_open('no-roles-roles.json') as f:\n        yield json.load(f)\n\n\n@pytest.fixture()\ndef sysadmin_user():\n    with my_open('sysadmin-user.json') as f:\n        yield json.load(f)\n\n\n@pytest.fixture()\ndef sysadmin_roles():\n    with my_open('sysadmin-roles.json') as f:\n        yield json.load(f)\n\n\ndef test_user_has_role(\n    no_roles_user,\n    no_roles_roles,\n    sysadmin_user,\n    sysadmin_roles,\n    mock_hub_instance\n):\n    test_roles = [\n        'License Manager',\n        'System Administrator',\n        'Policy Manager',\n        'Project Viewer',\n        'Global Code Scanner',\n        'Project Manager']\n\n    mock_hub_instance.get_roles_for_user_or_group = MagicMock()\n    mock_hub_instance.get_roles_for_user_or_group.return_value = no_roles_roles\n\n    for test_role in test_roles:\n        assert mock_hub_instance.user_has_role(no_roles_user, test_role) \\\n            is False\n\n    mock_hub_instance.get_roles_for_user_or_group.return_value = sysadmin_roles\n\n    for test_role in test_roles:\n        assert mock_hub_instance.user_has_role(sysadmin_user, test_role) \\\n            is True\n\n\ndef test_get_link(mock_hub_instance):\n    a_url = 'http://a-url'\n    link_name = 'a_link_name'\n    bd_rest_obj = {'_meta': {'links': [{'rel': link_name, 'href': a_url}]}}\n\n    assert mock_hub_instance.get_link(bd_rest_obj, link_name) == a_url\n\n\ndef test_get_link_returns_None_for_invalid_bd_rest_object(mock_hub_instance):\n    bd_rest_obj = {}\n\n    assert mock_hub_instance.get_link(bd_rest_obj, 'a_link_name') is None\n\n\ndef test_validated_json_with_a_dictionary(mock_hub_instance):\n    validated_json = mock_hub_instance._validated_json_data({'key': 'value'})\n    assert isinstance(validated_json, str)\n    assert validated_json == '{\"key\": \"value\"}'\n\n\ndef test_validated_json_with_a_list(mock_hub_instance):\n    validated_json = mock_hub_instance._validated_json_data(['item1', 'item2'])\n    assert isinstance(validated_json, str)\n    assert validated_json == '[\"item1\", \"item2\"]'\n\n\ndef test_validated_json_with_a_json_str(mock_hub_instance):\n    validated_json = mock_hub_instance._validated_json_data('{\"key\": \"value\"}')\n\n    assert validated_json == '{\"key\": \"value\"}'\n\n\ndef test_validated_json_fails_with_invalid_json_str(mock_hub_instance):\n    from json import JSONDecodeError\n\n    with pytest.raises(JSONDecodeError) as _:\n        mock_hub_instance._validated_json_data('invalid json')\n","sub_path":"test/test_hub_rest_api_python.py","file_name":"test_hub_rest_api_python.py","file_ext":"py","file_size_in_byte":28501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"571391279","text":"# k_y_tsa_sem5_project_env save with this directory name\nimport re\nimport tweepy\nfrom tweepy import OAuthHandler\nfrom textblob import TextBlob\nimport numpy as np\nimport pandas as pd\nfrom flask import Flask, render_template, request, jsonify\nfrom flask_cors import CORS, cross_origin\n\napp = Flask(__name__)\n\n\n# twitter authentication\nconsumer_key = \"aaaaaaaaaaaaaaaaaaaaaaaaa\"\nconsumer_secret = \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\"\n\naccess_token = \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\"\naccess_token_secret = \"aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa\"\n\ntry:\n    auth = OAuthHandler(consumer_key, consumer_secret)\n    auth.set_access_token(access_token, access_token_secret)\n    api = tweepy.API(auth)\nexcept:\n    print(\"Error: Authentication Failed\")\n\n##############################\n\n\n# all req functions\ndef make_text_clean(tweet):\n\n    return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\n\n\ndef get_tweet_sentiment(tweet):\n\n    analysis = TextBlob(make_text_clean(tweet))\n    if analysis.sentiment.polarity > 0:\n        return 'positive'\n    elif analysis.sentiment.polarity == 0:\n        return 'neutral'\n    else:\n        return 'negative'\n\n\ndef get_tweets(query, count):\n    tweets = []\n\n    try:\n        fetched_tweets = api.search(q = query, count = count)\n        for tweet in fetched_tweets:\n            parsed_tweet = {}\n            parsed_tweet['text'] = tweet.text\n            parsed_tweet['sentiment'] = get_tweet_sentiment(tweet.text)\n\n            if tweet.retweet_count > 0:\n                if parsed_tweet not in tweets:\n                    tweets.append(parsed_tweet)\n            else:\n                tweets.append(parsed_tweet)\n        return tweets\n\n    except tweepy.TweepError as e:\n        print(\"Error : \" + str(e))\n\n\n\n\n\n@app.route('/', methods=['GET'])  # route to display the first page\n@cross_origin()\ndef homepage():\n\n    return render_template('index.html')\n\n\n@app.route('/analysis', methods=['GET', 'POST'])\n@cross_origin()\ndef index():\n\n    if request.method == 'POST':\n\n        try:\n            tweet_string = request.form['content']\n            tweet_count = request.form['count']\n\n            tweets = get_tweets(query=tweet_string, count=tweet_count)\n            # tweets is a dictonary\n\n            p_tweets = [tweet['text'] for tweet in tweets if tweet['sentiment'] == 'positive']\n            n_tweets = [tweet['text'] for tweet in tweets if tweet['sentiment'] == 'negative']\n            neutral_tweets = [tweet['text'] for tweet in tweets if tweet['sentiment'] == 'neutral']\n\n            percent_p_t = 100 * len(p_tweets) / len(tweets)\n            percent_n_t = 100 * len(n_tweets) / len(tweets)\n            percent_neutral_t = 100 * len(neutral_tweets) / len(tweets)\n\n            #print(percent_n_t)\n            #print(percent_p_t)\n            #print(percent_neutral_t)\n            #print(p_tweets[0])\n            #print('-'*30)\n            #print(n_tweets[0])\n            #print('-'*30)\n            #print(neutral_tweets[0])\n\n            #print(\"Positive tweets percentage: {} %\".format(100 * len(p_tweets) / len(tweets)))\n            #all_tweet_data = api.search(q=tweet_string, count=tweet_count)\n\n            #tweet_text = [i.text for i in all_tweet_data]\n\n            #clean_tweets = list(map(make_text_clean, tweet_text))\n\n            if len(p_tweets) < 5:\n                pass\n            else:\n                p_tweets = p_tweets[:5]\n\n            if len(n_tweets) < 5:\n                pass\n            else:\n                n_tweets = n_tweets[:5]\n\n            if len(neutral_tweets) < 5:\n                pass\n            else:\n                neutral_tweets = neutral_tweets[6:11]\n\n\n            return render_template('results.html', tweet_string=tweet_string,\n                                   p_neu = percent_neutral_t, p_n = percent_n_t, p_p = percent_p_t,\n                                   p_tweets=p_tweets, n_tweets=n_tweets, neutral_tweets=neutral_tweets)\n\n        except:\n            pass\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4065,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"587880572","text":"from pymongo import MongoClient\nfrom pprint import pprint\nimport json, random\nnames = json.load(open('names.json'))['first_names']\n\n# connect to MongoDB on localhost\nclient = MongoClient(\"localhost:27017\")\n# create a new database called \"pets\"\ndb=client.pets\n# number of objects to add to the database\nnumber_to_add = 20000\nanimal_type = ['dog', 'cat', 'tiger', 'camel']\nfor x in xrange(0, number_to_add):\n    pet = {\n        'name': names[random.randint(0, 499)],\n        'age': random.randint(0, 20),\n        'animal_type': animal_type[random.randint(0, 3)]\n    }\n    # Insert object into MongoDB one by one\n    result=db.pets.insert_one(pet)\n    # Print to the console the ObjectID of the new document\n    print('Created {0} of {1} as {2}'.format(x,number_to_add, result.inserted_id))\nall_dogs = db.pets.find({'animal_type': 'dog'}).count()\nprint('Dogs added to database: {0}'.format(all_dogs))\n","sub_path":"Python-MongoDB/Python-MongoDB/Tutorial_1/insert_mongo_data_one_by_one.py","file_name":"insert_mongo_data_one_by_one.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"371141995","text":"# -*- coding: utf-8 -*-\n\nimport six\nimport types\n\n#\n# In python 3 there standard type representations BooleanType, etc.\n# were removed from types packages. They are represented with its native\n# expression.\n#\n# Links:\n# - http://www.diveintopython3.net/porting-code-to-python-3-with-2to3.html#types\n\nMethodType = types.MethodType\n\nif six.PY2:\n    InstanceType = types.InstanceType\n# In PY3 use: type(x)\n\nif six.PY2:\n    BooleanType = types.BooleanType\n    IntType = types.IntType\n    LongType = types.LongType\n    FloatType = types.FloatType\n    StringType = types.StringType\n    UnicodeType = types.UnicodeType\nelse:\n    BooleanType = bool\n    IntType = int\n    LongType = int\n    FloatType = float\n    StringType = bytes\n    UnicodeType = str\n","sub_path":"src/utils/py_version_compatibility.py","file_name":"py_version_compatibility.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"473551374","text":"import math\nfrom scipy.signal import correlate2d\nimport warnings\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom random import shuffle\n\nfrom matplotlib import rc\nrc('animation', html='html5')\n\nfrom Cell2D import Cell2D, Cell2DViewer\n\n\"\"\"\nUsing Allen's Cell2D and Cell2DViewer classes to simulate an opinion Landscape\n\"\"\"\n\ndef generatePairs(x,y):\n    for i in xrange(x):\n        for j in xrange(y):\n            yield i,j\n\nclass Landscape(Cell2D):\n    \"\"\"Simulation of opinion Landscape as 2D grid.\"\"\"\n\n    def __init__(self, n, m = None, t = 0.4, k = 6.0):\n        \"\"\"\n        n = number of rows\n        m = number of columns\n\n        \"\"\"\n        self.n = n\n        self.m = n if m is None else m\n        self.array = np.random.random_sample(size= (self.n, self.m))\n        self.t = t\n        self.k = k\n        # self.kernel = np.array([[-.125,-.125,-.125],\n        #                         [-.125,    1,-.125],\n        #                         [-.125,-.125,-.125]])\n        # self.options = dict(mode='same', boundary='wrap')\n\n    def __str__(self):\n        return str(self.array)\n\n    def interact(self,cell1,cell2, k):\n        # One interaction per timestep? Random one-hot kernel to select random neighbor\n        \"\"\"Takes the value of two cells and adjusts them accordingly.\"\"\"\n        difference = math.fabs(cell1-cell2)\n        if difference<=self.t: #if both cells are close enough: \n            if cell1>cell2:\n                # print \"Case 1\"\n                return (cell1-difference/k,cell2+difference/k)\n            else:\n                # print \"Case 2\"\n                return (cell1+difference/k,cell2-difference/k)\n        else: #cells too far apart, they radicalize\n            if cell1>cell2:\n                # print \"Case 3\"\n                return (cell1+(1-cell1)/k,cell2-cell2/k) #the higher one moves toward 1, the lower one moves toward 0\n            else:\n                # print \"Case 4\"\n                return (cell1-cell1/k,cell2+(1-cell2)/k)\n\n    # def step(self,a): #a is self.array\n    #     \"\"\"Simulates one timestep.\"\"\"\n    #     a = np.pad(a,(1,1),'wrap')\n    #     for x,y in generatePairs(self.n,self.m):\n    #         neighbors = a[x:x+3,y:y+3]\n    #         neighbors = [k for k in np.nditer(neighbors)]\n    #         shuffle(neighbors)\n    #         for cell in neighbors:\n    #             (a[x+1,y+1],cell) = self.interact(a[x+1,y+1],cell,self.k)\n    #     return self.measure_segregation(a)\n        \n\n    def step(self,a): #a is self.array\n        \"\"\"Simulates one timestep.\"\"\"\n        # for x in range(a.shape[0]):\n        #     for y in range(a.shape[1]): #turn two for loops into generator! for x,y in generatePairs(): slice = array[x-1:x+2,y-1:y+2] -> make slice/compute a function\n        for x,y in generatePairs(a.shape[0],a.shape[1]):\n            neighbors = []\n            for i in xrange(-1,2):\n                for j in xrange(-1,2): #make loop an array slice!\n                    if (i,j) != (0,0):\n                        neighbors.append(a[(x+i)%a.shape[0],(y+j)%a.shape[1]])\n            shuffle(neighbors)\n            # print neighbors\n            for cell in neighbors:\n                (a[x,y],cell) = self.interact(a[x,y],cell,self.k)\n        return self.measure_segregation(a)\n\n\n\n    def simulate(self, steps, measure_segregation = True):\n        \"\"\"Simulates a changing Landscape over (steps) timesteps.\"\"\"\n        if measure_segregation:\n            store = [self.measure_segregation(self.array)]\n            for i in xrange(steps):\n                store.append(self.step(self.array))\n            return store\n        else:\n            for i in xrange(steps):\n                self.step(self.array)\n            return [1 for i in xrange(steps)]\n\n    def measure_segregation(self,a):\n        \"\"\"Measures how different the average cell is from its neighbors.\"\"\"\n        b = 0\n        # for x in range(a.shape[0]):\n        #     for y in range(a.shape[1]):\n        for x,y in generatePairs(a.shape[0],a.shape[1]):\n            temp = 0\n            for i in xrange(-1,2):\n                for j in xrange(-1,2): #loop guarantees O(9n^2), but there's no way around it\n                    temp+=math.fabs(a[x,y]-a[(x+i)%a.shape[0],(y+j)%a.shape[1]]) #we sum the absolute difference between the center cell and each of its neighbors\n            b+=(temp/8.0) #we divide the sum of differences by 8 \n        return b/(self.n*self.m)\n\n   \n\n\n### Helper functions ###\ndef array_and_graph(n,m,t,k,steps, slice=None, name = 'test'):\n    if slice == None:\n        slice = steps/2\n    \"\"\"Runs a n x m simulation with threshold t over (steps) timesteps.\"\"\"\n    m = n if m is None else m\n    a = Landscape(n,m,t,k)\n\n    c = Cell2DViewer(a)\n    plt.subplot(2,3,1)\n    c.draw_array()\n\n    b = a.simulate(slice)\n    e = Cell2DViewer(a)\n    plt.subplot(2,3,2)\n    e.draw_array()\n\n    d = a.simulate(steps-slice)\n    f = Cell2DViewer(a)\n    plt.subplot(2,3,3)\n    f.draw_array()\n\n    g = b+d\n    plt.subplot(2,3,(4,6))\n    plt.plot([k for k in range(steps)],g[:steps],'r')\n    plt.savefig(name)\n    # plt.show()\n    plt.clf()\n    plt.cla()\n    plt.close()\n\n\narray_and_graph(n=40,m=40,t=0.4495,k=32.0,steps=200, slice=7, name='4495')\n\n# for i in np.linspace(0.4,0.6,20):\n#     array_and_graph(n=40,m=40,t=i,k=32.0,steps=200, slice=12, name='test%d' %(i*100))\n\ndef find_critical_t(n,m,k,steps,iterations,t1,t2,numT):\n    result = []\n    for t in np.linspace(t1,t2,num=numT):\n        store = 0.0\n        for i in range(iterations):\n            a = Landscape(n,m,t)\n            store += sum(a.simulate(steps)[-15:])/15.0\n        result.append(store/iterations)\n    plt.plot(np.linspace(t1,t2,num=numT),result)\n    plt.savefig('sweep4_steps')\n    plt.show()\n\n# find_critical_t(n=40,m=40,k=16.0,steps=200,iterations=2,t1=0.4,t2=0.6,numT=24)","sub_path":"code/Third Pass/Third Pass - messing with variables.py","file_name":"Third Pass - messing with variables.py","file_ext":"py","file_size_in_byte":5779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"483520635","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Date    : 2017-05-26 23:11:04\n# @Author  : YangXiaodi (15564219@qq.com)\n# @Link    : None\n# @Version : $Id$\n# 将包含Nge元素的元住或者序列分解到N个单独的变量\n# 元素个数必须匹配\n\np = (5, 6)\nx, y =p\nprint(x, y)\n\ndata = ['ACME', 50, 91.1, (2012, 12, 21)]\n\nname, shares, price, date = data\nprint(name, shares, price, date) \n\nname, shares, price, (year, month, day) =data\nprint (name, shares, price, year, month, day)\n\n# 字符串\ns = 'Hello'\na, b, c, d, e = s\nprint(a, b, c, d, e)\n\n# 丢弃一些值 使用'_' 作为变量名\n_, shares, price, _ = data\nprint(shares, price)\n\n","sub_path":"[第一章]数据结构和算法/1.1将序列分解为���独的变量.py","file_name":"1.1将序列分解为单独的变量.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"293894762","text":"'''\n풀이\nnums 배열을 만들고 숫자 개수를 센다. 9는 6에 카운팅 하고 나중에 2로 나눠준다.\n가장 큰 max값이 set수 이다\n'''\nnums=[0]*10\nfor i in input():\n    i=int(i)\n    if i == 9:\n        nums[6]+=1\n    else:\n        nums[i] +=1\n\nnums[6] = (nums[6]+1)//2\n\nprint(max(nums))\n\n","sub_path":"백준/Python/알고파/슬라이딩윈도우,투포인터,누적합/1475(방 번호).py","file_name":"1475(방 번호).py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"54620346","text":"import numpy as np\r\n\r\nclass Robot:\r\n    def __init__(self, wheels_width, wheels_scale, camera_matrix, camera_dist):\r\n        # State is a vector of [x,y,theta]' (3x1 column vector)\r\n        self.state = np.zeros((3,1))\r\n        \r\n        # Wheel parameters\r\n        self.wheels_width = wheels_width  # The distance between the left and right wheels\r\n        self.wheels_scale = wheels_scale  # The scaling factor converting ticks/s to m/s\r\n\r\n        # Camera parameters\r\n        self.camera_matrix = camera_matrix  # Matrix of the focal lengths and camera centre\r\n        self.camera_dist = camera_dist  # Distortion coefficients\r\n    \r\n    def drive(self, drive_meas):\r\n        # left_speed and right_speed are the speeds in ticks/s of the left and right wheels.\r\n        # dt is the length of time to drive for\r\n\r\n        # Compute the linear and angular velocity\r\n        linear_velocity, angular_velocity = self.convert_wheel_speeds(drive_meas.left_speed, drive_meas.right_speed)\r\n        \r\n        # Apply the velocities\r\n        dt = drive_meas.dt\r\n        # TODO: compute state (x,y,theta) from linear and angular velocity\r\n        current_theta = self.state[2]\r\n\r\n        if angular_velocity != 0:\r\n            # Radius of curvature\r\n            R = linear_velocity / angular_velocity\r\n            predict_theta = current_theta + angular_velocity * dt\r\n            self.state[0] = self.state[0] + R*(-np.sin(current_theta) + np.sin(predict_theta))\r\n            self.state[1] = self.state[1] + R*(np.cos(current_theta) - np.cos(predict_theta))\r\n            self.state[2] = predict_theta\r\n        else:\r\n            self.state[0] = self.state[0] + linear_velocity * dt * np.cos(self.state[2])\r\n            self.state[1] = self.state[1] + linear_velocity * dt * np.sin(self.state[2])\r\n            self.state[2] = self.state[2]\r\n\r\n\r\n    def measure(self, markers, idx_list):\r\n        # Markers are 2d landmarks in a 2xn structure where there are n landmarks.\r\n        # The index list tells the function which landmarks to measure in order.\r\n        \r\n        # Construct a 2x2 rotation matrix from the robot angle\r\n        th = self.state[2]\r\n        Rot_theta = np.block([[np.cos(th), -np.sin(th)],[np.sin(th), np.cos(th)]])\r\n        robot_xy = self.state[0:2,:]\r\n\r\n        measurements = []\r\n        for idx in idx_list:\r\n            marker = markers[:,idx:idx+1]\r\n            marker_bff = Rot_theta.T @ (marker - robot_xy)\r\n            measurements.append(marker_bff)\r\n\r\n        # Stack the measurements in a 2xm structure.\r\n        markers_bff = np.concatenate(measurements, axis=1)\r\n        return markers_bff\r\n    \r\n    def convert_wheel_speeds(self, left_speed, right_speed):\r\n        # Convert to m/s\r\n        left_speed_m = left_speed * self.wheels_scale\r\n        right_speed_m = right_speed * self.wheels_scale\r\n\r\n        # Compute the linear and angular velocity\r\n        linear_velocity = (left_speed_m + right_speed_m) / 2.0\r\n        angular_velocity = (right_speed_m - left_speed_m) / self.wheels_width\r\n        \r\n        return linear_velocity, angular_velocity\r\n\r\n    # Derivatives and Covariance\r\n    # --------------------------\r\n\r\n    def derivative_drive(self, drive_meas):\r\n        # Compute the differential of drive w.r.t. the robot state\r\n        DFx = np.zeros((3,3))\r\n        DFx[0,0] = 1\r\n        DFx[1,1] = 1\r\n        DFx[2,2] = 1\r\n\r\n        lin_vel, ang_vel = self.convert_wheel_speeds(drive_meas.left_speed, drive_meas.right_speed)\r\n\r\n        dt = drive_meas.dt\r\n        th = self.state[2]\r\n        if ang_vel == 0:\r\n            DFx[0,2] = -np.sin(th) * lin_vel * dt\r\n            DFx[1,2] = np.cos(th) * lin_vel * dt\r\n        else:\r\n            DFx[0,2] = lin_vel / ang_vel * (np.cos(th+dt*ang_vel) - np.cos(th))\r\n            DFx[1,2] = -lin_vel / ang_vel * (-np.sin(th+dt*ang_vel) + np.sin(th))\r\n\r\n        return DFx\r\n\r\n    def derivative_measure(self, markers, idx_list):\r\n        # Compute the derivative of the markers in the order given by idx_list w.r.t. robot and markers\r\n        n = 2*len(idx_list)\r\n        m = 3 + 2*markers.shape[1]\r\n\r\n        DH = np.zeros((n,m))\r\n\r\n        robot_xy = self.state[0:2,:]\r\n        th = self.state[2]        \r\n        Rot_theta = np.block([[np.cos(th), -np.sin(th)],[np.sin(th), np.cos(th)]])\r\n        DRot_theta = np.block([[-np.sin(th), -np.cos(th)],[np.cos(th), -np.sin(th)]])\r\n\r\n        for i in range(n//2):\r\n            j = idx_list[i]\r\n            # i identifies which measurement to differentiate.\r\n            # j identifies the marker that i corresponds to.\r\n\r\n            lmj_inertial = markers[:,j:j+1]\r\n            # lmj_bff = Rot_theta.T @ (lmj_inertial - robot_xy)\r\n\r\n            # robot xy DH\r\n            DH[2*i:2*i+2,0:2] = - Rot_theta.T\r\n            # robot theta DH\r\n            DH[2*i:2*i+2, 2:3] = DRot_theta.T @ (lmj_inertial - robot_xy)\r\n            # lm xy DH\r\n            DH[2*i:2*i+2, 3+2*j:3+2*j+2] = Rot_theta.T\r\n\r\n            # print(DH[i:i+2,:])\r\n\r\n        return DH\r\n    \r\n    def covariance_drive(self, drive_meas):\r\n        # Derivative of lin_vel, ang_vel w.r.t. left_speed, right_speed\r\n        Jac1 = np.array([[self.wheels_scale/2, self.wheels_scale/2],\r\n                [-self.wheels_scale/self.wheels_width, self.wheels_scale/self.wheels_width]])\r\n        \r\n        lin_vel, ang_vel = self.convert_wheel_speeds(drive_meas.left_speed, drive_meas.right_speed)\r\n        th = self.state[2]\r\n        dt = drive_meas.dt\r\n        th2 = th + dt*ang_vel\r\n\r\n        # Derivative of x,y,theta w.r.t. lin_vel, ang_vel\r\n        Jac2 = np.zeros((3,2))\r\n        if ang_vel == 0:\r\n            Jac2[0,0] = np.cos(th)\r\n            Jac2[1,0] = np.sin(th)\r\n        else:\r\n            Jac2[0,0] = 1/ang_vel * (np.sin(th2) - np.sin(th))\r\n            Jac2[0,1] = -1/(ang_vel**2) * (np.sin(th2) - np.sin(th)) + \\\r\n                            lin_vel / ang_vel * (dt * np.cos(th2))\r\n\r\n            Jac2[1,0] = -1/ang_vel * (np.cos(th2) - np.cos(th))\r\n            Jac2[1,1] = 1/(ang_vel**2) * (np.cos(th2) - np.cos(th)) + \\\r\n                            -lin_vel / ang_vel * (-dt * np.sin(th2))\r\n        Jac2[2,1] = dt\r\n\r\n        # Derivative of x,y,theta w.r.t. left_speed, right_speed\r\n        Jac = Jac2 @ Jac1\r\n\r\n        # Compute covariance\r\n        cov = np.diag((drive_meas.left_cov, drive_meas.right_cov))\r\n        cov = Jac @ cov @ Jac.T\r\n        \r\n        return cov\r\n","sub_path":"Week03-05/slam/Robot.py","file_name":"Robot.py","file_ext":"py","file_size_in_byte":6368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"171164886","text":"\n# This escapement will consist of a main escapement wheel which is a tru involute gear.\n# there will be four planetary gears of equal size the mesh into each other.\n# The plnets are oscillating and only need to have teeth where they touch each other and one single\n# tooth towards the main gear.\n# while oscillating, the planetary gears pass the main gear from one to the next\n\n\n# other idea. Three pendulums of equal lenght take over contol of the escapement gear one by one.\n# they only have one tooth which is driven by the escapement only on the forward stroke.\n# nothing else holds the esacpement wheel. The pendulums are 60 degree out of phase. \n# Nothing else sunchronises the pendulums.\n# \n\n# The Cat! Two grasshopper escapements running on the same wheel with 90 degrees phase shift.\n# each leg passes the wheel to the next leg at the end of the central 90 degrees when it gets\n# caught up by the other pendulum. Only one leg contacts the wheel at any time. unless the contact is passed on.\n# make all legs coplanar so that the total escapement can be see galloping along a big wheel.\n# Two escapements mean two pendulums which are not cupled except through the escapement.\n# may need a \"maintian power\" since it is a grasshopper really.\n\n# Three pendulum single leg escapement.\nfrom Geometry import Point, PointFromDistToOther,IntersectionCircleLine, Circumcenter,IntersectionLineLine\nfrom drawcommands import Circle\nfrom Construction import Hole\nfrom math import cos, sin, acos, asin, atan, pi, sqrt, tan,fabs\nimport math\nfrom Config import config\nimport PS_commands as PS\ntwopi = 2. * pi\ndeg = pi / 180.\n\n\n# Calculating the dimensions of a grasshopper escapement leg:\n#=============================================================\n# Dimensioning of the leg\n#\n#   Parameters:\n#       r - Radius of the escapement wheel\n#       N - number of teeth on the escapement wheel\n#       thetaC - the wheel angle at which the leg-wheel contanct begins\n#       alphaA - the pendulum amplitude\n#       yP - the height of the grasshopper pivot from the wheel centre\n#       gamma - angle arm to pendulum\n\ndefault_r = 100.0\ndefault_yP = 120.\ndefault_N=24.\ndefault_thetaC = 60 * deg\ndefault_alphaA = 5 * deg\ndefault_gamma = 90 * deg\n\nreturn_time = 0.1\n\nverbose = True\n\n#   Derived Constants:\n#       phi - the phase of the pendulum in its sinusoidal motion\n#       alpha - the pendulum angle\n#       theta - the rotation of the escapement wheel.\n#       alphaC - the pendulum angle at which the contact begins\n#       alphaD - the pendulum angle at which the contact ends, will be -alphaC in a symmetric case.\n#       phiC, phiD - the phase at wich the contact begins, ends\n#       l1 - length of fixed arm\n#       l2 - length of moving arm\n#       thetaD - the disconnecting point of the escapement wheel\n\n\nclass SingleLegGrasshopper(object):\n    \"\"\"A grasshopper escapement with a single leg\"\"\"\n    def __init__(self, pos, angle=0, r_escape=default_r, hinge_height=default_yP, N_teeth=default_N, theta_C=default_thetaC, pend_amplitude=default_alphaA, gamma=default_gamma, phase=0, phi_C = twopi/3):\n        super(SingleLegGrasshopper, self).__init__()\n        phase%=twopi\n        phi_C%=twopi\n        self.pos = pos\n        self.angle = angle\n        self.r_escape = r_escape\n        self.hinge_height = hinge_height\n        self.N_teeth = N_teeth\n        self.theta_C = theta_C\n        self.pend_amplitude = pend_amplitude\n        self.gamma = gamma\n        self.phase = -phase\n        self.phi = 0\n        self.phi_C = phi_C\n        self.marking_radius = 2\n        self.SetDerivedConstants()\n        \n    \n    def SetDerivedConstants(self):\n        self.theta_step = twopi/(3*self.N_teeth)\n        self.theta_D = self.theta_C-self.theta_step\n        self.phi_D = self.phi_C+twopi/3\n        cos_C = cos(self.gamma-self.alpha(self.phi_C)-self.beta(self.theta_C))\n        cos_D = cos(self.gamma-self.alpha(self.phi_D)-self.beta(self.theta_D))\n        self.l_arm = ( (self.distance_d(self.theta_D))**2 - (self.distance_d(self.theta_C))**2 )/(2*((self.distance_d(self.theta_D))*cos_D - (self.distance_d(self.theta_C))*cos_C))\n        self.l_coupling = sqrt(self.l_arm**2 + (self.distance_d(self.theta_C))**2 - 2*self.l_arm*self.distance_d(self.theta_C)*cos_C)\n        \n        self.hinge_point = Point(0,self.hinge_height)\n        # The connection radius:\n        self.tooth_centre_C = Point(0,self.r_escape).Rotate(self.theta_C)\n        self.tooth_centre_D = Point(0,self.r_escape).Rotate(self.theta_D)\n        \n        #The upper arm:\n        self.upper_arm_final_C = self.hinge_point+Point(0,-self.l_arm).Rotate(-self.gamma+self.alpha(self.phi_C))\n        self.upper_arm_final_D = self.hinge_point+Point(0,-self.l_arm).Rotate(-self.gamma+self.alpha(self.phi_D))\n        \n        self.coupling_angle=(self.tooth_centre_C-self.upper_arm_final_C).Angle()-self.alpha(self.phi_C)\n            \n    \n    def alpha(self,phi):\n        return self.pend_amplitude * sin(phi)\n    \n    def distance_d(self,theta):\n        return sqrt(self.hinge_height**2 + self.r_escape**2 - 2*self.hinge_height*self.r_escape*cos(theta))\n    \n    def beta(self,theta):\n        return asin((self.r_escape*sin(theta))/self.distance_d(theta))\n    \n    def PointIntToFinal(self, point):\n        return point.Rotate(self.angle)+self.pos\n    \n    def Toothpos(self, phi, n=0):\n        #for the movement of the tooth it only matters which part of the contact phase it is\n        # plus if it currenly is in contact with the pendulum before us, after us, or with us\n        phi_any=(phi-self.phi_C)%(twopi/3) + self.phi_C\n        offset_angle = self.theta_step * (math.floor( ((phi-self.phi_C)%twopi)/(twopi/3) ) )%3\n        upper_arm_final = self.hinge_point+Point(0,-self.l_arm).Rotate(-self.gamma+self.alpha(phi_any))\n        tooth_pos = PointFromDistToOther(upper_arm_final,self.l_coupling,Point(0,0),self.r_escape,choice=2)\n        return tooth_pos.Rotate(-offset_angle)\n        # return tooth_pos\n        \n    \n    def Draw(self,phi=0):\n        phi -= self.phase\n        phi%=twopi\n        outpath=\"\"\n        # The escapement wheel radius\n        outpath+=\"gsave\\n\"\n        outpath+=\"%f %f %f setrgbcolor\\n\"%(0.8,.8,.8)\n        # outpath+=Circle(self.pos,self.r_escape)\n        outpath+=Circle(self.pos,self.marking_radius)\n        outpath+=\"grestore\\n\"\n        # outpath+=PS.CuttingStroke()\n        \n        pendulum = Pendulum(self.PointIntToFinal(self.hinge_point),self.r_escape*2, self.pend_amplitude)\n        wheel = PinWheel(self.pos,self.Toothpos(phi).Angle()+self.angle+pi/2,self.r_escape,self.N_teeth, 1.)\n        \n        outpath += pendulum.Draw(phi)\n        outpath += wheel.Draw()\n        \n        \n        #upper arm doesn't care about swing or free:\n        upper_arm_final = self.hinge_point+Point(0,-self.l_arm).Rotate(-self.gamma+self.alpha(phi))\n        outpath+=Beam(self.PointIntToFinal(self.hinge_point),self.PointIntToFinal(upper_arm_final))\n        \n        if self.phi_C < phi and phi < self.phi_D:\n            # Draw the power phase of the movement\n            # the coupling\n            outpath+=Beam(self.PointIntToFinal(upper_arm_final),self.PointIntToFinal(self.Toothpos(phi)))\n        else:\n            # if phi < self.phi_D+return_time:\n            #     # The snap back phase:\n            #     #Draw the free swing phase\n            #     # the coupling\n            #     coupling_final = upper_arm_final+Point(self.l_coupling,0).Rotate(self.coupling_angle+self.alpha(phi))\n            #     outpath+=Beam(self.PointIntToFinal(upper_arm_final),self.PointIntToFinal(coupling_final))\n            # else:\n            #Draw the free swing phase\n            # the coupling\n            coupling_final = upper_arm_final+Point(self.l_coupling,0).Rotate(self.coupling_angle+self.alpha(phi))\n            outpath+=Beam(self.PointIntToFinal(upper_arm_final),self.PointIntToFinal(coupling_final))\n        \n        \n        #angle between coupling and arm\n        # \n        # outpath+=\"%f %f %f setrgbcolor\\n\"%(0.1,0.1,0)\n        # \n        # outpath+=DrawExtendedCoupling(twopi)\n        # outpath+=DrawExtendedCoupling(3*pi/2)\n        # outpath+=DrawExtendedCoupling(phiD)\n        \n        # the coupling end movement:\n        # arc=PS.Path(closed=1)\n        # # The large path from up to down\n        # arc+=PS.Arc(hinge_point,d_C*self.r,3*pi/2-(beta_C+alphaC-alpha(3*pi/2)),3*pi/2-(beta_C+alphaC-alpha(pi/2)))\n        # # back up to the interaction point\n        # arc+=PS.ArcN(hinge_point,d_C*self.r,3*pi/2-(beta_C+alphaC-alpha(pi/2)),3*pi/2-(beta_C+alphaC-alpha(phiC)))\n        # # The pushing phase\n        # arc+=PS.ArcN(self.pos,self.r,thetaC+pi/2,thetaD+pi/2)\n        # # The retreta\n        # arc+=PS.ArcN(upper_arm_final_D,l2*self.r,(tooth_D-upper_arm_final_D).Angle(),coupling_angle+alpha(phiD))\n        # # The last part of the upwash\n        # arc+=PS.ArcN(hinge_point,d_C*self.r,\n        #     (upper_arm_final_D+Point(l2*self.r,0).Rotate(coupling_angle+alpha(phiD))-hinge_point).Angle(),\n        # 3*pi/2-(beta_C+alphaC-alpha(3*pi/2)))\n        # # print arc.Draw()\n        # outpath+=arc.Draw()\n        # outpath+=PS.CuttingStroke()\n        \n        \n        # #Test\n        # outpath+=\"%f %f %f setrgbcolor\\n\"%(0.5,0.5,0)\n        # outpath+=Beam(hinge_point,hinge_point+Point(50,0).Rotate(0))\n        # outpath+=Beam(hinge_point,hinge_point+Point(50,0).Rotate(10*deg))\n        # outpath+=Beam(hinge_point,hinge_point+Point(50,0).Rotate(20*deg))\n        \n        return outpath\n    \n\n\n\nEscapement=SingleLegGrasshopper\n# class CatEscapement(object):\n#     \"\"\"docstring for CatEscapement\"\"\"\n#     def __init__(self, pos, r_escape=default_r, hinge_height=default_yP, N_teeth=default_N):\n#         super(CatEscapement, self).__init__()\n#         self.pos=pos\n#         self.r_escape=r_escape\n#         self.hinge_height=hinge_height\n#         self.N_teeth=N_teeth\n#     \n#     def Draw(self,phi):\n#         wheel = PinWheel()\n        \n\ndef Beam(pos1,pos2):\n    path = PS.Path(closed=0)\n    path += PS.MoveTo(pos1)\n    path += PS.LineTo(pos2)\n    return path.Draw()\n\n    \nclass Pendulum(object):\n    \"\"\"A Pendulum\"\"\"\n    def __init__(self, pos, length, amplitude, phase=0):\n        super(Pendulum, self).__init__()\n        self.pos = pos\n        self.length = length\n        self.amplitude = amplitude\n        self.phase = phase\n    \n    def Draw(self, phi=0):\n        outpath = \"\"\n        outpath+=Circle(self.pos, config.axle_r)\n        outpath+=Beam(self.pos,self.pos+Point(0,-self.length).Rotate(self.amplitude*sin(self.phase+phi)))\n        \n        return outpath\n\nclass PinWheel(object):\n    \"\"\"Escapement wheel\"\"\"\n    def __init__(self, pos, angle=0, radius=50, n_teeth=30, tooth_r=0.5):\n        super(PinWheel, self).__init__()\n        self.pos = pos\n        self.angle = angle\n        self.radius = radius\n        self.n_teeth = n_teeth\n        self.tooth_r = tooth_r\n        self.cutout=None\n        self.clearance = config.escapement_clearance\n    \n    def SetCutOut(self, cutout):\n        self.cutout = cutout\n        self.cutout.parent = self\n    \n    def Draw(self):\n        # prepare:\n        self.hole = Hole(self.pos)\n        class dummy:\n            pass\n        self.surface=dummy()\n        self.surface.r_root = self.radius-self.tooth_r\n        r_out = self.radius+self.tooth_r + config.material\n        self.outring = Circle(self.pos, r_out)\n        \n        # draw:\n        out_str=\"% Escapement PinWheel\\n\"\n        # out_str+=self.hole.Draw()\n        # if self.cutout is not None:\n        #     out_str+=self.cutout.Draw()\n        # out_str+=self.outring\n        # and now draw the individual teeth:\n        theta_step = 2*pi/self.n_teeth\n        for i in range(int(self.n_teeth)):\n            curr_angle = self.angle + i*theta_step\n            hole_pos = self.pos + Point(self.radius,0).Rotate(curr_angle)\n            out_str+=Circle(self.pos + Point(self.radius,0).Rotate(curr_angle),self.tooth_r+config.tool_r)\n            # out_str+=Beam(hole_pos,hole_pos+Point(3,0))\n        \n        return out_str\n\n","sub_path":"involute_gears/Clock_mixed/FullGrasshopper.py","file_name":"FullGrasshopper.py","file_ext":"py","file_size_in_byte":12030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"638416171","text":"'''\nThis script handling the training process.\n'''\n\nimport argparse\nimport math\nimport time\n\nfrom tqdm import tqdm\nimport torch\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.utils.data\nimport transformer.Constants as Constants\nfrom dataset import TranslationDataset, paired_collate_fn\nfrom transformer.Models import Transformer\nfrom transformer.Optim import ScheduledOptim\nimport os\nimport json\n\nimport sentencepiece as spm\n\nfrom transformer.Translator import Translator\n\nsp = spm.SentencePieceProcessor()\n\n########################################\n# Warm restart code credits: Ankur Garg#\n# First four functions in this code   #\n########################################\ndef check_restart_conditions(opt):\n    # Check for the status file corresponding to the model\n    status_file = os.path.join(opt.save_model_dir, 'status.json')\n    if os.path.exists(status_file):\n        with open(status_file, 'r') as f:\n            status = json.load(f)\n        opt.resume_from_epoch = status['epoch']\n    else:\n        opt.resume_from_epoch = 0\n    return opt\n\ndef write_status(opt, epoch):\n    status_file = os.path.join(opt.save_model_dir, 'status.json')\n    status = {\n        'epoch': epoch,\n    }\n    with open(status_file, 'w') as f:\n        json.dump(status, f, indent=4)\n\ndef load_models(model, opt, epoch):\n    checkpoint = torch.load(os.path.join(opt.save_model_dir, 'epoch_{0:02d}.chkpt'.format(epoch)))\n    model.load_state_dict(checkpoint['model'])\n    return model\n\ndef save_params(opt):\n    if not os.path.exists(os.path.join(opt.save_model_dir)):\n        os.makedirs(os.path.join(opt.save_model_dir))\n\n    with open(os.path.join(opt.save_model_dir, 'params.json'), 'w') as f:\n        json.dump(vars(opt), f, indent=4)\n\ndef cal_performance(pred, gold, smoothing=False):\n    ''' Apply label smoothing if needed '''\n\n    loss = cal_loss(pred, gold, smoothing)\n\n    pred = pred.max(1)[1]\n    gold = gold.contiguous().view(-1)\n    non_pad_mask = gold.ne(Constants.PAD)\n    n_correct = pred.eq(gold)\n    n_correct = n_correct.masked_select(non_pad_mask).sum().item()\n\n    return loss, n_correct\n\ndef print_preds(pred, batch_size, outfile):\n    pred = pred.view(batch_size,-1,pred.size(1))\n    pred = pred.max(2)[1]\n\n    for b in range(batch_size):\n        sent = pred[b,:]\n        indices = []\n        for token in sent:\n            if token != Constants.EOS:\n                indices.append(token.item())\n            else:\n                break\n        out = sp.DecodeIds(indices)\n        outfile.write(out + '\\n')\n\ndef cal_loss(pred, gold, smoothing):\n    ''' Calculate cross entropy loss, apply label smoothing if needed. '''\n\n    gold = gold.contiguous().view(-1)\n\n    if smoothing:\n        eps = 0.1\n        n_class = pred.size(1)\n\n        one_hot = torch.zeros_like(pred).scatter(1, gold.view(-1, 1), 1)\n        one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1)\n        log_prb = F.log_softmax(pred, dim=1)\n\n        non_pad_mask = gold.ne(Constants.PAD)\n        loss = -(one_hot * log_prb).sum(dim=1)\n        loss = loss.masked_select(non_pad_mask).sum()  # average later\n    else:\n        loss = F.cross_entropy(pred, gold, ignore_index=Constants.PAD, reduction='sum')\n\n    return loss\n\n\ndef train_epoch(model, training_data, optimizer, device, smoothing, loss_weight = 1):\n    ''' Epoch operation in training phase'''\n\n    model.train()\n\n    total_loss = 0\n    n_word_total = 0\n    n_word_correct = 0\n\n    for batch in tqdm(\n            training_data, mininterval=2,\n            desc='  - (Training)   ', leave=False):\n\n        # prepare data\n        src_seq, src_pos, tgt_seq, tgt_pos = map(lambda x: x.to(device), batch)\n        gold = tgt_seq[:, 1:]\n\n        # forward\n        optimizer.zero_grad()\n        #w = input(\"s\")\n        pred = model(src_seq, src_pos, tgt_seq, tgt_pos)\n\n        # backward\n        loss, n_correct = cal_performance(pred, gold, smoothing=smoothing)\n\n        loss *= loss_weight\n\n        loss.backward()\n\n        # update parameters\n        optimizer.step_and_update_lr()\n\n        # note keeping\n        total_loss += loss.item()\n\n        non_pad_mask = gold.ne(Constants.PAD)\n        n_word = non_pad_mask.sum().item()\n        n_word_total += n_word\n        n_word_correct += n_correct\n        # break\n\n    loss_per_word = total_loss/n_word_total\n    accuracy = n_word_correct/n_word_total\n    return loss_per_word, accuracy\n\ndef eval_epoch(model, validation_data, device):\n    ''' Epoch operation in evaluation phase '''\n\n    model.eval()\n\n    total_loss = 0\n    n_word_total = 0\n    n_word_correct = 0\n\n    with torch.no_grad():\n        for batch in tqdm(\n                validation_data, mininterval=2,\n                desc='  - (Validation) ', leave=False):\n\n            # prepare data\n            src_seq, src_pos, tgt_seq, tgt_pos = map(lambda x: x.to(device), batch)\n            gold = tgt_seq[:, 1:]\n\n            # forward\n            #print(src_seq,src_pos,tgt_seq,tgt_seq)\n            batch_size = src_seq.size(0)\n\n            pred = model(src_seq, src_pos, tgt_seq, tgt_pos)\n            \n            loss, n_correct = cal_performance(pred, gold, smoothing=False)\n\n            # note keeping\n            total_loss += loss.item()\n\n            non_pad_mask = gold.ne(Constants.PAD)\n            n_word = non_pad_mask.sum().item()\n            n_word_total += n_word\n            n_word_correct += n_correct\n\n    loss_per_word = total_loss/n_word_total\n    accuracy = n_word_correct/n_word_total\n    return loss_per_word, accuracy\n\ndef eval_bleu_score(opt, model, data, device, epoch, split = 'dev'):\n    translator = Translator(opt, model, load_from_file = False)\n    hyp_file = os.path.join(opt.save_model_dir, 'mypreds' + str(epoch) + '.hyp')\n    outfile = open(hyp_file, 'w')\n    for batch in tqdm(data, mininterval=2, desc='  - (Test)', leave=False):\n        src_seq, src_pos, tgt_seq, tgt_pos = map(lambda x: x.to(device), batch)\n        all_hyp, all_scores = translator.translate_batch(src_seq, src_pos)\n        for idx_seqs in all_hyp:\n            for idx_seq in idx_seqs:\n                pred = idx_seq\n                out = sp.DecodeIds(pred)\n                outfile.write(out + '\\n')\n    outfile.close()\n    os.system(\"sh calcBLEU.sh \" + split + \" \" + opt.save_model_dir + \" \" + str(epoch))\n\ndef train(model, training_data, validation_data, test_data, mined_data, optimizer, device, opt):\n    ''' Start training '''\n\n    log_train_file = None\n    log_valid_file = None\n\n    if opt.log:\n        log_train_file = os.path.join(opt.save_model_dir, 'train.log')\n        log_valid_file = os.path.join(opt.save_model_dir, 'valid.log')\n\n        print('[Info] Training performance will be written to file: {} and {}'.format(\n            log_train_file, log_valid_file))\n\n        with open(log_train_file, 'w') as log_tf, open(log_valid_file, 'w') as log_vf:\n            log_tf.write('epoch,loss,ppl,accuracy\\n')\n            log_vf.write('epoch,loss,ppl,accuracy\\n')\n\n    valid_accus = []\n    for epoch_i in range(opt.resume_from_epoch, opt.resume_from_epoch + opt.epoch):\n        print('[ Epoch', epoch_i, ']')\n\n        start = time.time()\n        train_loss, train_accu = train_epoch(\n            model, training_data, optimizer, device, smoothing=opt.label_smoothing)\n        print('  - (Train)   loss: {loss: 8.5f}, accuracy: {accu:3.3f} %, '\\\n              'elapse: {elapse:3.3f} min'.format(loss=train_loss,\n                   accu=100*train_accu,\n                  elapse=(time.time()-start)/60))\n\n        if opt.loss_weight > 0:\n            mined_loss, mined_accu = train_epoch(\n                model, mined_data, optimizer, device, smoothing=opt.label_smoothing, loss_weight = opt.loss_weight)\n            print('  - (Mined)   loss: {loss: 8.5f}, accuracy: {accu:3.3f} %, '\\\n                  'elapse: {elapse:3.3f} min'.format(loss=mined_loss,\n                      accu=100*mined_accu,\n                      elapse=(time.time()-start)/60))\n\n        start = time.time()\n        valid_loss, valid_accu = eval_epoch(model, validation_data, device)\n        print('  - (Valid)   loss: {loss: 8.5f}, accuracy: {accu:3.3f} %, '\\\n                'elapse: {elapse:3.3f} min'.format(loss=valid_loss,\n                    accu=100*valid_accu,\n                    elapse=(time.time()-start)/60))\n\n        valid_accus += [valid_accu]\n\n\n        test_loss, test_accu = eval_epoch(model, test_data, device)\n        print('  - (Test )   loss: {loss: 8.5f}, accuracy: {accu:3.3f} %, '\\\n                'elapse: {elapse:3.3f} min'.format(loss=test_loss,\n                    accu=100*test_accu,\n                    elapse=(time.time()-start)/60))\n\n\n        if (epoch_i+1)%(opt.test_epoch) == 0:\n            eval_bleu_score(opt, model, test_data, device, epoch_i, split = 'test')\n\n        model_state_dict = model.state_dict()\n        checkpoint = {\n            'model': model_state_dict,\n            'settings': opt,\n            'epoch': epoch_i\n        }\n\n        if opt.save_model_dir:\n            if opt.save_mode == 'all':\n                model_name = os.path.join(opt.save_model_dir, 'epoch_{0:02d}.chkpt'.format(epoch_i))\n                torch.save(checkpoint, model_name)\n            elif opt.save_mode == 'best':\n                model_name = os.path.join(opt.save_model_dir, 'epoch_{0:02d}.chkpt'.format(epoch_i))\n                if valid_accu >= max(valid_accus):\n                    torch.save(checkpoint, model_name)\n                    print('    - [Info] The checkpoint file has been updated.')\n\n        if log_train_file and log_valid_file:\n            with open(log_train_file, 'a') as log_tf, open(log_valid_file, 'a') as log_vf:\n                log_tf.write('{epoch},{loss: 8.5f},{ppl: 8.5f},{accu:3.3f}\\n'.format(\n                    epoch=epoch_i, loss=train_loss,\n                    ppl=math.exp(min(train_loss, 100)), accu=100*train_accu))\n                log_vf.write('{epoch},{loss: 8.5f},{ppl: 8.5f},{accu:3.3f}\\n'.format(\n                    epoch=epoch_i, loss=valid_loss,\n                    ppl=math.exp(min(valid_loss, 100)), accu=100*valid_accu))\n\n        write_status(opt, epoch_i)\n\ndef main():\n    ''' Main function '''\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('-data', required=True)\n    parser.add_argument('-mined_data', required=True)\n    parser.add_argument('-snippet_model', required=True)\n\n    parser.add_argument('-epoch', type=int, default=10)\n    parser.add_argument('-batch_size', type=int, default=64)\n\n    #parser.add_argument('-d_word_vec', type=int, default=512)\n    parser.add_argument('-d_model', type=int, default=512)\n    parser.add_argument('-d_inner_hid', type=int, default=2048)\n    parser.add_argument('-d_k', type=int, default=64)\n    parser.add_argument('-d_v', type=int, default=64)\n\n    parser.add_argument('-n_head', type=int, default=8)\n    parser.add_argument('-n_layers', type=int, default=6)\n    parser.add_argument('-n_warmup_steps', type=int, default=4000)\n\n    parser.add_argument('-dropout', type=float, default=0.1)\n    parser.add_argument('-embs_share_weight', action='store_true')\n    parser.add_argument('-proj_share_weight', action='store_true')\n\n    parser.add_argument('-log', type=bool, default=True)\n    parser.add_argument('-save_model_dir', default=None, required=True)\n    parser.add_argument('-save_mode', type=str, choices=['all', 'best'], default='all')\n\n    parser.add_argument('-no_cuda', action='store_true')\n    parser.add_argument('-label_smoothing', action='store_true')\n\n    # For bleu eval\n    parser.add_argument('-beam_size', type=int, default=5, help='Beam size')\n    parser.add_argument('-n_best', type=int, default=1,\n                        help=\"\"\"If verbose is set, will output the n_best\n                        decoded sentences\"\"\")\n\n    parser.add_argument('-test_epoch', type=int, default=5, help='Test every x epochs')\n    parser.add_argument('-resume_from_epoch', type=int, default=0, help='Warm restart')\n\n    # Not really needed\n    parser.add_argument('-alpha', type=float,default=1.0, help='Weighting loss')\n    parser.add_argument('-loss_weight', type=float,default=0.1, help='Mined loss weight')\n    parser.add_argument('-lr', type=float,default=1e-3, help='Learning rate')\n\n    opt = parser.parse_args()\n    opt.cuda = not opt.no_cuda\n    opt.d_word_vec = opt.d_model\n\n    # Snippet model sentencepiece\n    sp.Load(opt.snippet_model)\n\n    #========= Loading Dataset =========#\n    data = torch.load(opt.data)\n    mined_data = torch.load(opt.mined_data)\n\n    opt.inp_seq_max_len = 4*data['settings'].train_max_input_len\n    opt.out_seq_max_len = 4*data['settings'].train_max_output_len\n    \n    opt.max_token_seq_len = int(opt.out_seq_max_len/4)\n\n    training_data, validation_data, test_data, mined_data = prepare_dataloaders(data, mined_data, opt)\n\n    opt.src_vocab_size = training_data.dataset.src_vocab_size\n    opt.tgt_vocab_size = training_data.dataset.tgt_vocab_size\n\n    print(opt.inp_seq_max_len,opt.out_seq_max_len,opt.src_vocab_size,opt.tgt_vocab_size)\n\n    #========= Preparing Model =========#\n    if opt.embs_share_weight:\n        assert training_data.dataset.src_word2idx == training_data.dataset.tgt_word2idx, \\\n            'The src/tgt word2idx table are different but asked to share word embedding.'\n\n    print(opt)\n\n    device = torch.device('cuda' if opt.cuda else 'cpu')\n    transformer = Transformer(\n        opt.src_vocab_size,\n        opt.tgt_vocab_size,\n        opt.inp_seq_max_len,\n        opt.out_seq_max_len,\n        tgt_emb_prj_weight_sharing=opt.proj_share_weight,\n        emb_src_tgt_weight_sharing=opt.embs_share_weight,\n        d_k=opt.d_k,\n        d_v=opt.d_v,\n        d_model=opt.d_model,\n        d_word_vec=opt.d_word_vec,\n        d_inner=opt.d_inner_hid,\n        n_layers=opt.n_layers,\n        n_head=opt.n_head,\n        dropout=opt.dropout).to(device)\n\n    optimizer = ScheduledOptim(\n        optim.Adam(\n            filter(lambda x: x.requires_grad, transformer.parameters()),\n            betas=(0.9, 0.98), eps=1e-09, lr = opt.lr),\n        opt.d_model, opt.n_warmup_steps)\n\n    save_params(opt)\n\n    opt = check_restart_conditions(opt)\n    if opt.resume_from_epoch >= 1:\n        print('Loading Old model')\n        print('Loading model files from folder: %s' % opt.save_model_dir)\n        transformer = load_models(transformer, opt, opt.resume_from_epoch)\n\n    train(transformer, training_data, validation_data, test_data, mined_data, optimizer, device, opt)\n\ndef prepare_dataloaders(data, mined_data, opt):\n    # ========= Preparing DataLoader =========#\n    train_loader = torch.utils.data.DataLoader(\n        TranslationDataset(\n            src_word2idx=data['dict']['src'],\n            tgt_word2idx=data['dict']['tgt'],\n            src_insts=data['train']['src'],\n            tgt_insts=data['train']['tgt']),\n        num_workers=2,\n        batch_size=opt.batch_size,\n        collate_fn=paired_collate_fn,\n        shuffle=True)\n\n    valid_loader = torch.utils.data.DataLoader(\n        TranslationDataset(\n            src_word2idx=data['dict']['src'],\n            tgt_word2idx=data['dict']['tgt'],\n            src_insts=data['valid']['src'],\n            tgt_insts=data['valid']['tgt']),\n        num_workers=2,\n        batch_size=opt.batch_size,\n        collate_fn=paired_collate_fn)\n\n    test_loader = torch.utils.data.DataLoader(\n        TranslationDataset(\n            src_word2idx=data['dict']['src'],\n            tgt_word2idx=data['dict']['tgt'],\n            src_insts=data['test']['src'],\n            tgt_insts=data['test']['tgt']),\n        num_workers=2,\n        batch_size=opt.batch_size,\n        collate_fn=paired_collate_fn)\n\n    mined_loader = torch.utils.data.DataLoader(\n            TranslationDataset(\n                src_word2idx=mined_data['dict']['src'],\n                tgt_word2idx=mined_data['dict']['tgt'],\n                src_insts=mined_data['train']['src'],\n                tgt_insts=mined_data['train']['tgt']),\n            num_workers=2,\n            batch_size=opt.batch_size,\n            collate_fn=paired_collate_fn)\n\n    return train_loader, valid_loader, test_loader, mined_loader\n\n\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"finetune.py","file_name":"finetune.py","file_ext":"py","file_size_in_byte":16105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"343540636","text":"import logging\nimport time\nfrom datetime import datetime\nfrom typing import Any, Tuple\n\nimport pandas as pd\nimport numpy as np\nfrom dateutil.relativedelta import relativedelta\nfrom PyQt5 import uic\nfrom PyQt5.QtCore import Qt, pyqtSlot\n\nfrom business.model.asset import Asset\nfrom business.model.timeframe import Duration, TimeFrame\nfrom business.modules.asset_bl import AssetBL\nfrom ui.base.base_page import BasePage\nfrom ui.components.candlestick_chart.chart import MyCandlestickChart\nfrom ui.windows.asset_detail.shared.pages.history_chart.helpers import (\n    fillGapsInDays,\n    plotMonths,\n    plotQuarters,\n    plotSeasons,\n    plotUSHolidays,\n    plotWeekends,\n    plotWeeks,\n    plotYears,\n)\n\n# create logger\nlog = logging.getLogger(\"CellarLogger\")\n\n\nclass HistoryChartPage(BasePage):\n    asset: Asset\n    bl: AssetBL\n\n    originData: pd.DataFrame\n    dataDF: pd.DataFrame\n\n    currentRange: Tuple[int, int]\n\n    def __init__(self, **kwargs: Any):\n        super().__init__()\n        log.info(\"Running ...\")\n\n        # load template\n        uic.loadUi(\n            \"ui/windows/asset_detail/shared/pages/history_chart/history_chart.ui\",\n            self,\n        )\n\n        # load styles\n        with open(\n            \"ui/windows/asset_detail/shared/pages/history_chart/history_chart.qss\",\n            \"r\",\n        ) as fh:\n            self.setStyleSheet(fh.read())\n\n        # apply styles\n        self.setAttribute(Qt.WA_StyledBackground, True)\n\n        # INPUT data\n        self.asset: Asset = kwargs[\"asset\"]\n\n        # BL\n        self.bl: AssetBL = AssetBL()\n\n        # DEFAULTS\n        self.currentRange = (0, 0)\n        self.timeframe = TimeFrame.day1\n        self.duration = Duration.year1.value\n\n        # signals\n        self.durationComboBox.currentTextChanged.connect(\n            self.durationComboBoxChanged\n        )\n        self.usHolidaysCheckBox.stateChanged.connect(\n            self.usHolidaysCheckboxChanged\n        )\n        self.weeksCheckbox.stateChanged.connect(self.weeksCheckboxChanged)\n        self.monthsCheckbox.stateChanged.connect(self.monthsCheckboxChanged)\n        self.seasonsCheckbox.stateChanged.connect(self.seasonsCheckboxChanged)\n        self.quartersCheckbox.stateChanged.connect(\n            self.quartersCheckboxChanged\n        )\n        self.yearsCheckbox.stateChanged.connect(self.yearsCheckboxChanged)\n\n        # init chart ----------------------------------------------\n\n        # HIST DATA\n        log.info(\"Running\")\n        start = time.time()\n        self.data = self.__getHistData(self.asset.symbol, self.timeframe)\n        end = time.time()\n        log.info(f\"get data takes: {end - start} sec.\")\n\n        # FILL GAPS IN DATA\n        log.info(\"Running\")\n        start = time.time()\n        if self.data is not None:\n            self.data = fillGapsInDays(self.data, self.timeframe)\n        end = time.time()\n        log.info(f\"enhance data takes: {end - start} sec.\")\n\n        if self.data is not None:\n            self.data[\"id\"] = np.arange(self.data.shape[0])\n\n            # CANDLESTICK\n            start = time.time()\n            self.originData = self.data.copy()\n            self.__plotCandlestickChart(self.data, duration=self.duration)\n            end = time.time()\n            log.info(f\"plotCandlestickChart data takes: {end - start} sec.\")\n\n            # WEEKENDS\n            # start = time.time()\n            # plotWeekends(self.data, self.candlestickChart.candlestickPlot)\n            # end = time.time()\n            # log.info(f\"plotWeekends data takes: {end - start} sec.\")\n\n        self.durationComboBox.setCurrentText(self.duration)\n        # ---------------------------------------------------------\n\n    def __getHistData(self, symbol: str, value: TimeFrame):\n        start = time.time()\n        data = self.bl.getHistoricalDataFromDB(symbol, value)\n        if data is not None:\n            data = data.sort_index()\n\n            # # check duplications\n            # dupl = self.data.duplicated()\n            # allresults = dupl[dupl == True]\n\n        end = time.time()\n        log.info(f\"takes {end - start} sec.\")\n\n        return data\n\n    def __plotCandlestickChart(self, data: pd.DataFrame, **kwargs: Any):\n        range = (0, 0)\n        if \"duration\" in kwargs:\n            range = self.getDuration(kwargs[\"duration\"])\n        elif \"range\" in kwargs:\n            range = kwargs[\"range\"]\n\n        self.candlestickChart = MyCandlestickChart(data, range)\n        self.candlestickChart.on_range_update.connect(self.__updateRange)\n        self.chartBox.addWidget(self.candlestickChart, 0, 0, 0, 0)\n\n    # --------------------------------------------------------\n    # COMBO-BOXES\n    # --------------------------------------------------------\n\n    @pyqtSlot(str)\n    def durationComboBoxChanged(self, value: Duration):\n        log.info(\"Running\")\n        start = time.time()\n\n        self.duration = value\n\n        if self.data is not None:\n            range = self.getDuration(value)\n            self.candlestickChart.overviewPlot.timeRegion.setRegion(range)\n\n        end = time.time()\n        log.info(f\"durationComboBoxChanged data takes: {end - start} sec.\")\n\n    # --------------------------------------------------------\n    # CHECK-BOXES\n    # --------------------------------------------------------\n    @pyqtSlot(int)\n    def usHolidaysCheckboxChanged(self, state: int):\n        if state == Qt.Checked:\n            plotUSHolidays(self.data, self.candlestickChart.candlestickPlot)\n        else:\n            self.__reDrawChart()\n\n    @pyqtSlot(int)\n    def weeksCheckboxChanged(self, state: int):\n        if state == Qt.Checked:\n            plotWeeks(self.data, self.candlestickChart.candlestickPlot)\n        else:\n            self.__reDrawChart()\n\n    @pyqtSlot(int)\n    def monthsCheckboxChanged(self, state: int):\n        if state == Qt.Checked:\n            plotMonths(self.data, self.candlestickChart.candlestickPlot)\n        else:\n            self.__reDrawChart()\n\n    @pyqtSlot(int)\n    def seasonsCheckboxChanged(self, state: int):\n        if state == Qt.Checked:\n            plotSeasons(self.data, self.candlestickChart.candlestickPlot)\n        else:\n            self.__reDrawChart()\n\n    @pyqtSlot(int)\n    def quartersCheckboxChanged(self, state: int):\n        if state == Qt.Checked:\n            plotQuarters(self.data, self.candlestickChart.candlestickPlot)\n        else:\n            self.__reDrawChart()\n\n    @pyqtSlot(int)\n    def yearsCheckboxChanged(self, state: int):\n        if state == Qt.Checked:\n            plotYears(self.data, self.candlestickChart.candlestickPlot)\n        else:\n            self.__reDrawChart()\n\n    # --------------------------------------------------------\n    # --------------------------------------------------------\n    # Helpers\n    # --------------------------------------------------------\n    # --------------------------------------------------------\n\n    def __reDrawChart(self):\n        self.__plotCandlestickChart(self.originData, range=self.currentRange)\n        # plotWeekends(self.originData, self.candlestickChart.candlestickPlot)\n\n        if self.usHolidaysCheckBox.isChecked():\n            plotUSHolidays(\n                self.originData, self.candlestickChart.candlestickPlot\n            )\n\n        if self.weeksCheckbox.isChecked():\n            plotWeeks(self.originData, self.candlestickChart.candlestickPlot)\n\n        if self.monthsCheckbox.isChecked():\n            plotMonths(self.originData, self.candlestickChart.candlestickPlot)\n\n        if self.seasonsCheckbox.isChecked():\n            plotSeasons(self.originData, self.candlestickChart.candlestickPlot)\n\n        if self.quartersCheckbox.isChecked():\n            plotQuarters(\n                self.originData, self.candlestickChart.candlestickPlot\n            )\n\n        if self.yearsCheckbox.isChecked():\n            plotYears(self.originData, self.candlestickChart.candlestickPlot)\n\n    def __updateRange(self, range: Tuple[int, int]):\n        self.currentRange = range\n\n    def getDuration(self, duration: Duration):\n        minVal = datetime.now()\n        maxVal = datetime.now()\n        if duration == Duration.years20.value:\n            minVal = maxVal - relativedelta(years=20)\n        elif duration == Duration.years10.value:\n            minVal = maxVal - relativedelta(years=10)\n        elif duration == Duration.year5.value:\n            minVal = maxVal - relativedelta(years=5)\n        elif duration == Duration.year1.value:\n            minVal = maxVal - relativedelta(years=1)\n        elif duration == Duration.quarter1.value:\n            minVal = maxVal - relativedelta(months=3)\n        elif duration == Duration.month1.value:\n            minVal = maxVal - relativedelta(months=1)\n        elif duration == Duration.week1.value:\n            minVal = maxVal - relativedelta(weeks=1)\n        elif duration == Duration.all.value:\n            minVal = datetime.min\n\n        minIndex = 0\n        maxIndex = self.data.shape[0]\n\n        if minVal != datetime.min:\n            tempDf = self.data[self.data.index > minVal]\n            if tempDf.shape[0] > 0:\n                minIndex = self.data.index.get_loc(tempDf.index[0])\n\n        return (minIndex, maxIndex)\n\n    # --------------------------------------------------------\n    # --------------------------------------------------------\n    # DESTROY\n    # --------------------------------------------------------\n    # --------------------------------------------------------\n\n    # 1. CUSTOM destroy -----------------------------------------\n    def onDestroy(self):\n        log.info(\"Destroying ...\")\n\n    # 2. Python destroy -----------------------------------------\n    def __del__(self):\n        log.info(\"Running ...\")\n","sub_path":"finance_app/ui/windows/asset_detail/shared/pages/history_chart/history_chart.py","file_name":"history_chart.py","file_ext":"py","file_size_in_byte":9688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"297046629","text":"from core_main import init, start_probe, destroy\n\nif __name__ == '__main__':\n    init()\n    try:\n        while True:\n            n = input(\"Введите номер пробы: \")\n            r = input(\"Введите результат: \")\n            start_probe(n)\n            with open(f'all_probes/{n}_probe/Result', 'w') as f:\n                f.write(r)\n    except KeyboardInterrupt:\n        destroy()","sub_path":"probe_take.py","file_name":"probe_take.py","file_ext":"py","file_size_in_byte":408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"202495092","text":"#!/usr/bin/python\n# Author: Oscar Villagrana\n\nfrom bs4 import BeautifulSoup\nimport re\nimport pandas as pd\nimport requests\n\n\n#-------------------------------------------------\n# Making the Soup\n#-------------------------------------------------\n\n\ndef format_page():\n    print(\"First we will format the job search with rearch keywords\")\n    sub0 = input(\"Enter first Job Search keyword: \")\n    sub1 = input(\"Enter second Job Search keyword: \")\n    page = \"https://www.indeed.com/jobs?as_and={0}+{1}&as_phr=&as_any=&as_not=&as_ttl=&as_cmp=&jt=all&st=&as_src=&salary=&radius=0&l=San+Francisco&fromage=any&limit=50&sort=&psf=advsrch\"\n    a = page.format(sub0, sub1)\n    return a     \n\npage = \"https://www.indeed.com/q-software-developer-l-San-Francisco-jobs.html\"\n\n# page = format_page()\nheaders = {'User-Agent':'Mozilla/5.0'}\n\ndef get_soup():\n    session = requests.Session()\n    pageTree = session.get(page, headers=headers)\n    return BeautifulSoup(pageTree.content, 'html.parser')\n\npageSoup = get_soup()\n\n# print(pageSoup)\n\n#----------------------------------------------------\n# Getters\n#----------------------------------------------------\n\ndef print_company_names():\n    companyName = pageSoup.find_all('span', class_='company')\n    for span in companyName:\n        print(span.text)\n\n# print_company_names()\n\ndef print_job_titles():\n    jobTitle = pageSoup.find_all('div', class_='title')\n    for span in jobTitle:\n        print(span.text)\n\n# print_job_titles()\n\n# Prints company and job information\ndef print_company_and_jobs():\n    companyName = pageSoup.find_all('span', class_='company')\n    jobTitle = pageSoup.find_all('div', class_='title')\n    for span in jobTitle:\n        for x in companyName:\n            print(x.text,span.text)\n\n# print_company_and_jobs()\n\n\n# Makes a list with company and job information\ndef get_company_and_jobs():\n    comps_and_jobs = []\n    companyName = pageSoup.find_all('span', class_='company')\n    jobTitle = pageSoup.find_all('div', class_='title')\n    for span in jobTitle:\n        for x in companyName:\n            comps_and_jobs.append(str(x.text))\n            comps_and_jobs.append(str(span.text))\n    return comps_and_jobs\n\n# get_company_and_jobs()\n\ndef get_company_names():\n    comp_names = []\n    companyName = pageSoup.find_all('span', class_='company')\n    for span in companyName:\n        comp_names.append(str(span.text))\n    return comp_names\n\ndef get_job_titles():\n    jobs = []\n    jobTitle = pageSoup.find_all('div', class_='title')\n    for span in jobTitle:\n        jobs.append(str(span.text))\n    return jobs\n\n\n#-----------------------------------------------------\n# TODO: Get links from Soup and add them to df\n#-----------------------------------------------------\n\n\n# Here I am trying to translate this get_column_titles function \n# example into one that keeps the links of jobs and company\n# example: data visualisation with python and javascript p.152\ndef get_column_titles(table):\n    \"\"\" Get the Nobel categories from the table header \"\"\"\n    cols = []\n    for th in table.find('tr').find_all('th')[1:]: \n        link = th.find('a')\n        # Store the category name and any Wikipedia link it has\n        if link:\n            cols.append({'title':link.text,\\\n                         'href':link.attrs['href']})\n        else:\n            cols.append({'title':th.text, 'href':None})\n    return cols\n\n# my version so far:\ndef get_job_titles():\n    jobs = []\n    jobTitle = pageSoup.find_all('div', class_='title')\n    for span in jobTitle:\n        link = span.find('href')\n        if link:\n            jobs.append({'title':link.text,\n                          'href':link.attrs['href']})\n        else:\n            jobs.append({'title':span.text, 'href':None})\n    return jobs\n\n\n#not working\ndef print_links(): \n    jobLink = pageSoup.find_all('div', class_='title')\n    for div in jobLink: \n        print(div.find('a')['href']) \n\n#not working\ndef print_links(): \n    jobLink = [div.a for div in pageSoup.find_all('div', class_='title')]\n    for div in jobLink: \n        print(div['href']) \n\n\n#-----------------------------------------------------\n# TODO: Make table\n#-----------------------------------------------------\n\n\n# AttributeError when trying to append the output \n# from get_company_and_jobs\ndef make_table():\n    company_name = []\n    job_title = []\n    company_name.append(companyName.replace(\"\\n\",\"\"))\n    job_title.append(jobTitle.text)\n    df = pd.DataFrame({\"company_name\":company_name,\"job_title\":job_title})\n    return df\n\n\n#-----------------------------------------------------\n# TODO: Remove Duplicates\n# Python for data analysis.pg.194.e379\n#-----------------------------------------------------\n\n\ndef remove_duplicates():\n    data = get_company_and_jobs()\n    # returns boolean indicating duplicate row\n    data.duplicated()\n    # returns a df where the duplicated array is True\n    data.drop_duplicates(['column1'])\n    # take_last will return the last observed value combination and default keeps the first\n    data.drop_duplicates(['column1','column2'], take_last=True)\n\n\n# print_company_names()\n# print_job_titles()\n# print_company_and_jobs()\n# get_company_and_jobs()\n","sub_path":"job-search/Indeed_Scraper.py","file_name":"Indeed_Scraper.py","file_ext":"py","file_size_in_byte":5158,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"573282597","text":"import glob\nimport numpy as np\nimport h5py\nimport matplotlib.pyplot as plt\n\ndef gravity():\n    fig = plt.figure()\n    ax1 = fig.add_subplot('411')\n    ax2 = fig.add_subplot('412')\n    ax3 = fig.add_subplot('413')\n    ax4 = fig.add_subplot('414')\n\n    dx = 1.0 / 94.0\n    h5f = h5py.File(\"euler.h5\")\n    rho = h5f[\"prim\"][:,0]\n    phi = h5f[\"grav\"][:,0]\n    gph = (np.roll(phi, -1) - np.roll(phi, +1)) / (2*dx)\n    lph = (np.roll(gph, -1) - np.roll(gph, +1)) / (2*dx)\n\n    ax1.plot(h5f[\"prim\"][:,0], label='rho')\n    ax1.plot(lph+rho.mean(), label='laplacian phi + rhobar')\n    ax1.legend()\n    ax2.plot(h5f[\"prim\"][:,1])\n    ax3.plot(h5f[\"grav\"][:,0], label='')\n    ax4.plot(h5f[\"grav\"][:,1], label='spectral')\n    ax4.plot(gph, label='differenced')\n    ax4.legend()\n    plt.show()\n\ndef hydro():\n    fig = plt.figure()\n    ax1 = fig.add_subplot('311')\n    ax2 = fig.add_subplot('312')\n    ax3 = fig.add_subplot('313')\n\n    for fname in glob.glob('data/*.h5')[-1:]:\n        h5f = h5py.File(fname)\n        ax1.plot(h5f[\"prim\" ][:,0], label='code')\n        ax1.plot(h5f[\"exact\"][:,0], label='exact')\n        ax2.plot(h5f[\"prim\" ][:,1], label='code')\n        ax2.plot(h5f[\"exact\"][:,1], label='exact')\n        ax3.plot(h5f[\"prim\" ][:,2], label='code')\n        ax3.plot(h5f[\"exact\"][:,2], label='exact')\n\n    ax1.legend()\n    ax2.legend()\n    ax3.legend()\n    plt.show()\n\nhydro()\n","sub_path":"tools/gravity-plot.py","file_name":"gravity-plot.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"312847944","text":"\"\"\"\nTime Complexity: O(n log n)\nSpace Complexity: O(n) or O(n log n) reasoning being we are passing halved copies down the tree\n    these only exist for one leg of the tree at a time though\n\"\"\"\n\nfrom icecream import ic\n\ndef queue():\n    def __init__(arr):\n        self.arr = arr\n\n    def next(self):\n        return self.arr.pop(0)\n\n    def add(self, val):\n        self.arr.append(val)\n\ndef merge_sort(arr):\n    \n    if len(arr) < 2:\n        return arr\n\n    split = len(arr)//2\n    arr1 = arr[:split]\n    arr2 = arr[split:]\n    \n    arr1 = merge_sort(arr1)\n    arr2 = merge_sort(arr2)\n\n    ordarr = []\n\n    while len(arr1) != 0 and len(arr2) != 0:\n        if arr1[0] < arr2[0]:\n            ordarr.append(arr1.pop(0))\n        else:\n            ordarr.append(arr2.pop(0))\n\n    if len(arr1) == 0:\n        ordarr.extend(arr2)\n\n    else:\n        ordarr.extend(arr1)\n\n    return ordarr \n\n\nif __name__ == '__main__':\n    arr = [1,3,2,6,3,10,1,1,0,90,-1,0]\n    arr2 = [10,50,80,0,100,25,75,60]\n    arr3 = [10,10,10,0]\n    arr4 = [0,10,10,10]\n    arr5 = [0]\n    arr6 = [0, 0]\n    arr7 = [0,7,0]\n\n    assert merge_sort(arr) == [-1, 0, 0, 1, 1, 1, 2, 3, 3, 6, 10, 90]\n    assert merge_sort(arr2) == [0, 10, 25, 50, 60, 75, 80, 100]\n    assert merge_sort(arr3) == [0, 10, 10, 10]\n    assert merge_sort(arr4) == [0, 10, 10, 10]\n    assert merge_sort(arr5) == [0] \n    assert merge_sort(arr6) == [0,0] \n    assert merge_sort(arr7) == [0,0,7] \n\n    # print(merge_sort(arr))\n    # print(merge_sort(arr2))\n    # print(merge_sort(arr3))\n    # print(merge_sort(arr4))\n    # print(merge_sort(arr5))\n    # print(merge_sort(arr6))\n    # print(merge_sort(arr7))\n","sub_path":"python/sort/SORT_merge.py","file_name":"SORT_merge.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"189633476","text":"class Solution:\n    def isValid(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: bool\n        \"\"\"\n\n        if (len(s) % 2 == 1):\n            return False  # cannot be odd number\n\n        stack = []\n        \n        symbols = dict([(')', '('), (']', '['), ('}', '{')])\n        # closing paranthesis should be 'key' b/c of 'elif' inside the for loop\n        \n        for sub in s:\n            if sub in symbols.values():\n                stack.append(sub)\n            \n            elif sub in symbols.keys():\n                if (stack == []) or (symbols[sub] != stack.pop()):\n                    return False\n            else:\n                return False\n        \n        return stack == []","sub_path":"LeetCode/valid_parentheses.py","file_name":"valid_parentheses.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"519592524","text":"import os\nimport torch\n\nCHECKPOINT_KEY_MODEL = 'model'\nCHECKPOINT_KEY_OPTIMIZER = 'optimizer'\nCHECKPOINT_KEY_EPOCH = 'epoch'\nCHECKPOINT_KEY_ITERATION = 'iteration'\n\n\nclass AverageMeter(object):\n    \"\"\"Computes and stores the average and current value\"\"\"\n    def __init__(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n        self.reset()\n\n    def reset(self):\n        self.val = 0\n        self.avg = 0\n        self.sum = 0\n        self.count = 0\n\n    def update(self, val, n=1):\n        self.val = val\n        self.sum += val * n\n        self.count += n\n        self.avg = self.sum / self.count\n\n\ndef load_checkpoint(model, optimizer, save_path):\n    \"\"\" Load checkpoint.\n\n    Args:\n        model (torch.nn.Module): Model to be overwritten parameters.\n        optimizer (torch.optim.Optimizer): Optimizer to be\n            overwritten parameters.\n        save_path (str): Path to the saved checkpoint file.\n\n    Returns:\n          torch.Module\n          torch.optim.Optimizer\n          int\n          int\n\n    \"\"\"\n    if not os.path.isfile(save_path):\n        err_msg = '=> No checkpoint found at `{}`.'.format(save_path)\n        raise FileNotFoundError(err_msg)\n\n    print('=> Loading checkpoint `{}`.'.format(save_path))\n    state = torch.load(save_path)\n    model.load_state_dict(state[CHECKPOINT_KEY_MODEL])\n    optimizer.load_state_dict(state[CHECKPOINT_KEY_OPTIMIZER])\n\n    if CHECKPOINT_KEY_EPOCH in state.keys():\n        epoch = state[CHECKPOINT_KEY_EPOCH]\n    else:\n        epoch = None\n\n    if CHECKPOINT_KEY_ITERATION in state.keys():\n        iteration = state[CHECKPOINT_KEY_ITERATION]\n    else:\n        iteration = None\n    print('=> Loaded checkpoint `{}`.'.format(save_path))\n    print('=> epoch: `{}`, iteration: {}'.format(epoch, iteration))\n\n    return model, optimizer, epoch, iteration\n\n\ndef save_as_checkpoint(model, optimizer, save_path, epoch=None, iteration=None):\n    \"\"\" Save parameters, epoch, and iteration as checkpoint.\n\n    Args:\n        model (torch.nn.Module): Model to be saved.\n        optimizer (torch.optim.Optimizer): Optimizer.\n        save_path (str): Path to the checkpoint file to be saved.\n        epoch (int): Epoch.\n        iteration (int): Iteration.\n\n    \"\"\"\n    state = {\n        CHECKPOINT_KEY_MODEL: model.state_dict(),\n        CHECKPOINT_KEY_OPTIMIZER: optimizer.state_dict(),\n    }\n\n    if epoch is not None:\n        state[CHECKPOINT_KEY_EPOCH] = epoch\n    if iteration is not None:\n        state[CHECKPOINT_KEY_ITERATION] = iteration\n\n    torch.save(state, save_path)\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"304434923","text":"class Sudoku:\n\n    def __init__(self):\n        self.ROWS = 9\n        self.COLS = 9\n        self.section_size = 3\n        self.board = self.construct_board()\n\n    def construct_board(self):\n        return [[\n            0\n            for row in range(self.ROWS)\n\n        ]for col in range(self.COLS)]\n\n    def valid(self, number, row, col):\n        return self._check_row(number, row, col) and self._check_col(number, row, col) and self._check_section(number, row, col)\n\n    def _check_row(self, number, row, col):\n        for num in range(self.ROWS):\n            if num != col:\n                if number == self.board[row][num]:\n                    return False\n        return True\n\n    def _check_col(self, number, row, col):\n        for num in range(self.COLS):\n            if num != row:\n                if number == self.board[num][col]:\n                    return False\n        return True\n\n    def _check_section(self, number, row, col):\n        row_index, col_index = self._get_section_indexing(row, col)\n        for r in range(row_index, row_index + self.section_size):\n            for c in range(col_index, col_index + self.section_size):\n                if [r, c] != [row, col]:\n                    if number == self.board[r][c]:\n                        return False\n        return True\n\n    def _get_section_indexing(self, row, col):\n        if row < self.section_size:\n            row_index = 0\n        elif row < 2 * self.section_size:\n            row_index = 3\n        else:\n            row_index = 6\n\n        if col < self.section_size:\n            col_index = 0\n        elif col < 2 * self.section_size:\n            col_index = 3\n        else:\n            col_index = 6\n\n        return row_index, col_index\n\n    def next_tile_exists(self):\n        for row in range(self.ROWS):\n            for col in range(self.COLS):\n                if self.board[row][col] == 0:\n                    return row, col\n        return False\n\n    def fill(self):\n        for row in range(self.ROWS):\n            print('Feed in row: {} '.format(row))\n            for col in range(self.COLS):\n                string = input('Press enter to skip, or 1 - 9 for number at col: {} '.format(col))\n                if string != '':\n                    self.board[row][col] = int(string)\n            self.print_board()\n\n    def solve(self):\n\n        if not self.next_tile_exists():\n            return True\n        else:\n            row, col = self.next_tile_exists()\n\n        for num in range(1, 10):\n            if self.valid(num, row, col):\n                self.board[row][col] = num\n\n                if self.solve():\n                    return True\n\n                self.board[row][col] = 0\n\n        return False\n\n    def print_board(self):\n        for row in self.board:\n            print(row)\n\n\nif __name__ == '__main__':\n    sd = Sudoku()\n    fill = int(input('Want to insert numbers? 1 for yes, 0 for no: '))\n    if fill:\n        sd.fill()\n    solvable = sd.solve()\n    print('Was the Sudoku solvable?', solvable)\n    if solvable:\n        sd.print_board()\n","sub_path":"sudoku.py","file_name":"sudoku.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"18822956","text":"import kubernetes.client\nimport kubernetes.config\nimport logging\nimport pk_config\nimport time\n\ndef query_list_of_nodes(endpoint,status='ready'):\n  log=logging.getLogger('pk_docker')\n  list_of_nodes=[]\n  if pk_config.simulate():\n    return None\n  kubernetes.config.load_kube_config()\n  client = kubernetes.client.CoreV1Api()\n  try:\n    if status=='ready':\n      nodes = [x for x in client.list_node().items if not x.spec.taints]\n    elif status=='down':\n      nodes = [x for x in client.list_node().items if x.spec.taints and 'master' not in x.spec.taints[0].key]\n    for n in nodes:\n      a = {}\n      a['ID']=n.metadata.name\n      a['Addr']=n.status.addresses[0].address\n      list_of_nodes.append(a.copy())\n    return list_of_nodes\n  except Exception as e:\n    log.exception('(Q) Query of k8s nodes failed.')\n    return None\n\ndef scale_docker_service(endpoint,service_name,replicas):\n  service_name = '-'.join(service_name.split('_')[1:])\n  log=logging.getLogger('pk_docker')\n  log.info('(S) => m_container_count: {0}'.format(replicas))\n  if pk_config.simulate():\n    return\n  kubernetes.config.load_kube_config()\n  client = kubernetes.client.ExtensionsV1beta1Api()\n  try:\n    dep = client.read_namespaced_deployment(service_name, \"default\")\n    dep.spec.replicas = replicas\n    client.patch_namespaced_deployment_scale(service_name, \"default\", dep)\n  except Exception as e:\n    log.warning('(S) Scaling of k8s service \"{0}\" failed: {1}'.format(service_name,str(e)))\n  return\n\ndef query_docker_service_replicas(endpoint,service_name):\n  service_name = '-'.join(service_name.split('_')[1:])\n  log=logging.getLogger('pk_docker')\n  instance = 1\n  if pk_config.simulate():\n    return\n  kubernetes.config.load_kube_config()\n  client = kubernetes.client.ExtensionsV1beta1Api()\n  try:\n    dep = client.read_namespaced_deployment(service_name, \"default\")\n    replicas = dep.spec.replicas\n    log.debug('(C) => m_container_count for {0}: {1}'.format(service_name,replicas))\n  except Exception as e:\n    log.warning('(C) Querying k8s service \"{0}\" replicas failed: {1}'.format(service_name,str(e)))\n  return instance\n\ndef query_service_network(endpoint, stack_name, service_name):\n  id = None\n  log=logging.getLogger('pk_docker')\n  #client = docker.DockerClient(base_url=endpoint)\n  #full_service_name = stack_name + \"_\" + service_name\n  #if pk_config.simulate():\n    #return None\n  #service_list = client.services.list()\n  #i = 0\n  #while i < len(service_list) and service_list[i].name != full_service_name:\n    #i += 1\n  #if i < len(service_list) and service_list[i].name == full_service_name:\n    #if len(service_list[i].attrs.get(\"Spec\").get(\"TaskTemplate\").get(\"Networks\")) == 1:\n      #id = service_list[i].attrs.get(\"Spec\").get(\"TaskTemplate\").get(\"Networks\")[0].get(\"Target\")\n      #log.debug('Docker service \"{0}\" in stack \"{1}\" is connected to network \"{2}\" with id \"{3}\".'\n\t\t #.format(service_name, stack_name, client.networks.get(id).name),str(id))\n    #else:\n      #log.warning('Docker service \"{0}\" is connected to more than one network.'.format(full_service_name))\n  #else:\n  log.warning('Docker networking not supported in kubernetes, cannot query')\n  return id\n\n\ndef attach_container_to_network(endpoint, container, network_id):\n  log=logging.getLogger('pk_docker')\n  #client = docker.DockerClient(base_url=endpoint)\n  #network = client.networks.get(network_id)\n  #if client.containers.get(container).status == \"running\":\n    #network.connect(container)\n    #log.info('Container \"{0}\" is connected to network \"{1}\".'.format(container, network.name))\n  #else:\n  log.warning('Docker networking not supported in kubernetes, attach container failed')\n  return\n\ndef detach_container_from_network(endpoint, container, network_id):\n  log=logging.getLogger('pk_docker')\n  #client = docker.DockerClient(base_url=client_address, version=client_version)\n  #network = client.networks.get(network_id)\n  #if client.containers.get(container).status == \"running\":\n    #network.disconnect(container)\n    #log.info('Container \"{0}\" is disconnected from network \"{1}\".'.format(container, network.name))\n  #else:\n  log.warning('Docker networking not supported in kubernetes, detach container failed')\n  return\n\ndown_nodes_stored={}\n\ndef remove_node(endpoint,id):\n  log=logging.getLogger('pk_docker')\n  if pk_config.simulate():\n    return\n  kubernetes.config.load_kube_config()\n  client = kubernetes.client.CoreV1Api()\n  try:\n    client.delete_node(id, {})\n  except Exception:\n    log.error('(M) => Removing k8s node failed.')\n  return\n\ndef down_nodes_cleanup_by_list(stored, actual):\n  setStored = { v['ID'] for k,v in stored.items() }\n  setActual = { x['ID'] for x in actual }\n  missing = { x for x in setStored if x not in setActual }\n  for x in missing:\n    del stored[x]\n\ndef down_nodes_add_from_list(stored, actual):\n  for node in actual:\n    if 'ID' in node and node['ID'] not in stored:\n      stored[node['ID']]=node\n      stored[node['ID']]['micado_timestamp']=int(time.time())\n\ndef down_nodes_cleanup_by_timeout(endpoint, stored, timeout):\n  log=logging.getLogger('pk_docker')\n  current_time = int(time.time())\n  for id, node in stored.items():\n    if node['micado_timestamp']+timeout < current_time:\n      log.info('(M) => Node {0} is down for more than {1} seconds, removing.'.format(id,timeout))\n      remove_node(endpoint,id)\n      del stored[id]\n\ndef down_nodes_maintenance(endpoint, down_nodes_timeout = 120):\n  log=logging.getLogger('pk_docker')\n  down_nodes_actual = query_list_of_nodes(endpoint,status='down')\n  down_nodes_cleanup_by_list(down_nodes_stored, down_nodes_actual)\n  down_nodes_add_from_list(down_nodes_stored, down_nodes_actual)\n  down_nodes_cleanup_by_timeout(endpoint, down_nodes_stored, down_nodes_timeout)\n  return\n","sub_path":"handle_k8s.py","file_name":"handle_k8s.py","file_ext":"py","file_size_in_byte":5739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"108592700","text":"# Copied from test_middleware.py\n\nimport pytest\nfrom django.test import TestCase\nfrom django.test.utils import override_settings\nfrom django_dynamic_fixture import get\n\nfrom readthedocs.builds.models import Version\nfrom readthedocs.projects.constants import PUBLIC\nfrom readthedocs.projects.models import Domain, Project, ProjectRelationship\nfrom readthedocs.proxito.middleware import ProxitoMiddleware\nfrom readthedocs.rtd_tests.base import RequestFactoryTestMixin\nfrom readthedocs.rtd_tests.utils import create_user\n\n\n@pytest.mark.proxito\n@override_settings(PUBLIC_DOMAIN='dev.readthedocs.io')\nclass MiddlewareTests(RequestFactoryTestMixin, TestCase):\n\n    def setUp(self):\n        self.middleware = ProxitoMiddleware()\n        self.url = '/'\n        self.owner = create_user(username='owner', password='test')\n        self.pip = get(\n            Project,\n            slug='pip',\n            users=[self.owner],\n            privacy_level='public'\n        )\n\n    def run_middleware(self, request):\n        return self.middleware.process_request(request)\n\n    def test_proper_cname(self):\n        domain = 'docs.random.com'\n        get(Domain, project=self.pip, domain=domain)\n        request = self.request(self.url, HTTP_HOST=domain)\n        res = self.run_middleware(request)\n        self.assertIsNone(res)\n        self.assertEqual(request.cname, True)\n        self.assertEqual(request.host_project_slug, 'pip')\n\n    def test_proper_cname_https_upgrade(self):\n        cname = 'docs.random.com'\n        get(Domain, project=self.pip, domain=cname, canonical=True, https=True)\n\n        for url in (self.url, '/subdir/'):\n            request = self.request(url, HTTP_HOST=cname)\n            res = self.run_middleware(request)\n            self.assertIsNone(res)\n            self.assertTrue(hasattr(request, 'canonicalize'))\n            self.assertEqual(request.canonicalize, 'https')\n\n    def test_canonical_cname_redirect(self):\n        \"\"\"Requests to the public domain URL should redirect to the custom domain if the domain is canonical/https.\"\"\"\n        cname = 'docs.random.com'\n        domain = get(Domain, project=self.pip, domain=cname, canonical=False, https=False)\n\n        request = self.request(self.url, HTTP_HOST='pip.dev.readthedocs.io')\n        res = self.run_middleware(request)\n        self.assertIsNone(res)\n        self.assertFalse(hasattr(request, 'canonicalize'))\n\n        # Make the domain canonical/https and make sure we redirect\n        domain.canonical = True\n        domain.https = True\n        domain.save()\n        for url in (self.url, '/subdir/'):\n            request = self.request(url, HTTP_HOST='pip.dev.readthedocs.io')\n            res = self.run_middleware(request)\n            self.assertIsNone(res)\n            self.assertTrue(hasattr(request, 'canonicalize'))\n            self.assertEqual(request.canonicalize, 'canonical-cname')\n\n    # We are not canonicalizing custom domains -> public domain for now\n    @pytest.mark.xfail(strict=True)\n    def test_canonical_cname_redirect_public_domain(self):\n        \"\"\"Requests to a custom domain should redirect to the public domain or canonical domain if not canonical.\"\"\"\n        cname = 'docs.random.com'\n        domain = get(Domain, project=self.pip, domain=cname, canonical=False, https=False)\n\n        request = self.request(self.url, HTTP_HOST=cname)\n        res = self.run_middleware(request)\n        self.assertIsNone(res)\n        self.assertTrue(hasattr(request, 'canonicalize'))\n        self.assertEqual(request.canonicalize, 'noncanonical-cname')\n\n        # Make the domain canonical and make sure we don't redirect\n        domain.canonical = True\n        domain.save()\n        for url in (self.url, '/subdir/'):\n            request = self.request(url, HTTP_HOST=cname)\n            res = self.run_middleware(request)\n            self.assertIsNone(res)\n            self.assertFalse(hasattr(request, 'canonicalize'))\n\n    def test_proper_cname_uppercase(self):\n        get(Domain, project=self.pip, domain='docs.random.com')\n        request = self.request(self.url, HTTP_HOST='docs.RANDOM.COM')\n        self.run_middleware(request)\n        self.assertEqual(request.cname, True)\n        self.assertEqual(request.host_project_slug, 'pip')\n\n    def test_invalid_cname(self):\n        self.assertFalse(Domain.objects.filter(domain='my.host.com').exists())\n        request = self.request(self.url, HTTP_HOST='my.host.com')\n        r = self.run_middleware(request)\n        # We show the 404 error page\n        self.assertContains(r, 'my.host.com', status_code=404)\n\n    def test_proper_subdomain(self):\n        request = self.request(self.url, HTTP_HOST='pip.dev.readthedocs.io')\n        self.run_middleware(request)\n        self.assertEqual(request.subdomain, True)\n        self.assertEqual(request.host_project_slug, 'pip')\n\n    @override_settings(PUBLIC_DOMAIN='foo.bar.readthedocs.io')\n    def test_subdomain_different_length(self):\n        request = self.request(\n            self.url, HTTP_HOST='pip.foo.bar.readthedocs.io'\n        )\n        self.run_middleware(request)\n        self.assertEqual(request.subdomain, True)\n        self.assertEqual(request.host_project_slug, 'pip')\n\n    def test_request_header(self):\n        request = self.request(\n            self.url, HTTP_HOST='some.random.com', HTTP_X_RTD_SLUG='pip'\n        )\n        self.run_middleware(request)\n        self.assertEqual(request.rtdheader, True)\n        self.assertEqual(request.host_project_slug, 'pip')\n\n    def test_request_header_uppercase(self):\n        request = self.request(\n            self.url, HTTP_HOST='some.random.com', HTTP_X_RTD_SLUG='PIP'\n        )\n        self.run_middleware(request)\n\n        self.assertEqual(request.rtdheader, True)\n        self.assertEqual(request.host_project_slug, 'pip')\n\n    def test_long_bad_subdomain(self):\n        domain = 'www.pip.dev.readthedocs.io'\n        request = self.request(self.url, HTTP_HOST=domain)\n        res = self.run_middleware(request)\n        self.assertEqual(res.status_code, 400)\n\n\n@pytest.mark.proxito\n@override_settings(PUBLIC_DOMAIN='dev.readthedocs.io')\nclass MiddlewareURLConfTests(TestCase):\n\n    def setUp(self):\n        self.owner = create_user(username='owner', password='test')\n        self.domain = 'pip.dev.readthedocs.io'\n        self.pip = get(\n            Project,\n            slug='pip',\n            users=[self.owner],\n            privacy_level=PUBLIC,\n            urlconf='subpath/to/$version/$language/$filename'  # Flipped\n        )\n        self.testing_version = get(\n            Version,\n            slug='testing',\n            project=self.pip,\n            built=True,\n            active=True,\n        )\n        self.pip.versions.update(privacy_level=PUBLIC)\n\n    def test_proxied_api_methods(self):\n        # This is mostly a unit test, but useful to make sure the below tests work\n        self.assertEqual(self.pip.proxied_api_url, 'subpath/to/_/')\n        self.assertEqual(self.pip.proxied_api_host, '/subpath/to/_')\n\n    def test_middleware_urlconf(self):\n        resp = self.client.get('/subpath/to/testing/en/foodex.html', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 200)\n        self.assertEqual(\n            resp['X-Accel-Redirect'],\n            '/proxito/media/html/pip/testing/foodex.html',\n        )\n\n    def test_middleware_urlconf_redirects_subpath_root(self):\n        resp = self.client.get('/subpath/to/', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 302)\n        self.assertEqual(\n            resp['Location'],\n            'http://pip.dev.readthedocs.io/subpath/to/latest/en/',\n        )\n\n    def test_middleware_urlconf_redirects_root(self):\n        resp = self.client.get('/', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 302)\n        self.assertEqual(\n            resp['Location'],\n            'http://pip.dev.readthedocs.io/subpath/to/latest/en/',\n        )\n\n    def test_middleware_urlconf_invalid(self):\n        resp = self.client.get('/subpath/to/latest/index.html', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 404)\n\n    def test_middleware_urlconf_subpath_downloads(self):\n        # These aren't configurable yet\n        resp = self.client.get('/subpath/to/_/downloads/en/latest/pdf/', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 200)\n        self.assertEqual(\n            resp['X-Accel-Redirect'],\n            '/proxito/media/pdf/pip/latest/pip.pdf',\n        )\n\n    def test_middleware_urlconf_subpath_api(self):\n        # These aren't configurable yet\n        resp = self.client.get(\n            '/subpath/to/_/api/v2/footer_html/?project=pip&version=latest&language=en&page=index',\n            HTTP_HOST=self.domain\n        )\n        self.assertEqual(resp.status_code, 200)\n        self.assertContains(\n            resp,\n            'Inserted RTD Footer',\n        )\n\n    def test_urlconf_is_escaped(self):\n        self.pip.urlconf = '3.6/$version/$language/$filename'\n        self.pip.save()\n\n        self.assertEqual(self.pip.proxied_api_url, '3.6/_/')\n        self.assertEqual(self.pip.proxied_api_host, '/3.6/_')\n\n        resp = self.client.get('/316/latest/en/index.html', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 404)\n        resp = self.client.get('/3.6/latest/en/index.html', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 200)\n\n        resp = self.client.get('/316/_/downloads/en/latest/pdf/', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 404)\n        resp = self.client.get('/3.6/_/downloads/en/latest/pdf/', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 200)\n\n        resp = self.client.get(\n            '/316/_/api/v2/footer_html/?project=pip&version=latest&language=en&page=index',\n            HTTP_HOST=self.domain\n        )\n        self.assertEqual(resp.status_code, 404)\n        resp = self.client.get(\n            '/3.6/_/api/v2/footer_html/?project=pip&version=latest&language=en&page=index',\n            HTTP_HOST=self.domain\n        )\n        self.assertEqual(resp.status_code, 200)\n\n\n\n@pytest.mark.proxito\n@override_settings(PUBLIC_DOMAIN='dev.readthedocs.io')\nclass MiddlewareURLConfSubprojectTests(TestCase):\n\n    def setUp(self):\n        self.owner = create_user(username='owner', password='test')\n        self.domain = 'pip.dev.readthedocs.io'\n        self.pip = get(\n            Project,\n            name='pip',\n            slug='pip',\n            users=[self.owner],\n            privacy_level=PUBLIC,\n            urlconf='subpath/$subproject/$version/$language/$filename'  # Flipped\n        )\n        self.pip.versions.update(privacy_level=PUBLIC)\n        self.subproject = get(\n            Project,\n            name='subproject',\n            slug='subproject',\n            users=[self.owner],\n            privacy_level=PUBLIC,\n            main_language_project=None,\n        )\n        self.testing_version = get(\n            Version,\n            slug='testing',\n            project=self.subproject,\n            built=True,\n            active=True,\n        )\n        self.subproject.versions.update(privacy_level=PUBLIC)\n        self.relationship = get(\n            ProjectRelationship,\n            parent=self.pip,\n            child=self.subproject,\n        )\n\n    def test_middleware_urlconf_subproject(self):\n        resp = self.client.get('/subpath/subproject/testing/en/foodex.html', HTTP_HOST=self.domain)\n        self.assertEqual(resp.status_code, 200)\n        self.assertEqual(\n            resp['X-Accel-Redirect'],\n            '/proxito/media/html/subproject/testing/foodex.html',\n        )\n","sub_path":"readthedocs/proxito/tests/test_middleware.py","file_name":"test_middleware.py","file_ext":"py","file_size_in_byte":11596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"473625921","text":"import mir_eval\nimport numpy as np\nfrom pystoi import stoi\nfrom pesq import pesq\n\ndef si_sdr(est, true):\n    alpha = np.dot(true, est)/np.dot(true, true)\n    target = alpha*true\n    noise = target - est\n    val = 10*np.log10(np.dot(target, target)/np.dot(noise, noise))\n    return val\n\n\ndef sp_enhance_evals(est_source, clean_source, noisy_source, fs):\n    est_source = est_source.cpu().clone().numpy()\n    clean_source = clean_source.cpu().clone().numpy()\n    noisy_source = noisy_source.cpu().clone().numpy()\n    \n    pesq_val = pesq(fs, clean_source, est_source, 'wb')\n    stoi_val = stoi(clean_source, est_source, fs, extended=False)\n    si_sdr_val = si_sdr(est_source, clean_source)\n    noisy_si_sdr_val = si_sdr(noisy_source, clean_source)\n    si_sdr_improvement = si_sdr_val -noisy_si_sdr_val\n    return pesq_val, stoi_val, si_sdr_val, si_sdr_improvement\n\ndef mss_evals(est_source, clean, noisy):\n    est_source = est_source.to('cpu').detach().numpy().copy()\n    clean = clean.to('cpu').detach().numpy().copy()\n    noisy = noisy.to('cpu').detach().numpy().copy()\n    est_accompany = noisy - est_source\n    true_accompany = noisy - clean\n   \n    \n    true_len = est_source.shape[0]\n    est_buff = np.zeros((2, true_len), dtype=np.float32)\n    true_buff = np.zeros((2, true_len), dtype=np.float32)\n    \n    est_buff[0,:] = est_source[:]\n    est_buff[1,:] = est_accompany[:]\n    \n    true_buff[0,:] = clean[:]\n    true_buff[1,:] = true_accompany[:]\n    sdr, sir, sar, perm =  mir_eval.separation.bss_eval_sources(true_buff, est_buff)\n    si_sdr_val = si_sdr(est_source, clean)\n    noisy_si_sdr_val = si_sdr(noisy, clean)\n    si_sdr_improvement = si_sdr_val -noisy_si_sdr_val\n    return sdr[0], sir[0], sar[0], si_sdr_val, si_sdr_improvement\n     \n    ","sub_path":"utils/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":1755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"168918287","text":"'''\nSource : https://oj.leetcode.com/problems/plus-one/\nAuthor : Yuan Wang\nDate   : 2018-05-28\n\n/********************************************************************************** \n* \n* Given a non-negative number represented as an array of digits, plus one to the number.\n* \n* The digits are stored such that the most significant digit is at the head of the list.\n*               \n**********************************************************************************/\n'''\n\n\n#convert the list to an integer and plus this inter by one\n#then convert it back to string and list it to get a new list\n#time complexity O(n), space complexity O(n)\ndef plusOne(digits):\n    \"\"\"\n    :type digits: List[int]\n    :rtype: List[int]\n    \"\"\"\n    digits=[str(i) for i in digits]\n    number=\"\".join(digits)\n    number=int(number)\n    number+=1\n    number=str(number)\n    \n    digits=[int(i) for i in number]\n    \n    return digits\n\ndef plusOneB(digits):\n    d = [str(x) for x in digits]\n    num = int(''.join(d)) + 1\n    \n    return list(map(int, str(num)))\n\n#iterate the list from the end, detect if any digit is 9\n#if it is 9, make it to 0 and add by 1 at the digit before it\n#if initial number is 9, we need add a 1 at the front of the list\ndef plusOneC(digits):\n\n    for i in range(len(digits)-1,-1,-1):\n        if digits[i] < 9:\n            digits[i]+=1\n            return digits\n        else:\n            digits[i] = 0\n\n    digits.insert(0,1)\n    return digits\n\ndigits=[9]\nprint(plusOneC(digits))","sub_path":"Array/PlusOne.py","file_name":"PlusOne.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"313691228","text":"import math\nfrom datetime import datetime, timedelta\n\nimport cv2\nimport depthai as dai\nimport numpy as np\nfrom model_api import BBox\n\n\ndef pad_img(img, pad_value, target_dims):\n    h, w, _ = img.shape\n    pads = []\n    pads.append(int(math.floor((target_dims[0] - h) / 2.0)))\n    pads.append(int(math.floor((target_dims[1] - w) / 2.0)))\n    pads.append(int(target_dims[0] - h - pads[0]))\n    pads.append(int(target_dims[1] - w - pads[1]))\n    padded_img = cv2.copyMakeBorder(\n        img, pads[0], pads[2], pads[1], pads[3], cv2.BORDER_CONSTANT, value=pad_value,\n    )\n    return padded_img, pads\n\n\ndef wait_for_results(queue):\n    start = datetime.now()\n    while not queue.has():\n        if datetime.now() - start > timedelta(seconds=1):\n            return False\n    return True\n\n\nclass FaceProcessor:\n    def __init__(self, threshold):\n        self.threshold = threshold\n        self.class_names = {\n            0: \"background\",\n            1: \"person\",\n        }\n        self.input_height, self.input_width = (300, 300)\n\n    def preprocess(self, data):\n        preprocessed_data = []\n        data_infos = []\n        for img in data:\n            img = np.array(img)[:, :, ::-1]\n            height, width, _ = img.shape\n            if self.input_height / self.input_width < height / width:\n                scale = self.input_height / height\n            else:\n                scale = self.input_width / width\n\n            scaled_img = cv2.resize(\n                img, (0, 0), fx=scale, fy=scale, interpolation=cv2.INTER_CUBIC,\n            )\n            padded_img, pad = pad_img(\n                scaled_img, (0, 0, 0), [self.input_height, self.input_width],\n            )\n\n            padded_img = padded_img.transpose((2, 0, 1))\n            planar_img = padded_img.flatten().astype(np.float32)\n            preprocessed_data.append(planar_img)\n            data_infos.append((scale, pad))\n\n        return [preprocessed_data, data_infos]\n\n    def postprocess(self, predictions):\n        postprocessed_result = []\n\n        for result, input_info in zip(predictions[0], predictions[1]):\n            scale, pads = input_info\n            h, w = self.input_height, self.input_width\n            original_h = int((h - (pads[0] + pads[2])) / scale)\n            original_w = int((w - (pads[1] + pads[3])) / scale)\n            boxes = np.array(result.getLayerFp16(result.getAllLayerNames()[0])).reshape(\n                -1, 7,\n            )\n            boxes = boxes[boxes[:, 2] > self.threshold]\n            image_predictions = []\n            for box in boxes:\n                image_predictions.append(\n                    BBox(\n                        x1=float(\n                            np.clip((box[3] * w - pads[1]) / scale, 0, original_w),\n                        ),\n                        y1=float(\n                            np.clip((box[4] * h - pads[0]) / scale, 0, original_h),\n                        ),\n                        x2=float(\n                            np.clip((box[5] * w - pads[1]) / scale, 0, original_w),\n                        ),\n                        y2=float(\n                            np.clip((box[6] * h - pads[0]) / scale, 0, original_h),\n                        ),\n                        score=float(box[2]),\n                        class_name=self.class_names[int(box[1])],\n                    ),\n                )\n            postprocessed_result.append(image_predictions)\n\n        return postprocessed_result\n\n    def forward(self, in_queue, out_queue, data):\n        results = []\n        for sample in data[0]:\n            nn_data = dai.NNData()\n            nn_data.setLayer(\"data\", sample)\n            in_queue.send(nn_data)\n            assert wait_for_results(out_queue)\n            results.append(out_queue.get())\n        data[0] = results\n        return data\n","sub_path":"age_gender_recognition_retail/age_gender_recognition_retail/face_processing.py","file_name":"face_processing.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"505822925","text":"import signal\n\nclass KeyboardInterruptBlocked(object):\n\n    def __init__(self, ProgressBarObject=None):\n        '''\n        Blocks the KeyboradInterrupt signal\n\n        Temporarily block the KeyboardInterrupt signal, restore the handler for SIGINT,\n        and raise KeyboardInterrupt if necessary.\n\n        Args:\n\n        Returns:\n            KeyboardInterruptBlocked: An instance of the class\n        '''\n        self.ProgressBar = ProgressBarObject\n\n    def __enter__(self):\n        '''\n        Mute the KeyboardInterrupt signal\n\n        Store the default handler for SIGINT (KeyboardInterrupt), replace the handler\n        with our own to ignore KeyboardInterrupt signal.\n\n        Args:\n\n        Returns:\n\n        '''\n        self.received_signal = False\n        self.old_handler = signal.getsignal(signal.SIGINT)\n        signal.signal(signal.SIGINT, self.handler)\n\n    def handler(self, signum, frame):\n        '''\n        Handle the SIGINT signal\n\n        Update the message for the progress bar object if there is one and store the\n        components of the triggered signal so that the signal can be raised later.\n\n        Args:\n            signum (int) : the number associated with the triggered signal\n            frame (str): the stack frame when the signal was triggered\n\n        Returns:\n\n        '''\n        if self.ProgressBar:\n            self.ProgressBar.set_message('Finishing up...')\n        self.received_signal = (signum, frame)\n\n    def __exit__(self, exception_type, exception_value, traceback):\n        '''\n        Unmute the KeyboardInterrupt signal\n\n        Rebind the old signal handler for SIGINT, and re-raises the KeyboardInterrupt if\n        it was triggered while the signal was muted.\n\n        Args:\n            exception_type (str): the type of the exception raised (None if no exceptions)\n            exception_value (str): the value of the exception raised (None if no exceptions)\n            traceback (str): traceback of the exception raised  (None if no exceptions)\n\n        Returns:\n\n        '''\n        signal.signal(signal.SIGINT, self.old_handler)\n        if self.received_signal:\n            self.old_handler(*self.received_signal)\n\ndef assert_extended(condition, assert_message='', function=None):\n    \"\"\"\n    Assert the given condition and run a function if it fails\n\n    Try to assert condition.  Catch the AssertionError upon failure and run function if it\n    is provided.  Raise the AssertionError afterwards.\n\n    Args:\n        condition (bool): the condition to test when asserting\n        assert_message (str, optional): the message to print when the assertion fails, defaults to\n            empty string\n        function (func, optional): the function to run if the assertion fails (should not take\n            any arguments), defaults to None\n\n    Returns:\n\n    Raises:\n        AssertionError: Occurs when the condition asserted is False, should never happen\n    \"\"\"\n    try:\n        if assert_message:\n            assert condition, assert_message\n        else:\n            assert condition\n    except AssertionError:\n        if function:\n            function()\n        raise\n","sub_path":"lib/Config/interrupt.py","file_name":"interrupt.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"628519216","text":"#!/usr/bin/env python3\n\n\"\"\" MINIZINC CONSTRAINT PROGRAMMING\n    - poses the problem as a CP problem and solves it that way\n    - fast to satisfy, but extremely slow to optimise\n    TODO\n    - try different solvers \"\"\"\n\nfrom argparse import ArgumentParser\nfrom collections import Counter\nfrom itertools import combinations\nimport subprocess\nimport util\n\n\nAPPROACH = 3\nOFFSET = 0  # bottomleft\n\n\ndef write_dzn(lib, dzn_file):\n    \"\"\" converts the library to dzn format \"\"\"\n\n    b_attr = lib['B_ATTR']\n    c_attr = lib['C_ATTR']\n    h_attr = lib['H_ATTR']\n    n_attr = lib['N_ATTR']\n\n    dzn = ''\n\n    # board bbox\n    dzn += 'boardBox = [ {}, {}, {}, {} ];\\n'.format(*b_attr['BOARD_AREA'])\n\n    # keepout bbox\n    dzn += 'nKeepouts = {};\\n'.format(len(b_attr['BOARD_PLACEMENT_KEEPOUT']))\n    dzn += 'keepoutBox = [|'\n    for keepout in b_attr['BOARD_PLACEMENT_KEEPOUT']:\n        dzn += '\\n {}, {}, {}, {} |'.format(*keepout['PLACEMENT_OUTLINE'])\n    dzn += '];\\n'\n\n    # holes\n    dzn += 'nHoles = {};\\n'.format(len(h_attr))\n    dzn += 'holeBox = [|'\n    for hole in h_attr:\n        x, y, r = *hole['OFFSET'], hole['RADIUS']\n        dzn += '\\n {}, {}, {}, {} |'.format(x-r, y-r, x+r, y+r)\n    dzn += '];\\n'\n\n    # number of componenets\n    n_comp = len(c_attr)\n    dzn += 'nComponents = {};\\n'.format(n_comp)\n\n    # reference numbers\n    ref_nos = sorted(c_attr)\n    dzn += 'componentRef = [ \"{}\" ];\\n'.format('\", \"'.join(ref_nos))\n\n    # component bbox\n    dzn += 'componentBox = [|'\n    for i in ref_nos:\n        dzn += '\\n {}, {}, {}, {} |'.format(*c_attr[i]['OFFSET'],\n                                           *c_attr[i]['PLACEMENT_OUTLINE'])\n    dzn += '];\\n'\n\n    # component orientation\n    dzn += 'componentRotate = [ '\n    dzn += ', '.join(str(int(c_attr[i]['ORIENTATION'])) for i in ref_nos)\n    dzn += ' ];\\n'\n    \n    # fixed components\n    n_fixed = len(b_attr['FIXED_COMPONENT_LOCATION'])\n    dzn += 'nFixed = {};\\n'.format(n_fixed)\n    dzn += 'componentFixed = [ {} ];\\n'.format(\n            ', '.join(str(i+1)\n                for i in range(n_comp)\n                if ref_nos[i] in b_attr['FIXED_COMPONENT_LOCATION']))\n\n    # unfixed components\n    dzn += 'nUnfixed = {};\\n'.format(n_comp - n_fixed)\n    dzn += 'componentUnfixed = [ {} ];\\n'.format(\n            ', '.join(str(i+1)\n                for i in range(n_comp)\n                if ref_nos[i] not in b_attr['FIXED_COMPONENT_LOCATION']))\n\n    # netlist\n    comp_id = dict(zip(ref_nos, range(1, n_comp+1)))\n    conns = Counter()\n    for netlist in n_attr.values():\n        for conn in combinations(sorted(set(list(zip(*netlist))[0])), 2):\n            conns[conn] += 1\n\n    dzn += 'nConnections = {};\\n'.format(len(conns))\n    dzn += 'componentNet = [|'\n    for (i1, i2), v in sorted(conns.items()):\n        dzn += '\\n {}, {}, {} |'.format(comp_id[i1], comp_id[i2], v)\n    dzn += '];\\n'\n\n    # save as dzn\n    with open(dzn_file, 'w') as f:\n        f.write(dzn)\n    print('Saving .dzn library [{}]'.format(dzn_file))\n\n\ndef run_minizinc(lib, args):\n    \"\"\" runs a constraint program using minizinc\n        returns None \"\"\"\n\n    # load the component library\n    dzn_file       = '/'.join((util.PATH, args.data_dir, 'lib.dzn'))\n    placement_file = '/'.join((util.PATH, args.data_dir, 'a3_placement.txt'))\n\n    # convert library to dzn format\n    write_dzn(lib, dzn_file)\n\n    # run the constraint program\n    cmd = 'mzn-{} a3_{}.mzn {} -o {}'.format(args.solver, args.solve,\n                                             dzn_file, placement_file)\n    print(cmd)\n    subprocess.run(cmd, shell=True, check=True)\n    print('Saving placement [{}]'.format(placement_file))\n\n    return None\n\n\ndef main(args):\n    lib = util.load_lib(args.data_dir, offset=OFFSET)\n    run_minizinc(lib, args)\n    util.draw_layout(args.data_dir, lib, approach=APPROACH, offset=OFFSET,\n                     label=args.label, paths=args.paths)\n\n    return None\n\nif __name__ == '__main__':\n    parser = ArgumentParser(description='minizinc\\'s CP solver')\n    parser.add_argument('-data_dir', default='files/toy_example',\n                        help='path to directory with component library')\n    parser.add_argument('-solver', default='chuffed',\n                        help='chuffed, gecode, g12fd, g12lazy, g12mip')\n    parser.add_argument('-solve', default='satisfy',\n                        help='optimise/satisfy')\n    parser.add_argument('-label', action='store_true',\n                        help='render component labels')\n    parser.add_argument('-paths', action='store_true',\n                        help='render routing paths')\n    args = parser.parse_args()\n\n    util.timeit(main, args)\n\n","sub_path":"a3_minizinc.py","file_name":"a3_minizinc.py","file_ext":"py","file_size_in_byte":4641,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"466266314","text":"\n\n#calss header\nclass _SHOT():\n\tdef __init__(self,): \n\t\tself.name = \"SHOT\"\n\t\tself.definitions = [u'(of silk) having small threads of a colour in it, so that the main colour appears to change depending on the angle at which the cloth is seen: ', u'no longer working or effective: ', u'to get rid of or free of something, or to leave something: ']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'adjectives'\n\n\n\tdef run(self, obj1, obj2):\n\t\tself.jsondata[obj2] = {}\n\t\tself.jsondata[obj2]['properties'] = self.name.lower()\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/adjectives/_shot.py","file_name":"_shot.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"69170998","text":"\n\nfrom xai.brain.wordbase.adjectives._weird import _WEIRD\n\n#calss header\nclass _WEIRDEST(_WEIRD, ):\n\tdef __init__(self,): \n\t\t_WEIRD.__init__(self)\n\t\tself.name = \"WEIRDEST\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"weird\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_weirdest.py","file_name":"_weirdest.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"561327048","text":"import os\r\n\r\n\r\ndef allTrue(l):\r\n    for i in l:\r\n        if i == False:\r\n            return False\r\n    return True\r\n\r\ndef solveSheep(path):\r\n    with open(path, \"rt\") as fin:\r\n        stuff = fin.read().splitlines()\r\n    string = \"\"\r\n    numbers = [False]*10    \r\n    for i in xrange(1, len(stuff)):\r\n        if (int(stuff[i]) == 0):\r\n            string = string + \"Case #%d: INSOMNIA\\n\" %(i)\r\n        else:\r\n            count = 1\r\n            a = int(stuff[i])\r\n            while not(allTrue(numbers)):\r\n                n = a * count\r\n                while n != 0:\r\n                    numbers[n%10] = True\r\n                    n = n/10\r\n                count = count + 1\r\n            string = string + \"Case #%d: %d\\n\" %(i, a*(count-1))\r\n            numbers = [False] * 10\r\n\r\n    with open(\"solution1 large.txt\", \"wt\") as fout:\r\n        fout.write(string)\r\n","sub_path":"codes/CodeJamCrawler/16_0_1_neat/16_0_1_Jonmerrin_qualifying1.py","file_name":"16_0_1_Jonmerrin_qualifying1.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"556461570","text":"#!/usr/bin/env python3\n\ndef main():\n    a = []\n    with open(\"input.txt\") as f:\n        for l in f:\n            a.append(int(l))\n\n    for x in a:\n        for y in a:\n            for z in a:\n                if x + y + z== 2020:\n                    print(x, y, z, x*y*z)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"1/pgm01.py","file_name":"pgm01.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"452037187","text":"import sys\nimport math\nimport data_loader\n\nfrom functions import *\nfrom settings import *\nfrom tests import *\nfrom tables import *\nfrom globals import the as the\nfrom globals import help as help\n\nimport re\nimport os\n\n\n\n\n\ndef driver(options, help, funs):\n    saved, fails = {}, 0\n    d=cli(settings(help))\n\n    for key in d:\n        val=d[key]\n        options[key]=val\n\n\n    saved = options\n\n    if options['help'] is not False:\n\n        print(help)\n    else:\n        for what, fun in funs.items():\n            if options['go'] == 'all' or what == options['go']:\n\n                for k, v in saved.items():\n                    options[k] = v\n                Seed = options['seed']\n                if funs[what]() == False:\n                    fails = fails + 1\n\n                    print(str(u'\\u274C') + \" fail: \" + str(what))\n\n                else:\n\n                    print(str(u'\\u2705') + \" pass: \" + str(what))\n\n    sys.exit(fails)\n\n\n\n\n\n\n\n\n\n\n\n\n#Main\nif __name__ == '__main__':\n\n    eg('the', 'show settings', the_test_1, help)\n    eg('sym', 'check syms', sym_test_3, help)\n    eg('num', 'check nums', num_test_4, help)\n    # eg('csv', 'read from csv', csv_5, help)\n    # eg('data', 'read data csv', data_6, help)\n    # eg(\"stats\", \"stats from data\", stats_7, help)\n    # eg('clone', 'duplicate structure', clone_test_8, help)\n    # eg('around', 'sorting nearest neighbors', around_test_9, help)\n    # eg('half', '1-level bi-clustering', half_test_10, help)\n    # eg('cluster', 'N-level bi-clustering', cluster_test_11, help)\n    # eg('optimize', 'semi-supervised optimization', optimize_test_12, help)\n    eg('copy', 'check copy', copy_test_13, help)\n    eg('repcols', 'checking repcols', repcols_test_14, help)\n    eg('synonyms', 'checking repcols cluster', synonyms_test_15, help)\n    eg('reprows', 'checking reprows', reprows_test_16, help)\n    eg('prototypes', 'checking reprows cluster', prototypes_test_17, help)\n    eg('position', 'where is wally', position_test_18, help)\n    eg('every', 'the whole enchilada', every_test_19, help)\n\n\n\n\n\n\n\n\n\n    driver(the, help, egs)\n\n\n\n\n","sub_path":"src/HW4/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"74476805","text":"\"\"\"Tests for fourier function.\"\"\"\nimport unittest\nimport numpy as np\n\nfrom toqito.matrix.matrices.fourier import fourier\n\n\nclass TestFourierMatrix(unittest.TestCase):\n    \"\"\"Unit test for fourier_matrix.\"\"\"\n\n    def test_fourier_matrix_dim_2(self):\n        \"\"\"Fourier matrix of dimension 2.\"\"\"\n        expected_res = np.array(\n            [[1 / np.sqrt(2), 1 / np.sqrt(2)], [1 / np.sqrt(2), -1 / np.sqrt(2)]]\n        )\n\n        res = fourier(2)\n\n        bool_mat = np.isclose(res, expected_res)\n        self.assertEqual(np.all(bool_mat), True)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"tests/test_fourier_matrix.py","file_name":"test_fourier_matrix.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"138146679","text":"#!/usr/bin/python\r\n#conding=utf-8\r\nimport requests,json,sys\r\nmobiles=sys.argv[1]\r\nsubject=sys.argv[2]\r\nmessages=sys.argvp[3]\r\ndef gettenant_access_token():\r\n    tokenurl=\"https://open.feishu.cn/open-apis/auth/v3/tenant_access_token/internal/\"\r\n    headers={\"Content-Type\":\"application/json\"}\r\n    data={\r\n        \"app_id\":\"cli_9ec6258e80b12340e\",\r\n        \"app_secret\":\"f716Gi27Yi25n5K0WbaKpfrfsretgsg1ujl\"\r\n\r\n    }\r\n    request=requests.post(url=tokenurl,headers=headers,json=data)\r\n    response=json.loads(request.content)['tenant_access_token']\r\n    return response\r\ndef getuserid(tenant_access_token):\r\n    #mobiles=\"15101456278\"\r\n    userurl=\"https://open.feishu.cn/open-apis/user/v1/batch_get_id?mobiles=%s\"%mobiles\r\n    headers={\"Authorization\":\"Bearer %s\"%tenant_access_token}\r\n    request=requests.get(url=userurl,headers=headers)\r\n    response=json.loads(request.content)['data']['mobile_users'][mobiles][0]['user_id']\r\n    return response\r\ndef getchatid(tenant_access_token):\r\n    #获取chatid\r\n    chaturl=\"https://open.feishu.cn/open-apis/chat/v4/list?page_size=20\"\r\n    headers={\"Authorization\":\"Bearer %s\"%tenant_access_token,\"Content-Type\":\"application/json\"}\r\n    request=requests.get(url=chaturl,headers=headers)\r\n    response=json.loads(request.content)['data']['groups'][0]['chat_id']\r\n    return response\r\ndef sendmes(user_id,chat_id,tenant_access_token):\r\n    #向群里发送消息\r\n    sendurl=\"https://open.feishu.cn/open-apis/message/v4/send/\"\r\n    headers={\"Authorization\":\"Bearer %s\"%tenant_access_token,\"Content-Type\":\"application/json\"}\r\n    data={\"chat_id\":chat_id,\r\n        \"msg_type\":\"text\",\r\n        \"content\":{\r\n            \"text\":\"%s<at user_id=\\\"%s\\\">test</at>\"%(messages,user_id)\r\n        }\r\n    }\r\n    #给个人发送消息\r\n    # data={\"user_id\":user_id,\r\n    #     \"msg_type\":\"text\",\r\n    #     \"content\":{\r\n    #         \"text\":\"%s<at user_id=\\\"%s\\\">test</at>\"%(messages,user_id)\r\n    #     }\r\n    # }\r\n    request=requests.post(url=sendurl,headers=headers,json=data)\r\n    print(request.content)\r\n\r\n\r\ntenant_access_token=gettenant_access_token()\r\nuser_id=getuserid(tenant_access_token)\r\nchat_id=getchatid(tenant_access_token)\r\nsendmes(user_id,chat_id,tenant_access_token)\r\n","sub_path":"feishu.py","file_name":"feishu.py","file_ext":"py","file_size_in_byte":2217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"270264739","text":"from Instrucciones.TablaSimbolos.Instruccion import Instruccion\nfrom Instrucciones.Sql_create.ShowDatabases import ShowDatabases\nfrom Instrucciones.Excepcion import Excepcion\nfrom storageManager.jsonMode import *\nfrom Optimizador.C3D import *\nfrom Instrucciones.TablaSimbolos import Instruccion3D as c3d\n\nclass AlterDatabase(Instruccion):\n    def __init__(self, id, tipo, opcion, id2, strGram, linea, columna):\n        Instruccion.__init__(self,tipo,linea,columna, strGram)\n        self.nombreAntiguo = id\n        self.nombreNuevo = id2\n        self.opcion = opcion\n\n    def ejecutar(self, tabla, arbol):\n        super().ejecutar(tabla,arbol)\n        #aqui vamos a renombrar el alter\n        res = alterDatabase(self.nombreAntiguo, self.nombreNuevo)\n        if( res == 1):\n            Excepcion(3,\"Semántico\",\"No se pudo renombrar la base de datos\",self.linea,self.columna)\n        elif(res == 2):\n            error = Excepcion(\"100\",\"Semantico\",\"La bd no existe.\",self.linea,self.columna)\n        elif(res == 3):\n            error = Excepcion(\"100\",\"Semantico\",\"La bd nueva ya existe.\",self.linea,self.columna)\n        else:\n            if(len(arbol.listaBd)==0):\n                #aqui vamos a renombrar en memoria\n                Instruccion = ShowDatabases(None, None, self.linea, self.columna)\n                Instruccion.ejecutar(tabla,arbol)\n                '''\n                import os\n\n                archivo = \"/home/decodigo/Documentos/python/archivos/archivo.txt\"\n                nombre_nuevo = \"/home/decodigo/Documentos/python/archivos/archivo_renombrado.txt\"\n\n                os.rename(archivo, nombre_nuevo)\n                '''\n                arbol.consola.append(f\"La base de datos se cambio: {self.nombreNuevo} correctamente.\")\n            else:\n                #aqui vamos a renombrar en memoria\n                arbol.renombrarBd(self.nombreAntiguo,self.nombreNuevo)\n                arbol.consola.append(f\"La base de datos se cambio: {self.nombreNuevo} correctamente.\")\n\n    def generar3D(self, tabla, arbol):\n        super().generar3D(tabla,arbol)\n        code = []\n        code.append(c3d.asignacionH())\n        code.append(c3d.aumentarP())\n        t0 = c3d.getTemporal()\n        code.append(c3d.asignacionString(t0, \"ALTER DATABASE \" + self.nombreAntiguo + \" RENAME TO \" + self.nombreNuevo + \";\"))\n        code.append(c3d.asignacionTemporalStack(t0))\n        code.append(c3d.LlamFuncion('call_funcion_intermedia'))\n\n        return code\n\n    def generar3DV2(self, tabla, arbol):\n        super().generar3D(tabla,arbol)\n        code = []\n        code.append('h = p')\n        code.append('h = h + 1')\n        t0 = c3d.getTemporal()\n        code.append(t0 + ' = \"' + self.nombreAntiguo + '\"')\n        code.append('heap[h] = ' + t0)\n        code.append('h = h + 1')\n        t1 = c3d.getTemporal()\n        code.append(t1 + ' = \"' + self.opcion + '\"')\n        code.append('heap[h] = ' + t1)\n        code.append('h = h + 1')\n        t2 = c3d.getTemporal()\n        code.append(t2 + ' = \"' + self.nombreNuevo + '\"')\n        code.append('heap[h] = ' + t2)\n        code.append('p = h')\n        code.append('call_alter_database()')\n        \n        return code\n\n'''\ninstruccion = AlterDatabase(\"hola mundo\",None, 1,2)\n\ninstruccion.ejecutar(None,None)\n'''","sub_path":"parser/fase2/team22/Instrucciones/Sql_alter/AlterDatabase.py","file_name":"AlterDatabase.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"32598453","text":"import tensorflow as tf\nimport functools\n\nfrom models.src.vocab import Vocab\n\n\ndef create_data_dict(data, mode):\n    data_dict = {\n            'sequences': data[0], \n            'lengths': data[1]\n        }\n\n    if mode == 'spellcheck':\n        data_dict['spellcheck'] = True\n\n    return data_dict\n\n\ndef generator_fn(data, vocab):\n    for sequence in data:\n        sequence = [vocab.char2idx['<START>']] + sequence + [vocab.char2idx['<END>']]\n        yield (sequence, len(sequence))\n\n\ndef input_fn(data, params, vocab, mode):\n    shapes = ([None], ())\n    types = (tf.int32, tf.int32)\n    defaults = (vocab.char2idx['<PAD>'], 0)\n\n    dataset = tf.data.Dataset.from_generator(functools.partial(generator_fn, data, vocab), \n                                             output_shapes=shapes, output_types=types)\n    if mode == 'train':\n        dataset = dataset.shuffle(buffer_size=params['train_size'], reshuffle_each_iteration=True)\n        dataset = dataset.repeat(params['num_epochs'])\n\n    dataset = dataset.padded_batch(params['batch_size'], shapes, defaults)\n    dataset = dataset.map(lambda *x: create_data_dict(x, mode))\n    dataset = dataset.prefetch(buffer_size=1) \n    return dataset","sub_path":"telegram_bot/models/src/feed.py","file_name":"feed.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"392815002","text":"from examples import feature_list_02012\nimport cv2\nfrom face_recognition import api\n\n\nvideo_capture = cv2.VideoCapture(0)\n\nwhile True:\n\n    ret, image = video_capture.read()\n    small_image = cv2.resize(image, (0, 0), fx=0.25, fy=0.25)\n\n    face_landmark_tuple = api.face_landmarks(small_image)\n\n    feature_list = feature_list_02012.FeatureList\n\n    for point in face_landmark_tuple:\n        feature = feature_list(point)\n        print(feature.result())\n\n        left_box = int(feature.left) * 4\n        top_box = int(feature.top) * 4\n        right_box = int(feature.right) * 4\n        bottom_box = int(feature.bottom) * 4\n\n        cv2.rectangle(image, (left_box, top_box), (right_box, bottom_box), (0, 255, 0), 2)\n\n        for i, j in point:\n            center = (i*4, j*4)\n            cv2.circle(image, center, 3, (255, 255, 255))\n\n    cv2.imshow(\"Video\", image)\n\n    if cv2.waitKey(1) & 0xFF == ord(\"q\"):\n        break\n\nvideo_capture.release()\ncv2.destroyAllWindows()\n","sub_path":"examples/opencv_landmarklist2.py","file_name":"opencv_landmarklist2.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"177427834","text":"from PIL import Image\nfrom PIL import ImageDraw\nfrom random import randint\nimport time\nimport numpy as np\n\n\n#compute mandelbrot\n#threshold = 1000\ndef compute_mandelbrot(c):\n\n    z = complex(0,0)\n    #we iterate a given amount(threshold), and check if our c is in the mandelbrot\n    for i in range(0,1000):\n        z = z*z+c\n\n        #if our absolute value of z, which is run a given amount of times over itself, is\n        #ever over 2, we know that it has escaped our escape value of 2, and is not in the mandelbrot set\n        #which is only when z can be added many times together for the value of c(complex coordinate)\n        #and never goes over 2.\n        #If c is not n the mandelbrot set, we return how many times we iterated before the c escaped 2\n        if(abs(z)>2):\n            return i\n    #if our z never goes over 2, we know its in the mandelbrot set, and its coordinates will be painted black\n    #we fix the coloring later\n    return None;\n\n\n#function that doesnt use numpy for the solving\ndef makeMandelbrotImageTraditional(minX,maxX,minY,maxY,centerX,centerY,colorStyle,fileName):\n\n    #add 1000 colors to our grid depending on what scale we are following (colorstyle)\n\n    if(colorStyle == \"random\"):\n        colorCollection = np.random.randint(255,size=(1000,3))\n    elif(colorStyle == \"red\"):#alternative color1 red\n        colorCollection = [0]*1000\n        for i in range (0,1000):\n            while True:\n                r=randint(0,255)\n                g=randint(0,20)\n                b=randint(0,20)\n\n                if (r,g,b) not in colorCollection: #in order to create distinct colors\n                    break\n                #if the color is not in our list yet we break and end up here\n            colorCollection[i] = (r,g,b)\n    elif(colorStyle==\"blue\"):   #alternative color2 blue\n        colorCollection = [0]*1000\n        for i in range (0,1000):\n\n            while True:\n                r=randint(0,255)\n                g=randint(94,255)\n                b=randint(250,255)\n\n                if (r,g,b) not in colorCollection: #in order to create distinct colors\n                    break\n        #if the color is not in our list yet we break and end up here\n            colorCollection[i] = (r,g,b)\n    elif(colorStyle==\"purple\"):#alternative color3 purp\n        colorCollection = [0]*1000\n\n        for i in range (0,1000):\n\n            while True:\n                r=randint(0,255)\n                g=randint(0,5)\n                b=randint(0,230)\n\n                if (r,g,b) not in colorCollection: #in order to create distinct colors\n                    break\n        #if the color is not in our list yet we break and end up here\n            colorCollection[i] = (r,g,b)\n\n    #since i am using the image module,i am having a hard time finding out how to reposition my rectangle on the mandelbrot,\n    #since it appears that a lot of the x and y-values that make colors(not in mandelbrot-set) are decimals,\n    #and i dont understand how i am going to apply these decimal-ranges to my coordinates,since my coordinates mostly\n    #are positive from 0 and so on so i just\n    #multiply with a decimal to scale the integers from for example min 0 to max 500 before i add it to the function\n    #which finds out the iterations below in the mandelbrot. SO now, it appears\n    #that the minX and maxY just decides the resolution of my image, which would work, if you think about it, to zoom in and explore\n    #the pattern.\n    #BUT ON THE BRIGHT SIDE: Although this might be a bad solution, it certainly takes a lot of computation, and it appears\n    #that you(the instructors), made this task to focus on the computation of mandelbrot, and not the image, in this case, my solution\n    #is adequate.\n\n    #rectangle to be filled, change from 0 to 1000 in min and max to get full picture. I dont really understand how to do this task\n    #in another way\n\n    scaleFix=1.0/((maxX-minX)/3)\n\n    #uncomment bottom center initialization to make center 0, but this makes the pattern more boring\n    #center=(2.2,1.5)\n    #center =(0.0,0.0)\n\n    image = Image.new(\"RGB\",(maxX-minX,maxY-minY))\n    drawer = ImageDraw.Draw(image)\n\n    #colorize our rectangle\n    for x in range (minX,maxX):\n        for y in range (minY,maxY):\n            iterationsBeforeEscape = compute_mandelbrot(complex(x*scaleFix-centerX,y*scaleFix-centerY))\n            if(iterationsBeforeEscape == None):\n                drawer.point((x,y),fill = (0,0,0))\n            else:\n                drawer.point((x,y),fill = colorCollection[iterationsBeforeEscape])\n\n    image.save(fileName)\n\n\n#make mandelbrot with minX=0,maxX=1000,minY=0;maxY=1000, and filename\n\n#startTime = time.time();\nmakeMandelbrotImageTraditional(0,200,0,200,\"result1.png\")\n#endTime = time.time();\n\n#total = endTime-startTime;\n\n#print(total);\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#\n","sub_path":"mandelbrot_1.py","file_name":"mandelbrot_1.py","file_ext":"py","file_size_in_byte":4830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"458939195","text":"import json\nimport re\nimport string\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport nltk\nnltk.download('wordnet')\nfrom nltk.stem import WordNetLemmatizer\nimport re\nfrom src import VOCAB_PATH\n\n\nclass SentenceTokenizer:\n    # Create array of integers (tokens) corresponding to input sentences.\n\n    def read_json(self):\n        \"\"\"read vocabulary from json file\"\"\"\n        f = open(VOCAB_PATH)\n        vocabulary = json.load(f)\n        f.close()\n        return vocabulary\n\n    def __init__(self, vocabulary=None):\n        if vocabulary is None:\n            self.vocabulary = self.lemmatize_vocab(self.read_json())\n        else:\n            self.vocabulary = self.lemmatize_vocab(vocabulary)\n\n\n    def decontracted(self, phrase):\n        # specific\n        phrase = re.sub(r\"won't\", \"will not\", phrase)\n        phrase = re.sub(r\"can\\'t\", \"can not\", phrase)\n        phrase = re.sub(r\"could\\'t\", \"could not\", phrase)\n        phrase = re.sub(r\"would\\'t\", \"would not\", phrase)\n\n        # general\n        phrase = re.sub(r\"n\\'t\", \" not\", phrase)\n        phrase = re.sub(r\"\\'re\", \" are\", phrase)\n        phrase = re.sub(r\"\\'s\", \" is\", phrase)\n        phrase = re.sub(r\"\\'d\", \" would\", phrase)\n        phrase = re.sub(r\"\\'ll\", \" will\", phrase)\n        phrase = re.sub(r\"\\'t\", \" not\", phrase)\n        phrase = re.sub(r\"\\'ve\", \" have\", phrase)\n        phrase = re.sub(r\"\\'m\", \" am\", phrase)\n        return phrase\n\n    def lemmatize_vocab(self, vocabulary):\n        # exchange keys and values\n        vocabulary = dict((v, k) for k, v in vocabulary.items())\n        # now words are values\n        lemmatizer = WordNetLemmatizer()\n        lemmatized_values = [lemmatizer.lemmatize(w) for w in vocabulary.values()]\n        tokens = list(range(0, len(lemmatized_values)))\n        return dict(zip(lemmatized_values, tokens))\n\n    def extend_vocabulary(self, new_word):\n        # Extends current vocabulary with new words\n        lemmatizer = WordNetLemmatizer()\n        base_val = max(self.vocabulary.values())\n        new_val = base_val + 1\n        self.vocabulary[lemmatizer.lemmatize(new_word)] = new_val\n        return new_val\n\n    def save_vocabulary(self, vocabulary):\n\n        with open(VOCAB_PATH, 'w') as outfile:\n            json.dump(vocabulary, outfile, indent=2)\n        outfile.close()\n\n    def tokenize_sentences(self, sentences, maxlen=50, save=False):\n        \"\"\"Converts a given list of sentences into a array of integers according to vocabulary\"\"\"\n\n        tokens_matr = np.zeros((len(sentences), maxlen), dtype='uint16')\n        i = 0\n        for sentence in sentences:\n            sentence = self.decontracted(sentence)\n            # take only words\n            sentence = re.sub('[' + string.punctuation + ']', '', sentence).split()\n            tokens = [self.vocabulary[k.lower()] if k.lower() in self.vocabulary.keys() else self.vocabulary['CUSTOM_UNKNOWN'] for k in sentence]\n\n            # Empty sentence\n            if len(tokens) == 0:\n                tokens = [self.vocabulary['CUSTOM_UNKNOWN']]\n\n            for j in range(len(tokens)):\n                if j < maxlen:\n                    tokens_matr[i, j] = tokens[j]\n            i += 1\n\n\n\n        if save:\n            self.save_vocabulary(self.vocabulary)\n        return tokens_matr\n\n    def to_sentence(self, arr_tokens):\n        \"\"\"Converts a given list of tokens into a array of words according to vocabulary\"\"\"\n        # create inverse vocabulary\n        vocabulary = dict((v, k) for k, v in self.vocabulary.items())\n        arr_sentences = []\n        for token in arr_tokens:\n            sentence = [vocabulary[k] if k in vocabulary.keys() else 'NotFound' for k in token]\n            arr_sentences.append(sentence)\n        return arr_sentences\n\n    def split_train_val_test(self, data, split=[0.8, 0.1, 0.1], extend_with=0):\n\n        indexes = list(range(len(data)))\n        ind_train, ind_test = train_test_split(indexes, test_size=split[2])\n        ind_train, ind_val = train_test_split(ind_train, test_size=split[1])\n\n        train = [data[x] for x in ind_train]\n        test = [data[x] for x in ind_test]\n        val = [data[x] for x in ind_val]\n\n        result = [self.tokenize_sentences(s) for s in [train, val, test]]\n\n        return result\n\n\n\n","sub_path":"src/features/sentence_tokenizer.py","file_name":"sentence_tokenizer.py","file_ext":"py","file_size_in_byte":4251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"636114095","text":"import aiohttp\nimport asyncio\nimport async_timeout\n\n@asyncio.coroutine\ndef fetch(session, url):\n    with async_timeout.timeout(10):\n        response = yield from session.get(url)\n        try:\n            return (yield from response.text())\n        except Exception as e:\n            response.close()\n            raise e\n        finally:\n            yield from response.release()\n\n@asyncio.coroutine\ndef main(loop):\n    with aiohttp.ClientSession(loop=loop, connector = aiohttp.TCPConnector(verify_ssl=False)) as session:\n        html = yield from fetch(session, 'https://dns.google.com/resolve?name=g.cn')\n        print(html)\n\nloop = asyncio.get_event_loop()\nloop.run_until_complete(main(loop))","sub_path":"tests/test_aio.py","file_name":"test_aio.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"579333115","text":"import json\nimport csv\n\nif __name__ == '__main__':\n    \"\"\"\n    Read in artist user pairs, and convert the user id to the hash value and\n    save as a new file.\n    \"\"\"\n    with open('data/artist_user_pairs.txt') as up:\n        user_pairs = eval(up.read())\n    with open('data/hash_users.txt') as hu:\n        user_lookup = eval(hu.read())\n    output_dict = {}\n    for key, value in user_pairs.iteritems():\n        new_value = user_lookup[value]\n        output_dict[key] = new_value\n\n    json.dump(output_dict, open(\"data/hashed_artist_user_pairs.txt\",'w'))\n    with open('data/hash_songs.txt') as hs:\n        song_lookup = eval(hs.read())\n\n    song_meta_wid = []\n    with open('data/song_meta.csv') as sm:\n        reader = csv.reader(sm)\n        reader.next()\n        song_meta_wid = dict()\n        for line in reader:\n            content = line\n            try:\n                h_id = song_lookup[line[0]]\n                song_meta_wid[h_id] = line\n            except KeyError:\n                pass\n    with open('data/song_meta_hashed.txt', 'wb') as sm_hashed:\n        sm_hashed.write(str(song_meta_wid))\n\n\n    with open('data/artist_user_reccomendations.txt') as aur:\n        artist_user_recommendations = eval(aur.read())\n        artist_user_rec_meta = dict()\n        for artist, songs in artist_user_recommendations.iteritems():\n            song_data = []\n            for song in songs:\n                song_data.append(song_meta_wid[song][1:])\n            artist_user_rec_meta[artist] = song_data\n\n    with open('data/artist_user_recommendations_metadata.txt', 'wb') as aurm:\n        aurm.write(str(artist_user_rec_meta))\n","sub_path":"ETL/id_to_hash.py","file_name":"id_to_hash.py","file_ext":"py","file_size_in_byte":1627,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"584271514","text":"from bs4 import BeautifulSoup\nfrom errors import StudentNotFoundError, RollError\nfrom argparse import ArgumentTypeError\nimport requests\nimport random\nimport re\nimport os\n# TODO: Add functionality to scrape __VIEWSTATE first for remote scraping \n# url = 'http://14.139.205.172/web_new/Default.aspx'\n\ndef _parse_result_table(result_table):\n   '''\n      input: bs4 parsed html table\n      return: a dictionary of result table extracted from scraping\n   '''\n   trs = result_table.find_all(\"table\")[0].find_all(\"tr\", recursive=False)[1:]\n\n   result = {}\n   headers = ['', 'subject', 'test_1', 'test_2','assignment', 'quiz_avg', 'end_sem', 'total', 'grade']\n   code = ''\n\n   for tr in trs:\n      tds = tr.find_all(\"td\", recursive=False)\n      \n      for i,td in enumerate(tds):\n         if i == 0:\n            code = td.text.strip()\n            result[code] = dict()\n         else:\n            result[code][headers[i]] = td.text.strip()\n\n   return result\n\ndef _first_request(url, regno, semester=None):\n   '''\n      return: [ valid_semesters, form_data ]\n   '''\n   \n   post_data = {\n      \"ToolkitScriptManager1_HiddenField\" :\"\",\n      \"__EVENTTARGET\": \"\",\n      \"__EVENTARGUMENT\": \"\",\n      # always first manually/automatically set _VIEWSTATE\n      \"__VIEWSTATE\": \"/wEPDwULLTE5MTk3NDAxNjkPZBYCAgEPZBYCAicPZBYEAhIPFCsAAmRkZAIUD2QWAgIDDxQrAAJkZGQYAwUeX19Db250cm9sc1JlcXVpcmVQb3N0QmFja0tleV9fFgQFCmJ0bmltZ1Nob3cFEGJ0bmltZ1Nob3dSZXN1bHQFC2J0bkltZ1ByaW50BQxidG5pbWdDYW5jZWwFCWx2QmFja2xvZw9nZAUQbHZTdWJqZWN0RGV0YWlscw9nZEM0w+4vgN3bILbO4Zuep/+vlVWU\",\n      \"__VIEWSTATEGENERATOR\" :\"011071C8\",\n      \"txtRegno\": \"{}\".format(regno),\n      \"btnimgShow.x\": str(random.randint(1, 99)),\n      \"btnimgShow.y\": str(random.randint(1, 99)),\n      \"ddlSemester\": \"0\",\n      \"hfldno\": \"\",\n      \"hdfidno\": \"\"\n   }\n   \n   resp = requests.post(url, post_data)      \n   data = resp.text\n\n   if \"alert('Student Not Available or Result Yet not Published.')\" in data:\n      # Student with this regno does not exists\n      raise StudentNotFoundError('%s does not exists'%regno)\n   \n   # parse the html data and extract valid semesters\n   soup = BeautifulSoup(data, 'lxml')\n   \n   valid_sems = soup.find_all(\"option\", attrs={\"value\": re.compile(r'[123456789]')})\n   valid_sems = [ _['value'] for _ in valid_sems ]\n\n   # if no semester is supplied default to the last semester\n   if semester == None:\n      semester = valid_sems[-1]\n   \n   # change to str in case semester is passed as an integer\n   semester = str(semester)\n   \n   # supplied semester is not a valid semester\n   # happens when some semester is not present for some rolls but\n   # present for other rolls\n   if semester not in valid_sems:\n      raise StudentNotFoundError(\"Result for semester %s does not exists %s\"%(semester,valid_sems) )\n   \n   form_vars = [ \"ToolkitScriptManager1_HiddenField\", \"__EVENTTARGET\", \"__EVENTARGUMENT\",\n   \"__VIEWSTATE\", \"__VIEWSTATEGENERATOR\", \"hfIdno\", \"hdfidno\" ]\n\n   form_data = {}\n\n   for var in form_vars:\n      try:\n         form_data[var] = soup.find(\"input\", attrs={\"id\": var})['value']\n      except:\n         form_data[var] = \"\"\n\n   form_data[\"txtRegno\"] = regno\n   form_data[\"btnimgShowResult.x\"] = str(random.randint(1,100))\n   form_data[\"btnimgShowResult.y\"] = str(random.randint(1,100))\n   form_data[\"ddlSemester\"] = semester # setting the semester for `second call response`\n\n   return valid_sems, form_data\n\n\ndef _second_request_for_result(url, post_data):\n   '''\n      return: a dictionary of student data\n   '''\n   \n   resp = requests.post(url, post_data)\n   data = resp.text\n\n   soup = BeautifulSoup(data, 'lxml')\n\n   student = {}\n\n   student['regno'] = soup.find('span',attrs={'id':'lblRollNo'}).text\n   student['session'] = soup.find(\"span\", attrs={\"id\": \"lblsession\"}).text\n   student['degree'] = soup.find(\"span\", attrs={\"id\": \"lblDegreeName\"}).text\n   student['scheme'] = soup.find(\"span\", attrs={\"id\": \"lblSchemetype\"}).text\n\n   student['student_name'] = soup.find(\"span\", attrs={\"id\": \"lblStudentName\"}).text\n   student['branch'] = soup.find(\"span\", attrs={\"id\": \"lblBranchName\"}).text\n   student['semester'] = post_data['ddlSemester']\n\n   student['result_status'] = soup.find(\"span\", attrs={\"id\": \"lblResult\"}).text\n   student['sgpa'] = soup.find(\"span\", attrs={\"id\": \"lblSPI\"}).text\n   student['cgpa'] = soup.find(\"span\", attrs={\"id\": \"lblCPI\"}).text\n\n   student['publish_date'] = soup.find(\"span\", attrs={\"id\": \"lblPublishDate\"}).text\n   student['student_id'] = soup.find(\"input\", attrs={\"id\": \"hfIdno\"})['value']\n\n   student['result'] = _parse_result_table(soup.find(\"div\",attrs={\"id\": \"PnlShowResult\"}).find_all(\"tr\")[5])\n\n   return student\n\n\ndef get_pdf(regno, student_id, semester, log, url):\n   '''\n      Downloads and save the pdf in pdfs directory\n   '''\n   # regno is used in filename so to update the database\n   # easily, can use student_id in place of regno but\n   # database updation steps will increase\n   filename = '%s_%s'%(regno, semester)\n   filepath = os.path.join('pdfs', filename)\n   \n   if not os.path.exists('pdfs'):\n      log.debug('Creating folder pdfs')\n      os.makedirs('pdfs')\n\n   if os.path.exists(filepath):\n      log.warning('pdf for regno=%s, semester=%s already exists', regno, semester)\n      return\n\n   url= url.format(student_id, semester)\n   log.debug('Requesting to %s', url)\n   \n   try:\n      resp = requests.get(url)\n      with open(filepath, 'wb') as fp:\n         fp.write(resp.content)\n      log.info('Downloaded pdf for regno=%s, semester=%s', regno, semester)\n   except Exception:\n      log.error('Failed to download pdf for regno=%s, semester=%s', regno, semester)\n   \n   return resp.content\n\n\ndef get_result(regno, url, semester=None):\n   '''\n      This calls [ _first_call_response, _second_call_response ] to fetch a particular result\n      It also downloads the result pdf also. This is to save time by not making the same two calls\n      again.\n   '''\n\n   _, form_data = _first_request(url, regno, semester)\n   \n   # means no student found with the specified regno\n   if _ == None:\n      raise StudentNotFoundError('%s does not exists'%regno)\n   \n   student = _second_request_for_result(url, form_data)\n   return student\n    \n   \ndef get_semesters(regno, url):\n   valid_sems, _ = _first_request(url, regno)\n   return valid_sems\n   \ndef valid_range(_range):\n   '''\n      This method check if the range is composed of valid registration number\n      by internally calling valid_regno\n   '''\n   start, end = _range.split('-')\n   \n   valid_regno(start)\n   valid_regno(end)\n   \n   return [start, end]    \n   \n      \ndef valid_regno(regno):\n   '''\n      This method check if the regno is a valid registration number\n      by checking it against a regex\n      -> Raises Error if not a valid regno\n   '''\n   regno_regex = re.compile(r'^2\\d{3}(UG|PG)\\w{2,4}\\d{1,3}|TYC\\d\\d\\w{2,4}\\d{1,3}$', re.IGNORECASE)\n   \n   if re.match(regno_regex, regno):\n      return regno\n   else:\n      raise ArgumentTypeError('%s is not a valid registration number'%regno)\n\n   \ndef split_regno(regno):\n   '''\n      After the valid_regno function is True this function is called\n      to split the regno in three parts (year, course, branch)\n   '''\n   if 'TYC' in regno:\n      year = '20'+regno[3:5]\n      course = 'TYC'\n      branch = re.sub(r'\\d+$', '',regno[5:]).upper()\n   else:\n      year = regno[:4]\n      course = regno[4:6]\n      branch = re.sub(r'\\d+$', '', regno[6:]).upper()\n\n   return year, course, branch\n\n\ndef regno_from_opts(year, course, branch, roll):\n   \n   year = str(year)\n   regno = ''\n   \n   if course == 'TYC':\n      regno = ''.join([course, year[2:], branch, \"%02d\"%roll])\n   elif course == 'PG':\n      regno = ''.join([year, course, branch, \"%02d\"%roll])\n   else:\n      regno = ''.join([year, course, branch, \"%03d\"%roll])\n\n   return regno","sub_path":"rpf_cli/portal/scraper/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":7775,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"93702970","text":"# Code taken from : https://github.com/yaojingguo/python-code/blob/master/rc4.py\n\n# and adjusted\n\nA = ord('a')\n\ndef info_num(text):\n  for c in text:\n    print(ord(c) - A, \" \", end=\"\")\n  print()\n\n# Implementation of the example in http://en.wikipedia.org/wiki/Keystream\ndef keystream(plain, key):\n  i = 0\n  length = len(plain)\n  result = \"\"\n  while i < length:\n    byte = (ord(plain[i]) - A + ord(key[i % 26]) - A) % 26\n    print(byte, \" \", end=\"\")\n    result += chr(byte + A)\n    i = i + 1\n  print()\n  print(\"text: \", result)\n\n# Refer to http://en.wikipedia.org/wiki/Rc4#The_key-scheduling_algorithm_.28KSA.29\ndef ksa(key):\n  S = [c for c in range(256)]\n  j = 0\n  keylength = len(key)\n  for i in range(256):\n    j = (j + S[i] + key[i % keylength]) % 256\n    S[i], S[j] = S[j], S[i]\n  return S\n\n# Refer to http://en.wikipedia.org/wiki/Rc4#The_pseudo-random_generation_algorithm_.28PRGA.29\ndef prga(ciphertext, S): #  This decrypts the key, it is the final step.\n  i = 0\n  j = 0\n  k = 0\n  length = len(ciphertext)\n  while k < length:\n    pad = [len(ciphertext)];\n    i = (i + 1) % 256\n    j = (j + S[i]) % 256\n    S[i], S[j] = S[j], S[i]\n    pad[k] = S[(S[i] + S[j]) % 256]\n    k =  pad[k] ^ ciphertext[k]\n    print(\"%02X\" % k, end=\"\")\n    k += 1;\n  print()\n\ndef rc4(data_text, key_text):\n  ciphertext = [ord(c) for c in data_text]\n  key = [ord(c) for c in key_text]\n  S = ksa(key)\n  prga(ciphertext, S)\n\n\ndef test_keystream():\n  plain = \"attackatdawn\"\n  key = \"6e6963\"\n  info_num(plain)\n  info_num(key)\n  keystream(plain, key)\n\ndef test_ksa():\n  key = [0x97, 0x98, 0x99, 0x1F, 0x1F]\n  S = ksa(key) # S = \n  print(S)\n\ndef test_rc4():\n  rc4(\"Plaintext\", \"Key\")\n\ntest_keystream()\n#  test_ksa()\ntest_rc4()\n\n#  key-scheduling algorithm: initialize the s array\n#  for i = 0 to 255:\n#      s[i] = i\n#  j = 0\n#  for i = 0 to 255:\n#      j = (j + s[i] + key[i mod keylength]) mod 256  -- for us, keylength is 3\n#      swap values of s[i] and s[j]\n\n#  -- pseudo-random generation algorithm: generate byte stream (“pad”) to be xor'd with the ciphertext\n#  i = 0, j = 0\n#  for k = 0 to message_length-1:\n#      i = (i+1) mod 256\n#      j = (j+s[i]) mod 256\n#      swap values of s[i] and s[j]\n#      pad[k] = s[(s[i]+s[j]) mod 256]\n\n#  -- ciphertext xor pad --> plaintext\n#  for k = 0 to message_length-1:\n#      plaintext[k] = pad[k] xor ciphertext[k]  -- xor each byte\n","sub_path":"ksa.py","file_name":"ksa.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"392722625","text":"import os\nimport json\nimport codecs\n\ntemplate = \"\"\"\n<div class=\"card item col-lg-3 col-md-4 col-sm-6 col-12\">\n<h5 class=\"item-title\">{title}</h5>\n<img src=\"img/{name}\" alt=\"image\" />\n<a href=\"img/{name}\" data-lightbox=\"image-{number}\" data-title=\"{description}\" class=\"has-border\">\n</a>\n<div class=\"card-body\">\n<h5 class=\"card-title\">{title}</h5>\n<p class=\"card-text\">{description}</p>\n</div>\n</div>\n\"\"\"\n\n\nwith codecs.open(\"products.json\", \"r\", \"utf-8\") as pf:\n    products = json.load(pf)\nn = 1\nwith codecs.open(\"images.html\", \"w\", \"utf-8\") as wf:\n    for name in os.listdir(\".\"):\n        if name.endswith(\".jpg\"):\n            print(\"write {}\".format(name))\n            title = name.rstrip(\".jpg\")\n            description = \"\"\n            if title in products:\n                description = products[title]\n            p = {\n                \"name\": name,\n                \"title\": title,\n                \"description\": description,\n                \"number\": n\n            }\n            parts = template.format(**p)\n            wf.write(parts)\n            n += 1\n\n\n    ","sub_path":"img/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"539944849","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\n\"\"\"\n\n解析店铺页，获取店铺信息\n\n\"\"\"\n\nimport traceback\nfrom fun import *\n\nstime = int(time.time())\ncdir = os.path.dirname(os.path.abspath(__file__))\nlogfile = cdir + \"/0store.log\"\nfile_write(logfile, \"{}\\n{}\\n\\n\".format(__file__, now()), 'w')\n\ndb = mysqlc.mysql(CONFIG)\nDIC['fp'] = 0\nUARGS = sys.argv\n\ndef main(id, url=0):\n    try:\n        parse_store_page(id, url)\n    except Exception as e:\n        ss = traceback.format_exc()\n        print(\"{} --------------- {}\".format(url, ss))\n\n# url ='https://www.walmart.com/reviews/seller/9474'\n# main(64)\n\n# 单独采集\nif len(UARGS) == 2 and UARGS[1].isdigit():\n    main(UARGS[1])\n    exit('-----------------')\n\nMAXID = 0\ndtime = days_before(14, 1)\n\ncond = \"where id > {} and ( pnum=0 or addtime < '{}')\"\n# cond = 'where id in (57)'\n\nsql0 = \"select count(*) from t_walmart_store1 {}\".format(cond).format(MAXID, dtime)\nsql1 = \"select id from t_walmart_store1 {} limit 10\"\n\nnum = db.field(sql0)\npagesize = 10\npages = math.ceil(num / pagesize)\nprint(\"num: {} pages: {}\\n\".format(num, pages))\nprint(sql0)\n\nfor i in range(1, pages + 1):\n    sql = sql1.format(cond).format(MAXID, dtime)\n    rows = db.fetch(sql)\n    print(rows)\n    if not rows: continue\n    MAXID = max([W[0] for W in rows])\n\n    threads_run(main, rows)\n    print(\"主循环{} 休眠 {}s\".format(i+1, SLEEPTIME))\n    time.sleep(SLEEPTIME)\n\n\netime = time.time() - stime\nrun_time = \"%.2fs \" % etime\nprint(run_time)\n\nlogtxt = \"num: {}, pages: {}\\n\".format(num, pages)\nlogtxt += \"每页耗时: {}, 总耗时: {}, 内存: {}\\n\".format( round(etime/max(pages,1), 2), run_time, mem())\nlogtxt += \"验证码 {}\\n\".format(DIC['yzm'])\nlogtxt += now() + \"\\n\"\nfile_write(logfile, logtxt, 'a')\n","sub_path":"walmart/store.py","file_name":"store.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"345225049","text":"# coding=utf-8\nfrom spider import Spider\nimport config\nimport re\nfrom bs4 import BeautifulSoup\nimport requests\nfrom urllib.parse import unquote\n\nclass Tieba(Spider):\n    def __init__(self,tieba_name):\n        super(Tieba, self).__init__()\n        self.link =\"https://tieba.baidu.com/f?kw=\" + tieba_name + \"&ie=utf-8&pn={}\"\n        self.header = {\n\n        }\n\n    def format_control(self, str):\n        string = re.sub(r'\"','',str)\n        return string\n\n    def tieba(self):\n        def process_tieba(html):\n            titles = re.findall(r'<a rel=\"noreferrer\" [^>]+>',html)\n\n            for i in titles:\n                data_dict = {}\n                if \"j_th_tit\" in i:\n                    title = re.findall(r'title=\"[^>]+\"', str(i))[0]\n                    link = re.findall(r'href=\"[^>]+\"', str(i))[0]\n                    title = title.split(' ')[0]\n                    link = link.split(' ')[0]\n                    title = re.sub(r'title=','',str(title))\n                    link = re.sub(r'href=','',str(link))\n                    title = self.format_control(title)\n                    link = self.format_control(link)\n                    link = 'http://tieba.baidu.com' + link\n\n                elif \"frs-author-name\" in i:\n                    author = re.findall(r'href=\"[^>]+\"', str(i))[0]\n                    author_main_page = author.split(' ')[0]\n                    author_name = re.findall(r'un=[^>]+&ie', str(author_main_page))\n                    if author_name != []:\n                        author_name = author_name[0]\n                        author_name = re.sub(r'un=','',str(author_name))\n                        author_name = re.sub(r'&ie','',str(author_name))\n                        author_name = unquote(author_name, encoding='utf-8')\n                    author_main_page = 'http://tieba.baidu.com' + self.format_control(author_main_page)\n                try:\n                    data_dict[\"title\"] = title\n                    data_dict[\"link\"] = link\n                    data_dict[\"author_main_page\"] = author_main_page\n                    data_dict[\"author\"] = author_name\n                    self.save_thread('tieba',data_dict)\n                except:\n                    pass\n\n        for i in range(0, 100):\n            self.get_random_ip()\n            self.header[\"User-Agent\"] = self.random_select_header(self.usrAgent)\n            response = requests.get(self.link.format(i*50), headers=self.header, proxies = self.proxy)\n            html = str(response.content.decode('utf-8'))\n            process_tieba(html)\n            break\n\n    def save_thread(self, collection_name, data):\n        exist = self.db[collection_name].find({\"title\": data[\"title\"]}).count()\n        if exist == 0:\n            self.db[collection_name].insert(data)\n            print('news {} is saved'.format(data[\"title\"]))\n        else:\n            print('news {} exists'.format(data[\"title\"]))\n\nif __name__ == '__main__':\n    tieba = Tieba('')\n    tieba.tieba()","sub_path":"tieba_spider.py","file_name":"tieba_spider.py","file_ext":"py","file_size_in_byte":2966,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"21537311","text":"import numpy as np\r\nimport pickle\r\nimport time\r\nimport matplotlib.pyplot as plt\r\nimport fullReplication as fr\r\n\r\n\r\ndef scaleFullReplication(numShards, redundancy, sizeShard, sparsity,\r\n                         numEpochs, initBal):\r\n    numNodes = numShards * redundancy\r\n    scaling = numShards.size\r\n\r\n    tVer = np.zeros(scaling)\r\n    tUp = np.zeros(scaling)\r\n    tVote = np.zeros(scaling)\r\n    throughput = np.zeros(scaling)\r\n\r\n    for j in range(scaling):\r\n        tVer[j], tVote[j], tUp[j] = \\\r\n            fullReplicationScalingCore(numNodes[j], numShards[j], sizeShard,\r\n                                       sparsity, numEpochs, initBal)\r\n        throughput[j] = numShards[j] / (tVer[j] + tVote[j])\r\n\r\n    # save the results\r\n    result = {}\r\n    result['verification time'] = tVer\r\n    result['voting time'] = tVote\r\n    result['updating time'] = tUp\r\n    result['throughput'] = throughput\r\n    fileName = 'full_replication_redundancy_' + str(redundancy) +\\\r\n               '_M_' + str(sizeShard) + '_s_' +\\\r\n               str(sparsity) + '_numEpochs_' + str(numEpochs) + '_' +\\\r\n               str(int(time.time())) + '.pickle'\r\n    with open(fileName, 'wb') as handle:\r\n        pickle.dump(result, handle)\r\n\r\n    return tVer, tVote, tUp, throughput, fileName\r\n\r\n\r\ndef fullReplicationScalingCore(numNodes, numShards, sizeShard, sparsity,\r\n                               numEpochs, initBal):\r\n    '''\r\n    This function simulates the verification process of a full replication\r\n    scheme in a single epoch, with numEpochs blocks verified in each chain\r\n    '''\r\n\r\n    # initialize the system after numEpochs epoches\r\n    chainLength = numEpochs - 1\r\n    chains = chainInit(numShards, sizeShard, sparsity, chainLength, initBal)\r\n    tVer = np.zeros(numEpochs)\r\n    tUp = np.zeros(numEpochs)\r\n    tVote = np.zeros(numEpochs)\r\n\r\n    tVerMax, tVerMedian, tVote, tUp = \\\r\n        fr.simEpoch(chains, tVer, tUp, tVote, numNodes, numShards,\r\n                    sizeShard, sparsity, numEpochs - 1, initBal / numEpochs)\r\n\r\n    return tVerMax, tVerMedian, tVote, tUp\r\n\r\n\r\ndef chainInit(numShards, sizeShard, sparsity, chainLength, initBal):\r\n    chains = fr.chainInit(numShards, sizeShard, initBal)\r\n    txCap = initBal / chainLength / 2\r\n    for k in range(numShards):\r\n        for e in range(chainLength):\r\n            chains[k] = np.vstack([chains[k],\r\n                                   fr.blockGenCore(sizeShard, sparsity,\r\n                                                   txCap)])\r\n    return chains\r\n\r\n\r\ndef scaleSimpleSharding(numShards, redundancy, sizeShard, sparsity, numEpochs,\r\n                        initBal):\r\n    numNodes = numShards * redundancy\r\n    scaling = numShards.size\r\n\r\n    tVer = np.zeros(scaling)\r\n    tUp = np.zeros(scaling)\r\n    tVote = np.zeros(scaling)\r\n    throughput = np.zeros(scaling)\r\n\r\n    for j in range(scaling):\r\n        tVer[j], tVote[j], tUp[j] = \\\r\n            simpleShardingScalingCore(numNodes[j], numShards[j], sizeShard,\r\n                                      sparsity, numEpochs, initBal)\r\n        throughput[j] = numShards[j] / (tVer[j] + tVote[j])\r\n\r\n    # save the results\r\n    result = {}\r\n    result['verification time'] = tVer\r\n    result['voting time'] = tVote\r\n    result['updating time'] = tUp\r\n    result['throughput'] = throughput\r\n    fileName = 'simple_sharding_redundancy_' + str(redundancy) +\\\r\n               '_M_' + str(sizeShard) + '_s_' +\\\r\n               str(sparsity) + '_numEpochs_' + str(numEpochs) + '_' +\\\r\n               str(int(time.time())) + '.pickle'\r\n    with open(fileName, 'wb') as handle:\r\n        pickle.dump(result, handle)\r\n\r\n    return tVer, tVote, tUp, throughput, fileName\r\n\r\n\r\ndef simpleShardingScalingCore(numNodes, numShards, sizeShard, sparsity,\r\n                              numEpochs, initBal):\r\n    assert numNodes % numShards == 0\r\n\r\n    # the number of nodes that repeats a shard.\r\n    numRep = int(numNodes / numShards)\r\n    tVerMax = []\r\n    tVerMedian = []\r\n    tVote = []\r\n    tUp = []\r\n    for k in range(numShards):\r\n        t1, t2, t3, t4 = \\\r\n            fullReplicationScalingCore(numNodes=numRep, numShards=1,\r\n                                       sizeShard=sizeShard, sparsity=sparsity,\r\n                                       numEpochs=numEpochs, initBal=initBal)\r\n\r\n        tVerMax.append(t1)\r\n        tVerMedian.append(t2)\r\n        tVote.append(t3)\r\n        tUp.append(t4)\r\n    return np.max(tVerMax), np.median(tVerMedian), np.max(tVote), \\\r\n        np.median(tVote), np.max(tUp), np.mean(tUp)\r\n\r\n\r\ndef plots(fileName, numNodes):\r\n    print(fileName)\r\n    with open(fileName, 'rb') as handle:\r\n        result = pickle.load(handle)\r\n    for key in result.keys():\r\n        if key == 'throughput':\r\n            plt.plot(numNodes, result[key], label=key)\r\n    plt.xlabel('Number of nodes')\r\n    plt.ylabel('Throughput (# of blocks per sec)')\r\n    plt.grid()\r\n    plt.legend(loc='best')\r\n    plt.title('Data source:\\n' + fileName)\r\n    plt.xlim((0, None))\r\n    plt.ylim((0, None))\r\n    plt.tight_layout()\r\n    plt.show()\r\n","sub_path":"fullReplicationScaling.py","file_name":"fullReplicationScaling.py","file_ext":"py","file_size_in_byte":5039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"496749668","text":"# Definition for a binary tree node.\n\n\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution:\n    def depthOfNode(self, node: TreeNode):\n        if node is None:\n            return -1\n\n        depth = max(self.depthOfNode(node.left), self.depthOfNode(node.right)) + 1\n\n        return depth\n\n    def diameterOfBinaryTree(self, root: TreeNode) -> int:\n        if root is None:\n            return 0\n\n        diameter = max(\n            self.diameterOfBinaryTree(root.left),\n            self.diameterOfBinaryTree(root.right),\n            self.depthOfNode(root.left) + self.depthOfNode(root.right) + 2\n        )\n        return diameter\n\n\nif __name__ == '__main__':\n    nodes = [TreeNode(i) for i in range(1, 6)]\n    root = nodes[0]\n    root.left = nodes[1]\n    root.right = nodes[2]\n    nodes[1].left = nodes[3]\n    nodes[1].right = nodes[4]\n    solution = Solution()\n    assert solution.diameterOfBinaryTree(root) == 3\n","sub_path":"0543_diameter_of_binary_tree.py","file_name":"0543_diameter_of_binary_tree.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"210068372","text":"#with log transformation of target and rf ensembling\r\nimport sys\r\npath = 'E://utils'\r\nsys.path.append(path)\r\n\r\nimport common_utils  as utils\r\nimport regression_utils as rutils\r\nfrom sklearn import metrics, ensemble, model_selection, neighbors, linear_model, decomposition, manifold\r\nimport math\r\nimport pandas as pd\r\nimport os\r\nimport seaborn as sns\r\nimport numpy as np\r\n\r\ndef log_rmse(y_orig, y_pred):\r\n    return math.sqrt(metrics.mean_squared_log_error(y_orig,y_pred) )\r\n\r\ndef rmse(y_orig, y_pred):\r\n    return math.sqrt(metrics.mean_squared_error(y_orig,y_pred) )\r\n\r\n\r\npath = 'E://'\r\nhouse_train = pd.read_csv(os.path.join(path,\"house_train.csv\"))\r\nhouse_train.shape\r\nhouse_train.info()\r\n\r\nhouse_test = pd.read_csv(os.path.join(path,\"house_test.csv\"))\r\nhouse_test.shape\r\nhouse_test.info()\r\n\r\nhouse = pd.concat((house_train, house_test), axis = 0)\r\nhouse.shape\r\nhouse.info()\r\n\r\nprint(utils.get_continuous_features(house))\r\nprint(utils.get_non_continuous_features(house))\r\n\r\nsns.countplot(x='YearBuilt',data=house_train)\r\nsns.jointplot(x=\"SalePrice\", y=\"YearBuilt\", data=house_train)\r\nsns.FacetGrid(house_train, hue=\"YearBuilt\",size=8).map(sns.kdeplot, \"SalePrice\").add_legend()\r\n\r\nsns.countplot(x='YrSold',data=house_train)\r\nsns.jointplot(x=\"SalePrice\", y=\"YrSold\", data=house_train)\r\nsns.FacetGrid(house_train, hue=\"YrSold\",size=8).map(sns.kdeplot, \"SalePrice\").add_legend()\r\n\r\nfeatures_to_cast = ['MSSubClass']\r\nutils.cast_to_cat(house, features_to_cast)\r\n\r\nfeatures_to_drop = ['Id']\r\nmissing_features_above_th = utils.get_features_to_drop_on_missingdata(house, 0.25)\r\nfeatures_to_drop.extend(missing_features_above_th)\r\nhouse1 = utils.drop_features(house, features_to_drop)\r\nhouse1.info()\r\n\r\nimputable_cat_features = utils.get_non_continuous_features(house1)\r\ncat_imputer = utils.get_categorical_imputers(house1, imputable_cat_features)\r\nhouse1[imputable_cat_features] = cat_imputer.transform(house1[imputable_cat_features])\r\n\r\nimputable_cont_features = utils.get_continuous_features(house1)\r\ncont_imputer = utils.get_continuous_imputers(house1, imputable_cont_features)\r\nhouse1[imputable_cont_features] = cont_imputer.transform(house1[imputable_cont_features])\r\nhouse1.info()\r\n\r\nhouse2 = utils.ohe(house1, imputable_cat_features)\r\n\r\nscaler = utils.get_scaler(house2)\r\nhouse3 = scaler.transform(house2)\r\nhouse3 = pd.DataFrame(house3, columns=house2.columns)\r\n\r\nX_train = house3[:house_train.shape[0]]\r\ny_train = house_train['SalePrice']\r\n\r\nlasso_selector = linear_model.Lasso()\r\nlasso_selector.fit(X_train, y_train)\r\nprint(lasso_selector.coef_)\r\nutils.plot_feature_importances(lasso_selector, X_train, 40)\r\n\r\nX_train1 = utils.select_features(lasso_selector, X_train)\r\n\r\nutils.corr_heatmap(X_train1)\r\nlpca = decomposition.PCA(0.95)\r\nlpca.fit(X_train1)\r\nprint(np.cumsum(lpca.explained_variance_ratio_))\r\npca_data = lpca.transform(X_train1)\r\nprint(pca_data.shape)\r\n\r\ntsne = manifold.TSNE(n_components=2)\r\ntsne_data = tsne.fit_transform(pca_data)\r\nrutils.plot_data_3d_regression(tsne_data, y_train)\r\n\r\nscoring = metrics.make_scorer(log_rmse, greater_is_better=False)\r\n\r\nsns.distplot(y_train)\r\ny_trans = np.log1p(y_train)\r\nsns.distplot(y_trans)\r\n\r\nrf_estimator = ensemble.RandomForestRegressor(random_state=100)\r\nrf_grid = {'n_estimators':list(range(100,501,200)), 'max_features':[8, 10, 15], 'max_depth':[3,5,7]}\r\nfinal_rf_model = utils.grid_search_best_model(rf_estimator, rf_grid, pca_data, y_trans, scoring = scoring)\r\n\r\nX_test = house3[house_train.shape[0]:]\r\nX_test1 = utils.select_features(lasso_selector, X_test)\r\npca_test_data = lpca.transform(X_test1)\r\npca_test_data.shape\r\n\r\nhouse_test['SalePrice'] = np.expm1(final_rf_model.predict(pca_test_data))\r\nhouse_test.to_csv(\"C:\\\\Users\\\\Algorithmica\\\\Downloads\\\\submission.csv\", columns=[\"Id\", \"SalePrice\"], index=False)\r\n","sub_path":"2019-may/14.regression(kaggle)/5.house price prediction.py","file_name":"5.house price prediction.py","file_ext":"py","file_size_in_byte":3777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"300543099","text":"from PIL import Image\nimport numpy as np\n\nclass Solution:\n\tdef __init__(self):\n\t\tpass\n\n\tdef binarization(self, image):\n\t\tpixels = image.load()\n\t\tbin_image = Image.new('1', image.size)\n\t\tbin_pixels = bin_image.load()\n\t\th, w = image.size\n\t\tfor i in range(h):\n\t\t\tfor j in range(w):\n\t\t\t\tif pixels[i, j] >= 128:\n\t\t\t\t\tbin_pixels[i, j] = 1\n\t\t\t\telse:\n\t\t\t\t\tbin_pixels[i, j] = 0\n\n\t\treturn bin_image\n\n\tdef down_sampling(self, image, origin_size, target_size):\n\t\tpixels = image.load()\n\t\tnew_image = Image.new(image.mode, (target_size, target_size))\n\t\tnew_pixels = new_image.load()\n\t\tfor i in range(target_size):\n\t\t\tfor j in range(target_size):\n\t\t\t\tnew_pixels[i, j] = pixels[i * int(origin_size/target_size), j * int(origin_size/target_size)]\n\t\treturn new_image\n\n\n\tdef h(self, b, c, d, e):\n\t\tif b != c: return 's'\n\t\tif b == d and b == e: return 'r'\n\t\treturn 'q'\n\n\tdef valid(self, new_x, new_y, h, w):\n\t\treturn new_x >= 0 and new_x < h and new_y >= 0 and new_y < w\n\n\tdef check(self, image, cor, x, y):\n\t\th, w = image.size\n\t\tpixels = image.load()\n\t\tnew_x, new_y = x + cor[0], y + cor[1]\n\t\tif self.valid(new_x, new_y, h, w): return pixels[new_x, new_y]\n\t\treturn 0\n\n\tdef Yokoi_num(self, image, corners):\n\t\tpixels = image.load()\n\t\tres = []\n\t\th, w = image.size\n\t\tfor y in range(w):\n\t\t\trow = []\n\t\t\tfor x in range(h):\n\t\t\t\tif pixels[x, y] == 0:\n\t\t\t\t\trow.append(' ')\n\t\t\t\t\tcontinue\n\t\t\t\telse:\n\t\t\t\t\ttemp = []\n\t\t\t\t\tfor cor in corners:\n\t\t\t\t\t\tb = pixels[x, y]\n\t\t\t\t\t\tc, d, e = 0, 0, 0\n\t\t\t\t\t\tc = self.check(image, cor[0], x, y)\n\t\t\t\t\t\td = self.check(image, cor[1], x, y)\n\t\t\t\t\t\te = self.check(image, cor[2], x, y)\n\t\t\t\t\t\ttemp.append(self.h(b,c,d,e))\n\t\t\t\t\tif temp.count('r') == 4:\n\t\t\t\t\t\trow.append('5')\n\t\t\t\t\telse:\n\t\t\t\t\t\tif temp.count('q') == 0:\n\t\t\t\t\t\t\trow.append(' ')\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\trow.append(str(temp.count('q')))\n\t\t\tres.append(row)\n\t\treturn np.array(res)\n\n\tdef save(self, save_name, res):\n\t\tfile = open(save_name, \"w\")\n\t\twith open(save_name, \"w\") as file:\n\t\t\tfor i in range(res.shape[0]):\n\t\t\t\tfor j in range(res.shape[1]):\n\t\t\t\t\tfile.write(res[i, j])\n\t\t\t\tfile.write('\\n')\n\n\n\tdef problem1(self, path, out_file, corners):\n\t\timage = Image.open(path)\n\t\tdown_sampleing_image = self.down_sampling(image, 512, 64)\n\t\tdown_sampleing_image.save('down_sampling.bmp')\n\t\tbin_image = self.binarization(down_sampleing_image)\n\t\tbin_image.save('bin_image.bmp')\n\t\tyokoi = self.Yokoi_num(bin_image, corners)\n\t\tself.save(out_file, yokoi)\n\n\n\nif __name__ == '__main__':\n\tcorners = [ [( 1,  0), ( 1, -1), ( 0, -1)], \n\t\t\t\t[( 0, -1), (-1, -1), (-1,  0)], \n\t\t\t\t[(-1,  0), (-1,  1), ( 0,  1)], \n\t\t\t\t[( 0,  1), ( 1,  1), ( 1,  0)] ]\n\tpath = 'lena.bmp'\n\tout_file = 'lena_yokoi.txt'\n\tSolution().problem1(path, out_file, corners)\n\n\n\n\n\n\n\n","sub_path":"hw6/R08922A04_HW6_ver1/hw6.py","file_name":"hw6.py","file_ext":"py","file_size_in_byte":2677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"391739891","text":"import numpy as np\nfrom dtw import dtw\nfrom scipy.spatial.distance import euclidean\nimport sys\n######################### K-Medoids\ndef dist(xa,xb):\n        return np.sqrt(np.sum(np.square(xa-xb), axis=-1))\n##        return np.array(map(lambda x,y:dtw(x, y, dist=euclidean),xa,xb))\nclass k_Medoids():\n    def __init__(self,data,k=12,batch_size=1000,max_iterators=20):\n        self.data=data\n        self.k=k\n        self.batch_size=batch_size\n        self.max_iterators=max_iterators\n\n        #compute dis for each pairs\n        print(\"compute dis for each pairs......\")\n        self.datalens=len(data)\n        \n    def assign_nearest(self,ids_of_mediods):\n        dists = dist(self.data[:,None,:], self.data[None,ids_of_mediods,:])\n        return np.argmin(dists, axis=1)\n\n    def find_medoids(self,assignments,ids_of_medoids):\n        medoid_ids = np.full(self.k, -1, dtype=int)\n        if self.batch_size:  #is using greedy algorithm ? 0 is not using\n            subset = np.random.choice(self.datalens, self.batch_size, replace=False)\n        for i in range(self.k):\n            if self.batch_size:\n                indices = np.union1d(np.intersect1d(np.where(assignments==i)[0], subset),np.array([ids_of_medoids[i]]))\n            else:\n                indices = np.where(assignments==i)[0]\n    ##        distances = dist(x[indices, None, :], x[None, indices, :]).sum(axis=0)\n\n            distances = dist(self.data[indices, None, :], self.data[None, indices, :]).sum(axis=0)\n            medoid_ids[i] = indices[np.argmin(distances)]\n        return medoid_ids\n\n    def kmeds(self):\n        print(\"Initializing to random medoids.\")\n        ids_of_medoids = np.random.choice(self.datalens, self.k, replace=False)\n        class_assignments = self.assign_nearest(ids_of_medoids)\n\n        for i in range(self.max_iterators):\n            print(\"\\tFinding new medoids.\")\n            ids_of_medoids = self.find_medoids(class_assignments,ids_of_medoids)\n            print(\"\\tReassigning points.\")\n            new_class_assignments = self.assign_nearest(ids_of_medoids)\n\n            diffs = np.mean(new_class_assignments != class_assignments)\n            class_assignments = new_class_assignments\n\n            print(\"iteration {:2d}: {:.2%} of points got reassigned.\"\n                  \"\".format(i, diffs))\n            if diffs <= 0.01:\n                break\n        return class_assignments, ids_of_medoids\n\n# ## Generate Fake Data\n# print(\"Initializing Data.\")\n# ds = 3\n# ks = 12\n# ns = ks * 10000\n# #generate test data......\n# data = np.random.normal(size=(ns, ds))\n# for kk in range(ks):\n#     dd = (kk-1)%ds\n#     data[kk*ns//ks:(kk+1)*ns//ks,dd] += 3*ds*kk\n# ##compute dis for each pairs\n# ##print(\"compute dis for each pairs......\")\n# ##pair_dis = pairwise_distances(x, metric=dist)\n\n# ## doing the k-medoids clustering....\n# print(\"doing Kmedoids.....\")\n# t = k_Medoids(data)\n# final_assignments, final_medoid_ids = t.kmeds()\n\n","sub_path":"k_medoids_primary.py","file_name":"k_medoids_primary.py","file_ext":"py","file_size_in_byte":2928,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"289822960","text":"#!/usr/bin/env python3\nfrom flask import Flask, jsonify\n\napp = Flask(__name__)\n\nproducts = [\n    {\n        'id': 1,\n        'name': u'Jeans', \n    },\n    {\n        'id': 2,\n        'name': u'Shirt', \n    }\n]\n\n@app.route('/products', methods=['GET'])\ndef get_products():\n    return jsonify(products)\n\n@app.route('/products/<id>', methods=['GET'])\ndef get_product(id):\n    product = [p for p in products if p['id'] == int(id)]\n    return jsonify(product[0])\n\nif __name__ == '__main__':\n    print('Product service listening on port 8007!')\n    app.run(port=8007)","sub_path":"product_service.py","file_name":"product_service.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"260539144","text":"import numpy as np\nimport pandas as pd\nimport os\nimport sys\nfrom collections import OrderedDict as odict\n\nfrom util.mnkhelpers import bits2boards as b2b\n\n# This is almost pretty now! So excited!\n\nclass Data():\n    \"\"\"\n    Data is the primary object for holding experimental data.\n    It also contains functions for the loading, cleaning, augmentation, and\n    export of the data tables.\n    \"\"\"\n\n    def __init__(self, folder):\n        self.original_columns = [\n            'index', 'subject', 'color', 'gi', 'mi', 'status', 'bp', 'wp',\n            'response', 'rt', 'time', 'mouse_t', 'mouse_x'\n        ]\n\n        self.full_output_columns = [\n            'subject', 'is_comp', 'color', 'status', 'bp', 'wp', 'response',\n            'rt', 'gi', 'mi', 'computer', 'human', 'time',\n            'a', 'b', 'aval', 'bval', 'val', 'condition', 'group', 'session'\n        ]\n\n        self.game_model_columns = [\n            'subject', 'color', 'bp', 'wp', 'response', 'rt',\n            'condition', 'group', 'session'\n        ]\n\n        self.afc2_model_columns = [\n            'subject', 'color', 'bp', 'wp', 'response', 'rt',\n            'a', 'b', 'aval', 'bval', 'condition', 'group', 'session'\n        ]\n\n        self.eval_model_columns = [\n            'subject', 'color', 'bp', 'wp', 'response', 'rt',\n            'val', 'condition', 'group', 'session'\n        ]\n\n\n        self.group_ref = {\n            0: [0, 1, 2, 3, 4],\n            1: [0, 1, 4, 3, 2],\n            2: [2, 1, 0, 3, 4],\n            3: [4, 1, 0, 3, 2],\n            4: [2, 1, 4, 3, 0],\n            5: [4, 1, 2, 3, 0]\n        }\n\n        self.groups = {\n            'CE':0, 'GL':1, 'TQ':2, 'SW':3, 'LM':4, 'XW':5,\n            'XM':0, 'XZ':1, 'KL':2, 'MS':3, 'AB':4, 'VC':5,\n            'GB':0, 'HS':1, 'YV':2, 'YZ':3, 'EN':4, 'SL':5,\n            'IK':0, 'LW':1, 'EW':2, 'AB2':3, 'JC':4, 'OR':5,\n            'QC':0, 'JP':1, 'LH':2, 'XG':3, 'SG':4, 'IS':5\n       }\n\n        self.group_chart = pd.DataFrame(\n            index=self.groups.keys(),\n            columns=['a', '2', 'b', '4', 'c']\n        )\n\n        reffer = lambda x: self.group_ref[self.groups[x]]\n        for s in self.group_chart.index:\n            self.group_chart.loc[s, :] = reffer(s)\n\n        self.objective_errors, self.positions = self.load_boards()\n        self.data = self.load(folder)\n        self.append_errors(self.data)\n\n\n    def load_boards(self):\n        oe = pd.read_csv('util/objective_errors.csv')\n        oe.loc[:, 'bp'] = oe.loc[:, '0_pieces'].map(b2b)\n        oe.loc[:, 'wp'] = oe.loc[:, '1_pieces'].map(b2b)\n        oe = oe.loc[:, ['bp', 'wp', 'color', 'Game_theoretic_value', 'Confirmed', 'value_Zeyan ', 'confirmed_Zeyan']]\n        oe.columns = ['bp', 'wp', 'color', 'gtv', 'gtv_c', 'zv', 'zv_c']\n\n        po_cols = ['bp', 'wp', 'a', 'aval', 'b', 'bval', 'c', 'mu']\n        po = pd.read_csv('../1_gen/experiment_boards_new.txt', sep='\\t', names=po_cols)\n        po.loc[:, 'attempts'] = 0\n        po.loc[:, 'errors'] = 0\n\n        return oe, po\n\n    def load_file(self, folder, file_name, mouse=False):\n        \"\"\" Initial preparation of data for individual files \"\"\"\n\n        # load file, drop nuissance columns, remove non-observations\n        drop_cols = ['index'] if mouse else ['index', 'mouse_t', 'mouse_x']\n        data = pd.read_csv(folder + file_name, names=self.original_columns).drop(drop_cols, axis=1)\n        drop_status = (data.status != 'dummy') &  (data.status != 'ready') & (data.status != 'draw offer')\n        data = data.loc[drop_status, :].copy().reset_index(drop=True)\n\n        # assign unique subject label (from filename) and create separate cols for humans and computers\n        sub_filter = data.rt > 0\n        comp_filter = data.rt == 0\n        first_move_filter = (data.mi == 1) & (data.gi%2 == 0)\n        second_move_filter = (data.mi == 2) & (data.gi%2 == 0)\n\n        print(file_name[10:-4])\n        data.loc[sub_filter, 'subject'] = file_name[10:-4]\n        data.loc[:, 'human'] = file_name[10:-4]\n        data.loc[:, 'condition'] = file_name[8]\n        data.loc[:, 'group'] = self.groups[file_name[10:-4]]\n        print(self.group_chart.loc[file_name[10:-4], file_name[8]])\n        data.loc[:, 'session'] = self.group_chart.loc[file_name[10:-4], file_name[8]]\n        data.loc[:, 'computer'] = np.nan\n        data.loc[comp_filter, 'computer'] = data.loc[comp_filter, 'subject']\n        data.loc[first_move_filter, 'computer'] = data.loc[second_move_filter, 'computer']\n        data.loc[:, 'computer'] = data.loc[:, 'computer'].fillna(method='ffill')\n        data.loc[0, 'computer'] = data.loc[1, 'computer']\n\n        return data\n\n    def load(self, folder):\n        \"\"\" Calls other functions to corrale data and some support information \"\"\"\n        print('Loading data...')\n        self.exp_name = folder\n        files = os.listdir(folder + '/Raw/')\n        files = [f for f in files if f[-3:] == 'csv']\n        self.subjects = np.unique([f[10:-4] for f in files])\n        self.subject_dict = odict(zip(self.subjects, np.arange(len(self.subjects))))\n        data = pd.concat([self.load_file(folder + '/Raw/', f) for f in files]).reset_index(drop=True)\n        data = self.clean(data)\n\n        return data\n\n    def clean(self, df):\n        print('More cleaning...')\n        \"\"\" Performs further cleaning that can be done on all data collectively \"\"\"\n\n        # anonymize subjects\n        sub_filter = df.rt > 0 # filter computers out\n        df.loc[sub_filter, 'subject'] = df.loc[sub_filter, 'subject'].map(self.subject_dict)\n        df.loc[:, 'human'] = df.loc[:, 'human'].map(self.subject_dict)\n\n        # give computers identifiable names\n        comp_filter = df.rt == 0\n        df.loc[comp_filter, 'subject'] = df.loc[comp_filter, 'subject'].astype(int) + 1000\n        df.loc[pd.notnull(df.computer), 'computer'] = df.loc[pd.notnull(df.computer), 'computer'].astype(int) + 1000\n\n        # force remove response from board\n        for i in df.loc[df.status != 'EVAL', :].index.values:\n            if df.loc[i,\"color\"] == 0:\n                l = list(df.loc[i,\"bp\"])\n                l[df.loc[i, \"response\"]] = '0'\n                df.loc[i,\"bp\"] = ''.join(l)\n            else:\n                l = list(df.loc[i,\"wp\"])\n                l[df.loc[i,\"response\"]] = '0'\n                df.loc[i,\"wp\"] = ''.join(l)\n\n        # force correct colors\n        count_pieces = lambda x: np.array([np.array(list(df.loc[i, x])).astype(int).sum() for i in df.index.values])\n        df.loc[:, 'color'] = count_pieces('bp') - count_pieces('wp')\n        df.loc[:, 'color'] = df.loc[:, 'color'].astype(int).astype(str)\n\n        # add is_comp\n        is_computer = lambda x: \"0\" if x > 0 else \"1\"\n        df.loc[:, 'is_comp'] = df.loc[:, 'rt'].map(is_computer)\n\n        # correct move index in games\n        df.loc[df.status.isin(['playing', 'win', 'draw']), 'mi'] = df.loc[df.status.isin(['playing', 'win', 'draw']), 'mi'] - 1\n        return df\n\n    def append_errors(self, df):\n        print('Appending errors...')\n        new_cols = ['a', 'b', 'aval', 'bval', 'val']\n        for i in new_cols:\n            df.loc[:, i] = np.nan\n        d = df.loc[:, :]\n\n        for obs in d.index.values:\n            bm1 = (self.positions.bp == d.loc[obs, 'bp'])\n            bm2 = (self.positions.wp == d.loc[obs, 'wp'])\n            board_match_selector = bm1 & bm2\n            c = self.positions.loc[board_match_selector, :]\n\n            if len(c) > 1:\n                c = int(c.loc[c.index.values[0], :])\n            if len(c) == 1:\n                for h in new_cols[:-1]:\n                    df.loc[obs, h] = int(c.loc[:, h].values[0])\n\n            bm1 = (self.objective_errors.bp == d.loc[obs, 'bp'])\n            bm2 = (self.objective_errors.wp == d.loc[obs, 'wp'])\n            board_match_selector = bm1 & bm2\n            o = self.objective_errors.loc[board_match_selector, :]\n\n            if len(c) > 1:\n                o = o.loc[o.index.values[0], :]\n            if len(c) == 1:\n                df.loc[obs, 'val'] = o.loc[:, 'gtv'].values[0]\n\n        return None\n\n    def export_individuals(self, folder):\n        print('Exporting individual trials...')\n        for s, i in self.subject_dict.items():\n            c = self.data.human == i\n            d = self.data.loc[c, :].reset_index(drop=True)\n            d = d.reindex_axis(self.full_output_columns, axis=1)\n            d.to_csv(folder + '/Clean/' + s + '.csv', index=False)\n\n        return None\n\n    def export_tasks(self, folder):\n        print('Exporting by task...')\n        # separate tables by task\n        game_selector = self.data.loc[:, 'status'].isin(['playing', 'win', 'draw'])\n        afc2_selector = self.data.loc[:, 'status'] == 'AFC2'\n        eval_selector = self.data.loc[:, 'status'] == 'EVAL'\n\n        game = self.data.loc[game_selector, :]\n        afc2 = self.data.loc[afc2_selector, :]\n        eva  = self.data.loc[eval_selector, :]\n        print(eva.head(5))\n\n        game.loc[:, self.full_output_columns].to_csv(folder + '/Clean/_summaries/all_games_all_fields.csv', index=False)\n        game.loc[:, self.game_model_columns].to_csv(folder + '/Clean/_summaries/all_games_model_input.csv', header=False, index=False)\n        game.loc[game.rt > 0, self.game_model_columns].to_csv(folder + '/Clean/_summaries/all_games_model_input_no_comp.csv', header=False, index=False)\n        afc2.loc[:, self.full_output_columns].to_csv(folder + '/Clean/_summaries/all_afc2s_all_fields.csv', index=False)\n        afc2.loc[:, self.afc2_model_columns].to_csv(folder + '/Clean/_summaries/all_afc2s_model_input.csv', header=False, index=False)\n        eva.loc[:, self.full_output_columns].to_csv(folder + '/Clean/_summaries/all_evals_all_fields.csv', index=False)\n        eva.loc[:, self.eval_model_columns].to_csv(folder + '/Clean/_summaries/all_evals_model_input.csv', header=False, index=False)\n\n        return None\n\ndef main(folder):\n    D = Data(folder)\n    D.export_individuals(folder)\n    D.export_tasks(folder)\n    return None\n\nif __name__ == '__main__':\n    f = sys.argv[1]\n    main(f)\n","sub_path":"clean_learning.py","file_name":"clean_learning.py","file_ext":"py","file_size_in_byte":9999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"426680880","text":"def sort(lst): \n    if len(lst)==1:\n        return lst\n    else:\n        left, right = lst[:len(lst)//2], lst[len(lst)//2:]\n        return merge(sort(left), sort(right))\n\ndef merge(left, right):\n    merged = []\n    i = 0\n    j = 0\n    for k in range(2*len(right)):\n        if left[i] < right[j]:\n            merged.append(left[i])\n            i += 1\n            if i == len(left):\n                merged = merged + right[j:]\n                break\n        else:\n            merged.append(right[j])\n            j += 1\n            if j == len(right):\n                merged = merged + left[i:]\n                break\n    return merged","sub_path":"Wk2/mergesort.py","file_name":"mergesort.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"611373224","text":"from django.urls import path, re_path\nfrom . import views\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom django.conf import settings\nfrom django.conf.urls.static import static\n\n\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('articles/', views.articles, name='articles'),\n    path('404/', views.get_404, name='404'),\n    path('about/', views.about, name='about'),\n    path('contacts/', views.contacts, name='contacts'),\n    path('advertising/', views.advertising, name='advertising'),\n    path('sitemap/', views.sitemap, name='sitemap'),\n    path('robots.txt/', views.get_robots_page, name='robots'),\n    re_path(r'share/$', views.share, name='share'),\n    path('search/', views.search_results, name='search'),\n    path('<str:slug>/', views.rubric_page, name='rubric-page'),\n    path('<str:slug>/add_posts/', views.add_posts, name='add-posts-page'),\n    path('<str:rubric>/<str:slug>/', views.single_post, name='post-page'),\n    path('<str:rubric>/<str:slug>/rating/', views.get_rating, name='rating-page'),\n]\n# ] + static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n\n\n\n# if settings.DEBUG:\n#     urlpatterns += patterns('django.contrib.staticfiles.views',\n#         url(r'^static/(?P<path>.*)$', 'serve'),\n#     )\n\nurlpatterns += staticfiles_urlpatterns()\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)","sub_path":"catalog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"586550187","text":"# made by Eloise Bisbee 8/13/19 eloise.bisbee@tufts.edu\n# revised by Brian Ozarowicz - January 2020\n\n# This program takes in files numbered 1-numfiles with a .txt extension\n# as well as ground_truth_ratings.csv and outputs clean_comedy_data.csv\n\n# The numbered files should contain praat output data formatted as follows:\n# first line: full file path of which folder the data was extracted from\n# then a series of couplings:\n# pause_#.mp3\n# intensity\n# intensity std\n# pitch\n# pitch std\n# min\n# max\n\n# ground_truth_ratings.csv should contain the ground truth ratings for each comedy\n# performance's jokes formatted such that the first columns are:\n# PerformanceId, Performance, JokeId, Joke, HumanScore, HumanScorePostJokeOnly\n\n# The output, clean_comedy_data.csv, will contain columns:\n# PerformanceId, JokeId, Pitch, PitchStd, Intensity, IntensityStd,\n# MinSound, MaxSound, HumanScore, HumanScorePostJokeOnly\n\n# This program is designed to be run on a Windows computer,\n# differences in file path format on another OS may cause errors.\n# Requires Python 3.5 or later\n\nimport re\nimport csv\nimport json\nimport os\n\n# use performanceLog json file to list joke names in order they were told in the performance\njoke_order = []\nnumfiles = len([f for f in os.scandir(os.getcwd()) if f.name.endswith('.txt')]) # check how many txt files the praat script created\nfor i in range(1, numfiles + 1):\n    joke_performance = []\n    file = open(str(i)+\".txt\", \"r\", encoding='utf_16_le')\n    path = file.read().splitlines()[0][:-16] # get path to the performance directory\n    file.close()\n    json_name = [i for i in os.listdir(path) if os.path.isfile(os.path.join(path,i)) and 'performanceLog' in i] # find name of json file\n    json_path = path+json_name[0] # form path to json file\n    json_file = open(json_path, \"r\")\n    json_data = json.loads(json_file.read().splitlines()[0]) # read performanceLog json\n    json_file.close()\n    skips = [\"/../sounds/tags/family_joke_negative.ogg.wav\"] # skip unused joke names\n    if \"audio\" in json_data[2].keys():\n        skips.append(\"/../sounds/robot_name_joke_1.ogg.wav\")\n    for entry in json_data:\n        keys = entry.keys()\n        if (\"audio\" in keys) and (entry.get(\"audio\") not in skips):\n            joke_name = entry.get(\"audio\")[11:] # find all joke names in the json and save them in order\n            joke_performance.append(joke_name)\n    joke_order.append(joke_performance)\n    #print(joke_order) # uncomment to verify the resulting list\n\n# reading in data from praat files and storing in dictionary called all_data\nall_data = {}\nfor fi in range(1, numfiles + 1):\n    filename = str(fi) + \".txt\"\n    praat = open(filename, \"r\", encoding='utf_16_le')\n    raw_praat_data = praat.read().splitlines()\n    praat_data = {}\n    to_add = {}\n    to_add[\"performance\"] = raw_praat_data[0].split(\"\\\\\")[-2] # gets performance name from the directory path\n    to_add[\"data\"] = praat_data\n    features_per_pause = 43\n    for i in range(0, len(raw_praat_data)):\n        if raw_praat_data[i][0] == 'p' or raw_praat_data[i][0] == 'j':\n            row_data = []\n            for j in range(0,features_per_pause):\n                neg = re.findall(r'-', raw_praat_data[i + j + 1])\n                raw = re.findall(r'\\d*\\.?\\d+', raw_praat_data[i + j + 1])\n                if len(raw) != 0:\n                    if len(neg) != 0:\n                        if float(raw_praat_data[i + j + 1]) > 0:\n                            print(raw_praat_data[i + j + 1])\n                        row_data.append(-1 * float(raw[0]))\n                    else:\n                        row_data.append(float(raw[0]))\n                else:\n                    row_data.append(0)\n            temp = re.findall(r'\\d+', raw_praat_data[i])\n            index = int(temp[0])\n            praat_data[index] = row_data\n    all_data[fi] = to_add\n    praat.close()\n\n# reading in data from csv file and storing in dictionary called csv_data\ncsv_data = {}\nwith open('ground_truth_ratings.csv') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=',')\n    data_cats = []\n    count = 0\n    curr = \"none\"\n    temp = {}\n    for row in csv_reader:\n        if count == 0:\n            data_cats = row\n        else:\n            if curr != row[1]:\n                curr = row[1]\n                temp = {}\n                csv_data[curr] = temp\n                temp['PerformanceId'] = int(row[0])\n                temp['Performance'] = row[1]\n                temp['Jokes'] = []\n                temp['Jokes'].append([int(row[2]), row[4], row[5], row[3]])\n            else:\n                temp['Jokes'].append([int(row[2]), row[4], row[5], row[3]])\n        count += 1\n\n# output new csv file with all appropriate data\nwith open('clean_comedy_data.csv', mode='w', newline='\\n', encoding='utf-8') as csv_file:\n    fieldnames = ['PerformanceId', 'JokeId', 'Pitch', 'PitchSd', 'PitchMax', 'Intensity', \n                  'IntensitySd', 'MinSound', 'MaxSound', 'HumanScore', 'HumanScorePostJokeOnly',\n                  '1_mean', '1_max', '1_min',\n                  '2_mean', '2_max', '2_min',\n                  '3_mean', '3_max', '3_min',\n                  '4_mean', '4_max', '4_min',\n                  '5_mean', '5_max', '5_min',\n                  '6_mean', '6_max', '6_min',\n                  '7_mean', '7_max', '7_min',\n                  '8_mean', '8_max', '8_min',\n                  '9_mean', '9_max', '9_min',\n                  '10_mean', '10_max', '10_min',\n                  '11_mean', '11_max', '11_min',\n                  '12_mean', '12_max', '12_min',]\n    writer = csv.DictWriter(csv_file, fieldnames=fieldnames)\n    writer.writeheader()\n    for elem in all_data:\n        idx = all_data[elem]['performance']\n        if (len(all_data[elem]['data']) == len(csv_data[str(idx)][\"Jokes\"])):\n            for i in range(0, len(all_data[elem]['data'])):\n                perf_id = csv_data[idx]['PerformanceId']\n                cur_joke_name = csv_data[idx]['Jokes'][i][3] # check name of the joke being matched to a praat data txt file\n                pause_num = joke_order[perf_id].index(cur_joke_name) # find that name in the ordered list, its index is its pause number\n                row = {}\n                row[\"PerformanceId\"] = csv_data[idx]['PerformanceId']\n                row['JokeId'] = csv_data[idx]['Jokes'][i][0]\n                row['Intensity'] = all_data[elem]['data'][pause_num][0]\n                row['IntensitySd'] = all_data[elem]['data'][pause_num][1]\n                row['MinSound'] = all_data[elem]['data'][pause_num][2]\n                row['MaxSound'] = all_data[elem]['data'][pause_num][3]\n                row['Pitch'] = all_data[elem]['data'][pause_num][4]\n                row['PitchSd'] = all_data[elem]['data'][pause_num][5]\n                row['PitchMax'] = all_data[elem]['data'][pause_num][6]\n                row['1_mean'] = all_data[elem]['data'][pause_num][7]\n                row['1_max'] = all_data[elem]['data'][pause_num][8]\n                row['1_min'] = all_data[elem]['data'][pause_num][9]\n                row['2_mean'] = all_data[elem]['data'][pause_num][10]\n                row['2_max'] = all_data[elem]['data'][pause_num][11]\n                row['2_min'] = all_data[elem]['data'][pause_num][12]\n                row['3_mean'] = all_data[elem]['data'][pause_num][13]\n                row['3_max'] = all_data[elem]['data'][pause_num][14]\n                row['3_min'] = all_data[elem]['data'][pause_num][15]\n                row['4_mean'] = all_data[elem]['data'][pause_num][16]\n                row['4_max'] = all_data[elem]['data'][pause_num][17]\n                row['4_min'] = all_data[elem]['data'][pause_num][18]\n                row['5_mean'] = all_data[elem]['data'][pause_num][19]\n                row['5_max'] = all_data[elem]['data'][pause_num][20]\n                row['5_min'] = all_data[elem]['data'][pause_num][21]\n                row['6_mean'] = all_data[elem]['data'][pause_num][22]\n                row['6_max'] = all_data[elem]['data'][pause_num][23]\n                row['6_min'] = all_data[elem]['data'][pause_num][24]\n                row['7_mean'] = all_data[elem]['data'][pause_num][25]\n                row['7_max'] = all_data[elem]['data'][pause_num][26]\n                row['7_min'] = all_data[elem]['data'][pause_num][27]\n                row['8_mean'] = all_data[elem]['data'][pause_num][28]\n                row['8_max'] = all_data[elem]['data'][pause_num][29]\n                row['8_min'] = all_data[elem]['data'][pause_num][30]\n                row['9_mean'] = all_data[elem]['data'][pause_num][31]\n                row['9_max'] = all_data[elem]['data'][pause_num][32]\n                row['9_min'] = all_data[elem]['data'][pause_num][33]\n                row['10_mean'] = all_data[elem]['data'][pause_num][34]\n                row['10_max'] = all_data[elem]['data'][pause_num][35]\n                row['10_min'] = all_data[elem]['data'][pause_num][36]\n                row['11_mean'] = all_data[elem]['data'][pause_num][37]\n                row['11_max'] = all_data[elem]['data'][pause_num][38]\n                row['11_min'] = all_data[elem]['data'][pause_num][39]\n                row['12_mean'] = all_data[elem]['data'][pause_num][40]\n                row['12_max'] = all_data[elem]['data'][pause_num][41]\n                row['12_min'] = all_data[elem]['data'][pause_num][42]\n                row['HumanScore'] = int(float(csv_data[idx]['Jokes'][i][1]))\n                row['HumanScorePostJokeOnly'] = int(float(csv_data[idx]['Jokes'][i][2]))\n                writer.writerow(row)\n        else:\n            #this else case is for if the raw praat data lengths and ground\n            #truth lengths which should correspond to the same performance \n            #are not the same length. This means something went wrong\n            print(len(all_data[elem]['data']), len(csv_data[str(idx)][\"Jokes\"]))\n","sub_path":"MachineLearning/mfcc_to_comedy_data.py","file_name":"mfcc_to_comedy_data.py","file_ext":"py","file_size_in_byte":9827,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"390643693","text":"# Edgar Barrera / Github: https://github.com/EdgarCastillo101/EdgarCastillo101\n# Copyright (c) 2021 Edgar Barrera\n\nimport csv\n\ndef csvRead(filePath):\n    with open(filePath) as fd:\n        reader = csv.reader(fd, delimiter = ',')\n        for row in reader:\n            print(row[0] + ' ' + row[1])\n\ndef csvWrite(filePath, data):\n    with open(filePath, 'a') as fd:\n        writer = csv.writer(fd, delimiter=',', quoting=csv.QUOTE_NONNUMERIC)\n        writer.writerow(data)\n\nif __name__ == '__main__':\n    # data = ['Firstname', 'Lastname']\n    # csvWrite('example.csv', data)\n    userInput = input('What is your Fullname? ')\n    userInput = userInput.split(' ')\n    csvWrite('example.csv', userInput)\n    csvRead('example.csv')\n","sub_path":"Python Programs & Projects/P54_PythonCSV.py","file_name":"P54_PythonCSV.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"561300060","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\n\nimport requests\nimport re\nimport pandas\nimport numpy\n\nfrom bs4 import BeautifulSoup\nzhaobiao_url = 'http://www.ccgp-hubei.gov.cn:8050/quSer/search'\nsheng_url = 'http://www.ccgp-hubei.gov.cn/notice/cggg/pzhbgg/index_1.html'\npage_path1 ='http://www.ccgp-hubei.gov.cn/notice/cggg/pzhbgg//'\nshi_url =   'http://www.ccgp-hubei.gov.cn/notice/cggg/czbgg/index_1.html'\npage_path2 ='http://www.ccgp-hubei.gov.cn/notice/cggg/czhbgg//'\nbase_path='http://www.ccgp-hubei.gov.cn/'\ndef download_page(url):\n\theaders = {\n\t'User-Agent':'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.105 Safari/537.36'\n\t}\n\ttry:\n\t\tdata = requests.get(url, headers = headers).content\n\t\treturn data\n\texcept ConnectionError:\n\t\tprint(\"connection error\")\n\texcept:\n\t\tprint(\"try again\")\n\treturn None\n\ndef paser_html(html):\n\tsoup = BeautifulSoup(html, 'html.parser')\n\tul_lists = soup.find_all(\"ul\")\n\tif len(ul_lists)!=0:\n\t\tres=ul_lists[0]\n\telse:\n\t\treturn \"no match content\"\n\tfor i in ul_lists:\n\t\tif \"news-list-content\" in  i['class']:\n\t\t\tres = i\n\t\t\tbreak\n\treturn res\n\ndef init_page(html):\n\tsoup = BeautifulSoup(html, 'html.parser')\n\tul_lists = soup.find_all(\"ul\")\n\tif len(ul_lists)!=0:\n\t\tres = ul_lists[0]\n\telse:\n\t\treturn \"no match content\"\n\tfor i in ul_lists:\n\t\tif \"pagination\" in i['class']:\n\t\t\tpage_string=i.find_all('li')[-1].string\n\t\t\tpage_nums=int(str(page_string).split('/')[1].split('页')[0])\n\t\t\tbreak\n\treturn page_nums\n\ndef detail_page(html,title):\n\tsoup = BeautifulSoup(html,'html.parser')\n\tcondidate_p = soup.find_all(\"p\")\n\tdetail_data={\"title\" : title}\n\tfor p in condidate_p:\n\t\tif p.find('table'):\n\t\t\ttmp=[s.extract() for s in p('table')]\n\t\tstr_p=p.text.strip('\\r\\n\\t').strip('\\xa0').strip().split('：')\n\t\tif(len(str_p)<2):\n\t\t\tcontinue\n\t\tkey = str_p[-2]\n\t\tvalues=str_p[-1]\n\t\tdetail_data[key]=values\n\t\t# pattern=re.compile('供应商名称')\n\t\t# element=soup.find_all(string=pattern)\n\treturn  detail_data\n\ndef main():\n\t#print(paser_html(download_page(shi_url)))\n\tkey_words='医院'\n\tdata=[]\n\t# project_id=0\n\tpage_path = page_path1\n\t#初始化、首次访问后确定总共的页数，然后确定范围\n\tnums = init_page(download_page(page_path+'index_1.html'))\n\tfor num in range(nums):\n\t\tfinal_page = page_path+'index_'+str(num+1)+'.html'\n\t\tnews_list=paser_html(download_page(final_page)).find_all('li')\n\t\tfor link in news_list:\n\t\t\ttitle=str(link).split(']')[2].split('<')[0]\n\t\t\ttitle=link.text\n\t\t\tif(\"医院\" in title):\n\t\t\t\tdetail_page_address=base_path+link.a['href']\n\t\t\t\tdata.append(detail_page(download_page(detail_page_address),title))\n\t\t\t\t\n\t# workbook = xlsxwriter.Workbook('./rec_data.xlsx')\n\t# worksheet = workbook.add_worksheet()\n\t# bold_format = workbook.add_format({'bold': True})\n\theads=['项目名称','供应商名称','供应商地址','中标金额','购买物品名称','时间','联系人','联系方式']\n\tf = open(\"./data3.txt\",'a',encoding='utf-8')\n\tfor i in data:\n\t\tf.write(str(i)+'\\n')\n\t# print(paser_html(download_page(zhaobiao_url)))\n\tf.close()\n\nif __name__ == '__main__':\n\tmain()","sub_path":"notes/test/爬虫/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"419848153","text":"#!/usr/bin/env python3 -tt\n\n#modules\nfrom tkinter import *\nfrom tkinter import ttk\nimport random\n\n#CONSTANTS\nGRID_SIZE = (50,50)\nHEIGHT = GRID_SIZE[1] * 7\nWIDTH = GRID_SIZE[0] * 14\nDEF_SPAWN_POS = (WIDTH+GRID_SIZE[0],HEIGHT-GRID_SIZE[1]/2)\nOBS_TYPE = {1:{'pos':DEF_SPAWN_POS,\n\t             'size':(50,50)},\n\t          2:{'pos':(DEF_SPAWN_POS[0],DEF_SPAWN_POS[1]-GRID_SIZE[1]/2),\n\t\t\t\t\t\t\t 'size':(50,100)},\n\t\t\t\t\t\t3:{'pos':(DEF_SPAWN_POS[0],DEF_SPAWN_POS[1]-GRID_SIZE[1]*2),\n\t\t\t\t\t\t\t 'size':(50,50)}}\n\n#globals\ntick = 0 #game play time\nin_play = False\nobstacles = [] #list of obstacle instances\n\n#class definitions\nclass Obstacle:\n  def __init__(self, size, pos, vel=(0,0)):\n    self.pos = [pos[0],pos[1]]\n    self.vel = [vel[0],vel[1]]\n    self.size = size\n    self.obs_id = None\n\n  def draw(self, canvas):\n    self.obs_id = draw_rectangle(canvas, self.pos, self.size)\n\n  def destroy(self):\n    canvas.delete(self.obs_id)\n\n  def in_frame(self):\n    if self.pos[0]+self.size[0] > 0:\n      return True\n    self.destroy()\n    return False\n\n  def update(self):\n    self.pos[0] += self.vel[0]\n    self.pos[1] += self.vel[1]\n    if self.obs_id != None:\n      canvas.coords(self.obs_id, self.pos[0]-self.size[0]/2,self.pos[1]-self.size[1]/2,self.pos[0]+self.size[0]/2,self.pos[1]+self.size[1]/2)\n\n  def __str__(self):\n    return \"An obstacle is spawned with- \\npos:\"+str(self.pos)+\"\\nvel:\"+str(self.vel)+\"\\nsize:\"+str(self.size)\n\n#helper function\ndef draw_rectangle(canvas, pos, size):\n  return canvas.create_rectangle(pos[0]-size[0]/2,pos[1]-size[1]/2,pos[0]+size[0]/2,pos[1]+size[1]/2)\n\ndef timer():\n  global tick\n  if in_play:\n    tick += 1\n    update()\n    root.after(1, timer)\n\ndef update():\n  print(len(obstacles))\n  for obs in obstacles:\n    if obs.in_frame():\n      obs.update()\n    else:\n      obstacles.remove(obs)\n\n#event handlers\ndef start():\n  global in_play\n  if not in_play:\n    in_play = True\n    root.after(0, timer)\n  else:\n    print(\"Game already in play\")\n\ndef stop():\n  global in_play\n  if in_play: in_play = False\n  print(\"Game stopped\")\n\ndef spawn():\n  global obstacles\n  if in_play:\n    i,j = random.randrange(1,14), random.randrange(1,7)\n    obs_info = OBS_TYPE[random.randrange(1,4)]\n    obs = Obstacle(obs_info['size'],obs_info['pos'],(-10,0))\n    obs.draw(canvas)\n    print(obs)\n    obstacles.append(obs)\n\nroot = Tk()\ncontrol_frame = ttk.Frame(root)\ncontrol_frame.pack(side=\"left\")\ncanvas_frame = ttk.Frame(root)\ncanvas_frame.pack(side=\"left\")\n\nttk.Button(control_frame, text=\"start\", command=start).pack()\nttk.Button(control_frame, text=\"stop\", command=stop).pack()\nttk.Button(control_frame, text=\"spawn\", command=spawn).pack()\ncanvas = Canvas(canvas_frame, width=WIDTH, height=HEIGHT, bd=0, highlightthickness=0)\ncanvas.pack()\n\nroot.mainloop()\n","sub_path":"coursera_interactive_python/miniprojects/finalproject/obstacles.py","file_name":"obstacles.py","file_ext":"py","file_size_in_byte":2764,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"603465850","text":"\"\"\"\nThis module is used to calculate the weighted clumpiness error for\nthe actively modelled land-use classes for two input maps.\n\"\"\"\n\nfrom read_map import read_map\nimport numpy as np\nfrom clumpy_module import clumpiness_index\n\n\ndef w_clu_error(map1, map2, mask, weights, luc, pas, act):\n    # Calculate the clumpiness.\n    map1_clumpiness = clumpiness_index(map1, mask, luc)\n    map2_clumpiness = clumpiness_index(map2, mask, luc)\n    clu_error = [0] * luc\n    for i in range(0, luc):\n        clu_error[i] = abs(map1_clumpiness[i] - map2_clumpiness[i])\n    # Generate the error values.\n    weighted_clu_error = [0] * act\n    for i in range(0, act):\n        weighted_clu_error[i] = weights[i] * clu_error[i + pas]\n    # Calculate the weighted clumpiness error.\n    WCE = sum(weighted_clu_error)\n    return WCE\n\n# Module test.\n\"\"\"\namap_path = (\"C:\\\\Users\\\\charl\\\\Dropbox\\\\PhD\\\\Journal Papers\\\\\"\n             \"3. Integrated Automated Calibration\\\\Results\\\\\"\n             \"2 Output_maps\\\\Data_maps\\\\madrid_2000.asc\")\nsmap_path = (\"C:\\\\Users\\\\charl\\\\Dropbox\\\\PhD\\\\Journal Papers\\\\\"\n             \"3. Integrated Automated Calibration\\\\Results\\\\\"\n             \"2 Output_maps\\\\Seeded\\\\Seed_cal_map_5.rst\")\nmask_path = (\"C:\\\\Users\\\\charl\\\\Dropbox\\\\PhD\\\\Journal Papers\\\\\"\n             \"3. Integrated Automated Calibration\\\\Results\\\\\"\n             \"2 Output_maps\\\\Data_maps\\\\madrid_mask.asc\")\n# Read in the maps\nsmap = read_map(smap_path)\namap = read_map(amap_path)\nmask = read_map(mask_path)\n# Determine the map properties\nmap_dimensions = np.shape(amap)\nrows = map_dimensions[0]\ncols = map_dimensions[1]\nluc = np.max(amap) + 1\npas = 1\nfea = 5\nact = luc - (pas + fea)\n\nweights = [1/11, 1/11, 1/11, 1/11, 2/11, 2/11, 2/11, 1/11]\n\nWCE = w_clu_error(amap, smap, mask, weights, luc, pas, act)\nprint(WCE)\n\"\"\"","sub_path":"weighted_clu.py","file_name":"weighted_clu.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"613221050","text":"#!/usr/bin/env python3\n# -*- coding: utf8 -*-\n\nimport os\nimport mimetypes\nimport uuid\nfrom urllib.parse import urljoin\n\nimport requests\n\n\nclass Huntflow:\n    base_url = 'https://api.huntflow.ru/'\n\n    def __init__(self, token):\n        self.token = token\n        self.headers = {\n            'user-agent': 'test_task (gsouth@gmail.com)',\n            'authorization': 'Bearer {}'.format(self.token)\n            }\n\n    @classmethod\n    def _get_url(cls, entry_point):\n        return urljoin(cls.base_url, entry_point)\n\n    @staticmethod\n    def _handle_response(response):\n        if response.ok:\n            return response\n        else:\n            print('Error: Unexpected response. Status code: {}'.format(response.status_code))\n\n    def _get(self, url):\n        try:\n            response = self._handle_response(requests.get(url, headers=self.headers))\n            return response\n        except requests.RequestException as e:\n            print('Error: {}'.format(e))\n\n    def _post(self, url, data=None, files=None, headers=None):\n        try:\n            headers = self.headers if not headers else headers\n            response = self._handle_response(\n                requests.post(url, headers=headers, json=data, files=files))\n            return response\n        except requests.RequestException as e:\n            print('Error: {}'.format(e))\n\n    def get_accounts(self):\n        url = self._get_url('accounts')\n        response = self._get(url)\n        return response.json()\n\n    def get_sources(self, account_id):\n        url = self._get_url('account/{}/applicant/sources'.format(account_id))\n        response = self._get(url)\n        return response.json()\n\n    def get_statuses(self, account_id):\n        url = self._get_url('account/{}/vacancy/statuses'.format(account_id))\n        response = self._get(url)\n        return response.json()\n\n    def get_vacancies(self, account_id):\n        url = self._get_url('account/{}/vacancies'.format(account_id))\n        response = self._get(url)\n        return response.json()\n\n    def send_applicant(self, account_id, applicant_data):\n        url = self._get_url('account/{}/applicants'.format(account_id))\n        response = self._post(url, applicant_data)\n        return response.json()\n\n    def add_applicant_to_vacancy(self, account_id, applicant_id, vacancy_data):\n        url = self._get_url('account/{}/applicants/{}/vacancy'.format(account_id, applicant_id))\n        response = self._post(url, vacancy_data)\n        return response.json()\n\n    def send_file(self, account_id, application_path):\n        if application_path and os.path.exists(application_path):\n            url = self._get_url('account/{}/upload').format(account_id)\n            filename, extension = os.path.basename(application_path).split('.')\n            mimetype, encoding = mimetypes.guess_type(application_path)\n            with open(application_path, 'rb') as file_:\n                files = {\n                    'file': ('{}.{}'.format(uuid.uuid4().hex, extension), file_, mimetype)\n                }\n                headers = self.headers.copy()\n                headers['X-File-Parse'] = 'true'\n                response = self._post(url, headers=headers, files=files)\n                return response.json()\n","sub_path":"huntflow/huntflow.py","file_name":"huntflow.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"219937367","text":"import sys\nfrom os import listdir\nsys.path.insert(0, \"../../pyfunctor\")\nimport csv_handler as csv_handler\nimport transform as transform\n\n\ndataset = sys.argv[1]\ninput_dir = \"./deft_corpus/data/deft_files/\" + dataset\noutput_path = dataset + \".csv\"\n\nlabel_map = {\"B\" : 0, \"I\" : 1, \"O\" : 2}\n\ndef distill(input_path, label_map):\n    dataset = csv_handler.csv_readlines(input_path, delimit='\\t')\n    dataset = transform.filter_func(dataset, lambda row : len(row) >= 5)\n    token_label = transform.map_func(dataset, lambda row : [row[0].strip(), label_map[row[4].strip()[0]]])\n    return token_label\n\nfinal = []\nfor file_name in listdir(input_dir):\n    token_label = distill(input_dir + \"/\" + file_name, label_map)\n    final += token_label\n\nfinal = transform.indexleft_flat_func(final)\ncsv_handler.csv_writelines(output_path, final)\n","sub_path":"appendix/BIO/bio_convert.py","file_name":"bio_convert.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"16061495","text":"\nconfig = {}\nwith open(\"config.txt\",\"r\") as f:\n    config_file = f.read()\n\nfor line in config_file.split(\"\\n\"):\n    line_data = line.split(\"=\")\n    config[line_data[0]] = line_data[1]\n\nprint(config)\n\n# formating index html\nwith open(\"index.html\",\"r\") as f:\n    result = \"\"\n    index_content = f.read()\n    for line in index_content.split(\"\\n\"):\n        if \"<title>\" in line:\n            result+=f\"<title>{config['title']}</title>\\n\"\n        if '<meta name=\"description\"' in line:\n            result+=f'<meta name=\"description\" content=\"{config[\"description\"]}\" />\\n'\n        else:\n            result+=line+\"\\n\"\n\nwith open(\"index.html\",\"w\") as f:\n    f.write(result)\n\n#######################\n\n# formating main chunk js\nwith open(\"static/js/main.chunk.js\",\"r\") as f:\n    result = \"\"\n    main_chunk_content = f.read()\n    for line in main_chunk_content.split(\"\\n\"):\n        if \"Y = new v.PublicKey\" in line:\n            result+=f'Y = new v.PublicKey(\"{config[\"address\"]}\"),\\n'\n        if 'children: [Object(B.jsx)(\"h1\"' in line:\n            result+='children: [Object(B.jsx)(\"h1\", {'+f'children: \"{config[\"h1\"]}\"\\n'\n        else:\n            result+=line+\"\\n\"\n\nwith open(\"static/js/main.chunk.js\",\"w\") as f:\n    f.write(result)\n\n\"\"\"\n# formating chunk js\nwith open(\"static/js/chunk.js\",\"r\") as f:\n    chunk_content = f.read()\n    for line in chunk_content.split(\"\\n\"):\n        pass\n#####################\n\"\"\"","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"491844073","text":"#!/usr/bin/python\nimport yaml\n\nclass yconfig:\n    print('Attempting to load config file')\n    with open('config.yaml') as f:\n        scfg=yaml.safe_load(f)\n        f.close\n    #print (\"This is from inside config.py\")\n    #print(scfg)\n","sub_path":"server/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"19966432","text":"from __future__ import absolute_import\nfrom ..layers.convolutional import Convolution2D\nfrom .. import backend as K\n\nclass DilatedConvolution2D(Convolution2D):\n    '''\n      implment the dilated convolution2d, where the dilate = (1,1) means\n      no dilation \n    '''\n    def __init__(self, nb_filter, nb_row, nb_col, \n                 init='glorot_uniform', activation='linear', weights=None,\n                 border_mode='valid', subsample=(1, 1), dilate = (1,1), dim_ordering='th',\n                 W_regularizer=None, b_regularizer=None, activity_regularizer=None,\n                 W_constraint=None, b_constraint=None,\n                 bias=True, **kwargs):\n\n        self.dilate = dilate\n        super(DilatedConvolution2D, self).__init__(nb_filter, nb_row, nb_col, **kwargs)\n\n    def build(self, input_shape):\n        super(DilatedConvolution2D, self).build(input_shape)\n\n    def get_output_shape_for(self, input_shape):\n        if self.dim_ordering == 'th':\n            rows = input_shape[2]\n            cols = input_shape[3]\n        elif self.dim_ordering == 'tf':\n            rows = input_shape[1]\n            cols = input_shape[2]\n        else:\n            raise Exception('Invalid dim_ordering: ' + self.dim_ordering)\n\n        rows = conv_output_length(rows, self.nb_row ,\n                                  self.border_mode, self.subsample[0])\n        cols = conv_output_length(cols, self.nb_col ,\n                                  self.border_mode, self.subsample[1])\n\n        if self.dim_ordering == 'th':\n            if dilate == (1,1):\n                return (input_shape[0], self.nb_filter, rows, cols)\n            else:\n                batchsize = input_shape[0]\n                return ((batchsize, self.nb_filter) +\n                        tuple(conv_output_length(input, (filter-1) * dilate + 1, 1, 0)\n                              for input, filter, dilate\n                              in zip(input_shape[2:], self.nb_col,\n                                     self.dilate)))\n\n        elif self.dim_ordering == 'tf':\n            return (input_shape[0], rows, cols, self.nb_filter)\n        else:\n            raise Exception('Invalid dim_ordering: ' + self.dim_ordering)\n\n    def call(self, x, mask=None):\n        if self.dilate == (1,1) :\n            output = K.conv2d(x, self.W, strides=self.subsample,\n                          border_mode=self.border_mode,\n                          dim_ordering=self.dim_ordering,\n                          filter_shape=self.W_shape)\n        else:\n            output = K.dilconv2d(x, self.W, rate = self.dilate, strides=self.subsample,\n                          border_mode=self.border_mode,\n                          dim_ordering=self.dim_ordering,\n                          filter_shape=self.W_shape)\n\n        if self.bias:\n            if self.dim_ordering == 'th':\n                output += K.reshape(self.b, (1, self.nb_filter, 1, 1))\n            elif self.dim_ordering == 'tf':\n                output += K.reshape(self.b, (1, 1, 1, self.nb_filter))\n            else:\n                raise Exception('Invalid dim_ordering: ' + self.dim_ordering)\n        output = self.activation(output)\n        return output\n\n    def get_config(self):\n        config = { 'dilate': self.dilate}\n        base_config = super(DilatedConvolution2D, self).get_config()\n        return dict(list(base_config.items()) + list(config.items()))\n","sub_path":"DilatedConvolution2D.py","file_name":"DilatedConvolution2D.py","file_ext":"py","file_size_in_byte":3377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"274356843","text":"#!/usr/bin/env python\r\n# Author: liu_feng\r\n# Date: 2015-06-11\r\n\r\nfrom libcom.device_adapt.device.device import Device\r\n\r\n__all__ = [\"dev_M78C\"]\r\n\r\n# set device name (same as the 'hostname' of the device) and console\r\nhost = '192.168.204.124'\r\nport = 2003\r\ndev_name = 'Ruijie'\r\n\r\ndev_M78C = Device(dev_name)\r\ndev_M78C.config_telnet(host, port)\r\n\r\ndev_M78C.add_mac('00.d0.f8.22.34.34')\r\ndev_M78C.add_mgmt('mgmt', 'mgmt 0', '192.168.204.66', '192.168.204.37', '255.255.255.0')\r\n\r\ndev_M78C.add_card('1/M2', 1, 31, 'M7800C-CM', 0)\r\n\r\n# mapping of interface key and interface name\r\ndev_M78C.add_intf('intf1', 'GigabitEthernet 2/1', '2')\r\ndev_M78C.add_intf('intf2', 'GigabitEthernet 2/3', '2')\r\ndev_M78C.add_intf('intf3', 'GigabitEthernet 2/5', '2')\r\ndev_M78C.add_intf('intf4', 'GigabitEthernet 2/7', '2')\r\ndev_M78C.add_intf('intf5', 'GigabitEthernet 2/9', '2')\r\ndev_M78C.add_intf('intf6', 'GigabitEthernet 2/11', '2')\r\n\r\n\r\n","sub_path":"dev_cfg/switches/M78C.py","file_name":"M78C.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"332304278","text":"import logging, logging.handlers\nimport sys\nimport getpass\n\nclass Additional_logs(object):\n    def __init__(self, filename, level):\n        self.fileName = filename\n        self.Level = level\n        self.formatter = logging.Formatter(Additional_logs.formaTter['format4'])\n\n    def additional_log(self):\n        logging.basicConfig(filename=self.fileName, level=self.Level)\n        self.mylogger = logging.getLogger(__file__)\n        #self.handler = logging.StreamHandler()\n        self.handler = logging.FileHandler(self.fileName)\n        #self.formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(funcName)s():%(lineno)d\\t%(message)s')\n        self.handler.setFormatter(self.formatter)\n        self.mylogger.addHandler(self.handler)\n        return self.mylogger\n\n    formaTter = {\n        'format1' : '%(asctime)s - %(name)s - %(levelname)s - %(funcName)s():%(lineno)d\\t%(message)s' ,\n        'format2' : '%(asctime)s %(levelname)s: %(message)s',\n\t'format3' : '%(asctime)s %(levelname)s - %(funcName)s():%(lineno)d\\t%(message)s',\n        'format4' : '%(asctime)s - %(pathname)s - %(levelname)s - %(module)s.%(funcName)s(): %(message)s',\n        'format5' : '%(process)s -%(asctime)s - %(name)s - %(levelname)s -%(module)s.%(funcName)s :%(message)s'\n    }\n","sub_path":"karan/captive-portal-aoe/captive-portal-aoe/pyAOE/utils/additional_logging.py","file_name":"additional_logging.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"516365837","text":"#!/usr/bin/env python\n# coding=UTF-8\n\n__author__ = \"Pierre-Yves Langlois\"\n__copyright__ = \"https://github.com/pylanglois/uwsa/blob/master/LICENCE\"\n__credits__ = [\"Pierre-Yves Langlois\"]\n__license__ = \"BSD\"\n__maintainer__ = \"Pierre-Yves Langlois\"\n\n\nfrom uwsas.common import *\nfrom uwsas.core import L\n\nfrom uwsas.commands.command_manager import cmanager\nfrom uwsas.commands.install import InstallCommand\nfrom uwsas.helpers import files\nfrom uwsas.core import CONFIG\n\nclass InstallMuninNodeCommand(InstallCommand):\n\n    NAME = 'munin_node'\n    LOCIP = '\\nallow ^127\\.0\\.0\\.1$\\n'\n\n    def __init__(self):\n        InstallCommand.__init__(self)\n        self.packages = \"munin-node\"\n        self.add_package(self.packages)\n        self.add_file('/etc/munin/munin-node.conf', \n                        ck_func=self.check_munin_master_ip, \n                        fix_func=self.fix_munin_master_ip)\n        self.master_ip = self.LOCIP\n        conf = CONFIG.get()\n        if not CONFIG.is_set(self.NAME,'master_ip'):\n            self.master_ip = None\n        else:\n            self.master_ip = conf[self.NAME]['master_ip']\n\n    def check_munin_master_ip(self, element):\n        is_ok = True\n        if self.master_ip:\n            mip = \"\\nallow ^%s$\\n\" % self.master_ip.replace('.','\\.')\n            is_ok &= files.contains(element['name'],mip)\n\n        return is_ok\n\n    def fix_munin_master_ip(self, element):\n\n        if self.master_ip:\n            mip = \"allow ^%s$\\n\" % self.master_ip.replace('.','\\.')\n            loc_n_mip = LOCIP + mip\n            if not files.contains(element['name'], loc_n_mip):\n                files.replace_in(element['name'],\n                                self.LOCIP, \n                                loc_n_mip)\n\n\ncmanager.get(InstallCommand.NAME).register(InstallMuninNodeCommand.NAME, InstallMuninNodeCommand())\n","sub_path":"uwsas/commands/install_munin_node.py","file_name":"install_munin_node.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"464416655","text":"from django.shortcuts import render, redirect\n\n# Create your views here.\nfrom .models import Member\nfrom electeeManagement.models import Electee, Requirements, Social, Service_Hours\nfrom .forms import MemberForm\nfrom hknWebsiteProject.resume_zip import zip_resumes\nimport string\nfrom string import ascii_uppercase\nimport collections\nfrom hknWebsiteProject.utils import get_members_with_complete_profile\n\ndef member_list(request):\n\tif request.user.is_anonymous():\n\t\tcontext = {\n\t\t\t'error' : True,\n\t\t\t'error_msg' : 'You must be a member to see member\\'s profiles'\n\t\t}\n\telse :\n\t\talpha_list = collections.OrderedDict()\n\n\t\t# displays the list of all members who have a complete profile\n\t\tmembers_with_completed_profile = get_members_with_complete_profile()\n\n\t\t# separate member list by letter first name starts with in order to display \n\t\t# \tmembers in these groupings\n\t\tfor letter in ascii_uppercase:\n\t\t\tmember_list = members_with_completed_profile.filter(first_name__startswith=letter)\n\t\t\tmember_list = member_list.order_by('first_name','last_name')\n\t\t\tif member_list:\n\t\t\t\talpha_list[letter] = member_list\n\n\t\tcontext = {\n\t\t\t'member_list' : alpha_list,\n\t\t\t'error' : False,\n\t\t}\n\n\treturn render(request, \"member_list.html\", context)\n\n\ndef profile(request, uniqname, profile_saved=0):\n\tcontext = {}\n\n\tif request.user.is_anonymous():\n\t\tcontext = {\n\t\t\t'error' : True,\n\t\t\t'error_msg' : 'You must be a member to see member\\'s profiles'\n\t\t}\n\telse:\n\t\tis_curr_user = (request.user.username == uniqname)\n\n\t\tm = Member.objects.get(uniqname = uniqname)\n\t\tlogged_in_as = Member.objects.get(uniqname = request.user.username)\n\n\t\telectee_progress = m.is_electee() and (is_curr_user or logged_in_as.is_officer())\n\t\t\n\t\tif electee_progress:\n\t\t\te = Electee.objects.get(member_id = uniqname)\n\t\t\trequirements = dict ((requirements.requirement, requirements) for requirements in Requirements.objects.all())\n\t\t\tsocials = Social.objects.filter(electee_id = uniqname).order_by('-timestamp')\n\t\t\tprojects = Service_Hours.objects.filter(electee_id = uniqname).order_by('-timestamp')\n\n\t\t\tcontext = {\n\t\t\t\t'e' : e,\n\t\t\t\t'requirements' : requirements,\n\t\t\t\t'submit' : False,\n\t\t\t\t'socials' : socials,\n\t\t\t\t'projects' : projects\n\t\t\t}\n\n\t\t\t# if the request user is viewing their own electee progress, \n\t\t\t# \tshow the buttons to submit socials and service hours\n\t\t\tif (uniqname == request.user.username):\n\t\t\t\tcontext['submit'] = True\n\n\t\tcontext['profile'] = m\n\t\tcontext['is_curr_user'] = is_curr_user\n\t\tcontext['profile_saved'] = profile_saved\n\t\tcontext['electee_progress'] = electee_progress\n\t\tcontext['error'] = False\n\n\treturn render(request, \"profile.html\", context)\n\ndef profile_edit(request, uniqname):\n\tcontext = {}\n\tis_curr_user = (request.user.username == uniqname)\n\tif request.user.is_anonymous() or not is_curr_user:\n\t\tcontext = {\n\t\t\t'error' : True,\n\t\t\t'error_msg' : 'You cannot edit this profile'\n\t\t}\n\telse:\n\t\tcontext['profile_saved'] = False\n\t\t\n\t\tm = Member.objects.get(uniqname = uniqname)\n\t\tlogged_in_as = Member.objects.get(uniqname = request.user.username)\n\t\tform = MemberForm(instance = m)\t\n\n\t\tif request.POST:\n\t\t\tform = MemberForm(request.POST, request.FILES, instance = m)\n\t\t\tif form.is_valid():\n\t\t\t\tform.save()\n\t\t\t\tzip_resumes()\n\t\t\n\t\t\t\treturn redirect('profile', uniqname=uniqname, profile_saved=1)\n\n\t\tcontext['form'] = form\n\n\treturn render(request, \"profile_edit.html\", context)\n","sub_path":"src/users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3354,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"262022644","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom constant import *\nfrom lib import *\nplt.rc('text', usetex= True)\nplt.rc('font', family='serif')\n\n\n\nax = plt.subplot(111)\n\n\nXe129 = XENON(ATOM_TABLE['129Xe'], 40 * M_GEV)\nXe131 = XENON(ATOM_TABLE['131Xe'], 40 * M_GEV)\nx = np.linspace(0.1, 100, 100)\ne = x * KEV\ny = np.array([Xe129.SD_DifRate(ee, TYPE = 'p', XSEC0 = 3E-43) + Xe131.SD_DifRate(ee, TYPE = 'p', XSEC0 = 3E-43) for ee in e]) * KEV * 5 * YEAR / 365\nline1, = ax.semilogy(x, y, 'b--', linewidth = 1.5)\ny = np.array([Xe129.SD_DifRate(ee, TYPE = 'n', XSEC0 = 1E-44) + Xe131.SD_DifRate(ee, TYPE = 'n', XSEC0 = 1E-44) for ee in e]) * KEV * 5 * YEAR / 365\nline2, = ax.semilogy(x, y, 'b-', linewidth = 1.5)\n\nXe129 = XENON(ATOM_TABLE['129Xe'], 400 * M_GEV)\nXe131 = XENON(ATOM_TABLE['131Xe'], 400 * M_GEV)\nx = np.linspace(0.1, 100, 100)\ne = x * KEV\ny = np.array([Xe129.SD_DifRate(ee, TYPE = 'p', XSEC0 = 3E-43) + Xe131.SD_DifRate(ee, TYPE = 'p', XSEC0 = 3E-43) for ee in e]) * KEV * 5 * YEAR / 365\nline3, = ax.semilogy(x, y, 'r--', linewidth = 1.5)\ny = np.array([Xe129.SD_DifRate(ee, TYPE = 'n', XSEC0 = 1E-44) + Xe131.SD_DifRate(ee, TYPE = 'n', XSEC0 = 1E-44) for ee in e]) * KEV * 5 * YEAR / 365\nline4, = ax.semilogy(x, y, 'r-', linewidth = 1.5)\n\n\nax.set_xlim([0, 100])\nax.set_ylim([1E-8, 1E-3])\n# ax.text(45, 2E-8, r'$\\sigma_p=3\\times10^{-39}$'+'cm'+r'$^2$', fontsize = 10)\n# ax.text(45, 1E-7, r'$\\sigma_n=1\\times10^{-40}$'+'cm'+r'$^2$', fontsize = 10)\nax.grid(b=True, which='major', color='grey', linestyle='-', alpha = 0.4)\nax.grid(b=True, which='minor', color='grey', linestyle=':', alpha = 0.4)\nax.legend((line1, line2, line3, line4), \\\n\t\t  (r'$m_\\chi=$ 40 GeV, p-only', r'$m_\\chi=$ 40 GeV, n-only', r'$m_\\chi=$ 400GeV, p-only', r'$m_\\chi=$ 400GeV, n-only'),\\\n\t\t  loc = 'lower left')\nax.set_ylabel('Spin-dependent event rate (events/5 keV/kg/day)')\nax.set_xlabel('Recoil Energy (keV)')\nplt.savefig('SD_Rate.pdf')\nplt.show()","sub_path":"Week6/Rate_Calculation/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"546741882","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport sys\nimport xarray as xr\nfrom netCDF4 import Dataset\n\n# ---THIS IS THE FILE OF STATIC FILE PATHS ; NEEDS TO BE EDITED ----\nfrom model_paths import  base_path, data_path\nimport cartopy.io.shapereader as shpreader\nfrom cartopy.feature import ShapelyFeature\nimport cartopy.crs as ccrs\nfrom wrf import (to_np, getvar, smooth2d, get_cartopy, cartopy_xlim,\n                                 cartopy_ylim, latlon_coords)\nfrom scipy import stats\n\ndatabase = data_path + \"/CO_snotel.db\"\ndbengine = \"sqlite:///{}\".format(database)\n\n\ndf = pd.read_sql_table('main', dbengine)\nlats = []\nlons = []\nnames = []\nfor i,row in df.iterrows():\n    lats.append(row.lat)\n    lons.append(row.lon)\n    names.append(row.site_id)\n\ndef get_snotel(site_id):\n    df = pd.read_sql_table(site_id, dbengine)\n    df['IncPrecip'] = df['IncPrecip']*25.4\n    df['time'] = df['Date']\n    df = df.set_index('time')\n    del df['Date']\n    return df\n\n# read wrf data\nwrf = pd.read_csv(\"wrf_snotel_dataframe.csv\")\nwrf = wrf.set_index(\"time\")\nwrf.index = pd.to_datetime(wrf.index)\ndel wrf['time.1']\n\n# fig,ax = plt.subplots(5,5)\n# axx = ax.flatten()\n\n\n## MAKE A DATAFAME TO STORE ANNUAL ERRORS\nnewdf = pd.DataFrame()\nyrs0 = np.zeros(2020-1988)\nyrs  = np.arange(1988,2020)\nfor col in wrf.columns:\n    newdf[col] = yrs0\n    newdf['wy'] =  yrs\nnewdf = newdf.set_index('wy')\n### DONE WITH THAT\n\n\nfig, ax = plt.subplots()\nfig.set_size_inches(11, 6)\n\nperformance_list = []\n\n\nlt_twenty_total = 0\ntotal_total = 0\n\n# Loop through each station..\nfor i,col in enumerate(wrf.columns):\n    print(col)\n    sn = get_snotel(col)\n\n    # get the earliest snotel date\n    sd = max(sn.index[0], pd.to_datetime(\"1986-10-01\"))\n    ed = min([sn.index[-1], pd.to_datetime(\"2020-09-30\")])\n\n    lt_twenty = 0\n    total = 0\n\n    # create the column data...\n    for year in range(sd.year, ed.year+1):\n        sd_wy = pd.to_datetime(\"%s-10-01\"%(int(year)-1))\n        ed_wy = pd.to_datetime(\"%s-09-01\"%year)  # just winter precip...\n\n        if sd_wy < sd:\n            continue\n        if ed_wy > ed:\n            continue\n\n        # compute the error\n        w = wrf[col][sd_wy:ed_wy].sum()\n        s = sn['IncPrecip'].loc[sd_wy:ed_wy].sum()\n\n        if year == 1986:\n            print(w, s)\n\n        error = (w - s)/s * 100.\n        if abs(error) < 20.:\n            lt_twenty += 1\n            lt_twenty_total += 1\n\n        total += 1\n        total_total += 1\n\n        # assign error to df\n        newdf.at[year, col] = error\n\n    # how well did this statio perform?\n    performance_list.append(lt_twenty/total)\n\nperflist = pd.DataFrame({\"col\":wrf.columns,\n              \"performance\":performance_list})\nperflist.to_csv(\"performance.csv\")\n\n\nnewdf= newdf.sort_index()\nnewdfim = np.where(newdf == 0., np.nan, newdf)#[1:,:]\nnewdfim = newdfim[::-1,:]\n\n\n#np.where(newdf)\n#newdfim = newdfim.T\ny,x = newdfim.shape\nim=ax.imshow(newdfim.T[:,::-1], cmap='Spectral', vmin=-50, vmax=50)\nax.set_xticks(range(y))\nax.set_yticks(range(x))\nax.set_xticklabels(newdf.index[:],rotation=45)\nax.set_yticklabels(wrf.columns)  # the last part is to flip the index (we want earlier years on top)\n\n\ncb = fig.colorbar(im)\ncb.set_label(\"\\% Error \")\n\n#plt.show()\nplt.savefig(\"WRF_Snotel_Error_Matrix\")\n\n    # # get the values after aplpying a rolling filter..\n    # w = wrf[col][sd:ed].rolling(5).mean()#.values\n    # s = sn['IncPrecip'].loc[sd:ed].rolling(5).mean()#.values\n\n    # axx[i].plot(w)\n    # axx[i].plot(s)\n\n    # # mask out nans\n    # mask = ~np.isnan(w) & ~np.isnan(s)\n\n    # # KDE estimate\n    # slope, intercept, r_value, p_value, std_err = stats.linregress(w[mask], s[mask])\n\n    # axx[i].scatter(w[mask], s[mask], alpha=.01, zorder=0, color='red')\n\n    # # plot a slope line ...\n    # lreg_sty = {\"zorder\":1,\n    #             \"color\":\"black\"}\n\n    # axx[i].plot(w, intercept + slope*w, **lreg_sty)\n    # axx[i].set_xlim(0,50)\n    # axx[i].set_ylim(0,50)\n    # axx[i].text(0,50,\"Snotel # %s\"%col)\n    # axx[i].text(0,40,\"slope: %s\"%round(slope, 2))\n    # axx[i].text(0,30,\"r_value: %s\"%round(r_value, 2))\n\n\n# for axz in axx:\n#     axz.axis(\"off\")\n\n# fig,ax = plt.subplots()\n# s = sn['IncPrecip'].loc[sd:ed]\n# w =\n\n\n# # plt.plot(s.values)\n# # plt.plot(w.values)\n\n# plt.show()\n\n\n\n","sub_path":"snotel_corr.py","file_name":"snotel_corr.py","file_ext":"py","file_size_in_byte":4269,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"407595224","text":"import tkinter as tk\nimport datetime\n\nclass DateEntry(tk.Frame):\n    def __init__(self, master, default_date, frame_look={}, **look):\n        args = dict(relief=tk.SUNKEN, border=1)\n        args.update(frame_look)\n        tk.Frame.__init__(self, master, **args)\n\n        args = {'relief': tk.FLAT}\n        args.update(look)\n\n        self.day_v = tk.StringVar()\n        self.month_v = tk.StringVar()\n        self.year_v = tk.StringVar()\n\n        self.entry_1 = tk.Entry(self, width=2, textvariable=self.day_v, **args)\n        self.label_1 = tk.Label(self, text='.', bg='white', **args)\n        self.entry_2 = tk.Entry(self, width=2, textvariable=self.month_v, **args)\n        self.label_2 = tk.Label(self, text='.', bg='white', **args)\n        self.entry_3 = tk.Entry(self, width=4, textvariable=self.year_v, **args)\n\n        if default_date is not None:\n            self.set_date(default_date)\n\n        self.entry_1.pack(side=tk.LEFT)\n        self.label_1.pack(side=tk.LEFT)\n        self.entry_2.pack(side=tk.LEFT)\n        self.label_2.pack(side=tk.LEFT)\n        self.entry_3.pack(side=tk.LEFT)\n\n        self.entry_1.bind('<KeyRelease>', self._e1_check)\n        self.entry_2.bind('<KeyRelease>', self._e2_check)\n        self.entry_3.bind('<KeyRelease>', self._e3_check)\n\n    def set_date(self, new_date):\n        if isinstance(new_date, datetime.datetime):\n            self.day_v.set(str(new_date.day).zfill(2))\n            self.month_v.set(str(new_date.month).zfill(2))\n            self.year_v.set(str(new_date.year))\n        elif isinstance(new_date, str):\n            self.day_v.set(new_date[:2])\n            self.month_v.set(new_date[3:5])\n            self.year_v.set(new_date[6:10])\n\n    def _backspace(self, entry):\n        cont = entry.get()\n        entry.delete(0, tk.END)\n        entry.insert(0, cont[:-1])\n\n    def _e1_check(self, e):\n        cont = self.entry_1.get()\n        if len(cont) > 0:\n            if len(cont) >= 2:\n                self.entry_2.focus()\n            \n            if len(cont) > 2 or not cont[-1].isdigit():\n                self._backspace(self.entry_1)\n                self.entry_1.focus()\n\n    def _e2_check(self, e):\n        cont = self.entry_2.get()\n        if len(cont) > 0:\n            if len(cont) >= 2:\n                self.entry_3.focus()\n            if len(cont) > 2 or not cont[-1].isdigit():\n                self._backspace(self.entry_2)\n                self.entry_2.focus()\n\n    def _e3_check(self, e):\n        cont = self.entry_2.get()\n        if len(cont) > 0:\n            if len(cont) > 4 or not cont[-1].isdigit():\n                self._backspace(self.entry_3)\n\n    def get(self):\n        return datetime.datetime(year=int(self.entry_3.get()),\n                                 month=int(self.entry_2.get()),\n                                 day=int(self.entry_1.get()))\n\nif __name__ == '__main__':\n    def show_contents(e):\n        print(dentry.get())\n\n    win = tk.Tk()\n    win.title('DateEntry demo')\n\n    dentry = DateEntry(win, default_date='28/02/2016', font=('Helvetica', 14, tk.NORMAL), border=1)\n    dentry.pack()\n\n    win.bind('<Return>', show_contents)\n    win.mainloop()","sub_path":"DateUtils/DateEntry.py","file_name":"DateEntry.py","file_ext":"py","file_size_in_byte":3132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"74111008","text":"from fortress.detectors.abstract_detector import AbstractDetector, DetectorClassification\nfrom fortress.slithir.operations import LowLevelCall\nfrom fortress.analyses.data_dependency.data_dependency import is_tainted\n\n\ndef controlled_delegatecall(function):\n    ret = []\n    for node in function.nodes:\n        for ir in node.irs:\n            if isinstance(ir, LowLevelCall) and ir.function_name in [\n                \"delegatecall\",\n                \"callcode\",\n            ]:\n                if is_tainted(ir.destination, function.contract):\n                    ret.append(node)\n    return ret\n\n\nclass ControlledDelegateCall(AbstractDetector):\n\n    ARGUMENT = \"controlled-delegatecall\"\n    HELP = \"Controlled delegatecall destination\"\n    IMPACT = DetectorClassification.HIGH\n    CONFIDENCE = DetectorClassification.MEDIUM\n\n    WIKI = \"https://github.com/crytic/fortress/wiki/Detector-Documentation#controlled-delegatecall\"\n\n    WIKI_TITLE = \"Controlled Delegatecall\"\n    WIKI_DESCRIPTION = \"`Delegatecall` or `callcode` to an address controlled by the user.\"\n    WIKI_EXPLOIT_SCENARIO = \"\"\"\n```solidity\ncontract Delegatecall{\n    function delegate(address to, bytes data){\n        to.delegatecall(data);\n    }\n}\n```\nBob calls `delegate` and delegates the execution to his malicious contract. As a result, Bob withdraws the funds of the contract and destructs it.\"\"\"\n\n    WIKI_RECOMMENDATION = \"Avoid using `delegatecall`. Use only trusted destinations.\"\n\n    def _detect(self):\n        results = []\n\n        for contract in self.fortress.contracts_derived:\n            for f in contract.functions:\n                # If its an upgradeable proxy, do not report protected function\n                # As functions to upgrades the destination lead to too many FPs\n                if contract.is_upgradeable_proxy and f.is_protected():\n                    continue\n                nodes = controlled_delegatecall(f)\n                if nodes:\n                    func_info = [\n                        f,\n                        \" uses delegatecall to a input-controlled function id\\n\",\n                    ]\n\n                    for node in nodes:\n                        node_info = func_info + [\"\\t- \", node, \"\\n\"]\n                        res = self.generate_result(node_info)\n                        results.append(res)\n\n        return results\n","sub_path":"fortress/detectors/statements/controlled_delegatecall.py","file_name":"controlled_delegatecall.py","file_ext":"py","file_size_in_byte":2339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"177043116","text":"from django import forms\n# from django.core.validators import RegexValidator\nfrom django.core.exceptions import ValidationError\nfrom django.shortcuts import HttpResponse, render\n\n\ndef check(value):\n    if not value:\n        raise ValidationError('用户名不合法')\n\n\n# 手写实现form表单验证\n# def register(request):\n#     msg = ''\n#     if request.method == 'POST':\n#         username = request.POST.get('username')\n#         print(username, len(username))\n#         if len(username) < 6:\n#             msg = '用户名长度至少6位'\n#         else:\n#             # 把用户名和密码写入数据库\n#             return HttpResponse('注册成功')\n#\n#     return render(request, 'register.html', {'msg': msg})\n# from django import forms\n# 示例1\n# class RegForm(forms.Form):\n#     username = forms.CharField()\n#     pwd = forms.CharField()\n#\n#\n# def register(request):\n#     obj = RegForm()\n#     if request.method == 'POST':\n#         obj = RegForm(request.POST)\n#         if obj.is_valid():\n#             return HttpResponse('注册成功')\n#     return render(request, 'register.html', {'obj': obj})\n# 字段示例\nclass RegForm(forms.Form):\n    # username = forms.CharField(\n    #     min_length=5,\n    #     label='用户名',\n    #     initial='henry',\n    #     error_messages={\n    #         'required': '不能为空',\n    #         'invalid': '格式有误',\n    #         'min_length': '用户名最短6位'\n    #     }\n    # )\n    # pwd = forms.CharField(\n    #     min_length=6,\n    #     label='密码',\n    #     initial='123456',\n    #     widget=forms.PasswordInput,\n    #\n    # )\n\n    # gender = forms.ChoiceField(\n    #     choices=((0, 'female'), (1, 'male'), (3, 'secret')),\n    #     label='性别',\n    #     initial=3,\n    #     # widget=forms.widgets.RadioSelect,\n    #     # widget=forms.widgets.Select\n    #     # widget=forms.widgets.CheckboxInput\n    #     # widget=forms.widgets.SelectMultiple,\n    #     widget=forms.widgets.CheckboxSelectMultiple,\n    #\n    # )\n\n    # hobby = forms.ChoiceField(\n    #     choices=((1, 'travelling'), (2, 'reading'), (3, 'listening'),),\n    #     label='爱好',\n    #     initial=3,\n    #     widget=forms.widgets.Select(),\n    # )\n    # hobby = forms.MultipleChoiceField(\n    #     choices=(('1', 'travelling'), ('2', 'reading'), ('3', 'listening'),),\n    #     label='爱好',\n    #     initial=['3'],\n    #     widget=forms.widgets.SelectMultiple,\n    #     # widget=forms.widgets.CheckboxSelectMultiple,\n    # )\n    #\n    # keep = forms.fields.ChoiceField(\n    #     label='是否记住密码',\n    #     initial='checked',\n    #     widget=forms.widgets.CheckboxInput,\n    # )\n\n    #     多选checkbox\n\n    # hobby = forms.fields.MultipleChoiceField(\n    #     choices=((1, 'travelling'), (2, 'reading'), (3, 'listening'),),\n    #     label=\"爱好\",\n    #     initial=[1, 3],\n    #     widget=forms.widgets.CheckboxSelectMultiple\n    # )\n    # 动态多选\n    # hobby = forms.ModelChoiceField(\n    #     queryset=models.Hobby.objects.all()\n    # )\n\n    # 动态多选二\n    # hobby = forms.MultipleChoiceField(\n    #     choices=models.Hobby.objects.all().values_list('pk', 'name')\n    # )\n    #\n    # def __init__(self, *args, **kwargs):\n    #     super().__init__(*args,**kwargs)\n    #     self.fields['hobby'].choices = models.Hobby.objects.values_list()\n\n    # 校验手机号\n    # phone = forms.CharField(\n    #      =[RegexValidator(r'1[3-9]\\d{9}$', '手机号不合法')]\n    # )\n    # username = forms.CharField(label='用户名')\n    #\n    pwd = forms.CharField(\n        label='密码',\n        widget=forms.widgets.PasswordInput,\n    )\n    # re_pwd = forms.CharField(\n    #     label='密码',\n    #     widget=forms.widgets.PasswordInput,\n    # )\n\n    def clean_username(self):\n        if 'o' in self.cleaned_data.get('username'):\n            raise ValidationError('用户名不合法。。。。。')\n\n    def clean(self):\n        print(self.fields, '--------------------')\n        if not self.cleaned_data.get('pwd') == self.cleaned_data.get('re_pwd'):\n            self.add_error('re_pwd', '两次密码不一致')\n            raise ValidationError('两次密码不一致')\n\n\ndef register(request):\n    obj = RegForm()\n    if request.method == 'POST':\n        obj = RegForm(data=request.POST)\n        if obj.is_valid():\n            return HttpResponse('注册成功')\n    return render(request, 'register.html', {'obj': obj})\n","sub_path":"065 form组件/form/app01/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4434,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"200031988","text":"##############################################################\n#    ___           _                                         #\n#   / _ \\__ _  ___| | ____ _  __ _  ___  ___                 #\n#  / /_)/ _` |/ __| |/ / _` |/ _` |/ _ \\/ __|                #\n# / ___/ (_| | (__|   < (_| | (_| |  __/\\__ \\                #\n# \\/    \\__,_|\\___|_|\\_\\__,_|\\__, |\\___||___/                #\n#                            |___/                           #\n##############################################################\n\nfrom keras.layers import Input, Dense, Flatten\nfrom keras.models import Model\nfrom keras.utils import Sequence\nfrom keras.optimizers import SGD, RMSprop\nfrom keras import backend as K\nimport h5py\nimport numpy as np\nimport pickle\nfrom scipy.stats import binned_statistic\nfrom scipy.optimize import leastsq\nimport tensorflow as tf\nfrom energyflow.archs import PFN\n\n##############################################################\n#  _                 _ _                  ___      _         #\n# | | ___   __ _  __| (_)_ __   __ _     /   \\__ _| |_ __ _  #\n# | |/ _ \\ / _` |/ _` | | '_ \\ / _` |   / /\\ / _` | __/ _` | #\n# | | (_) | (_| | (_| | | | | | (_| |  / /_// (_| | || (_| | #\n# |_|\\___/ \\__,_|\\__,|__|_| |_|\\__, | /___,' \\__,_|\\__\\__,_| #\n#                              |___/                         #\n##############################################################\n\n\nclass DataGenerator(Sequence):\n\n    def __init__(self, batch_size=128, train=True):\n        self.batch_size = batch_size\n        try:\n            data = h5py.File('../../data/jets/QCD_Pt-30to150Run2Spring18.h5', 'r')\n        except OSError:\n            print('Data not found')\n            exit()\n        sess_str = \"val\"\n        if train is True:\n            sess_str = \"train\"\n        self.y = data[sess_str + '_eventValues']\n        self.x = data[sess_str + '_pfCanValues']\n\n    def __len__(self):\n        return int(np.floor(len(self.x) / float(self.batch_size)))\n\n    def __getitem__(self, idx):\n        batch_x = self.x[idx * self.batch_size:(idx + 1) * self.batch_size]\n        batch_y = self.y[idx * self.batch_size:(idx + 1) * self.batch_size]\n\n        return batch_x, batch_y[:, 7]\n    def dump_res(self):\n        return self.y[:, 7][:len(self)*self.batch_size]\n\n\n##################################################################\n#  _                    ______                _   _              #\n# | |                   |  ___|              | | (_)             #\n# | |     ___  ___ ___  | |_ _   _ _ __   ___| |_ _  ___  _ __   #\n# | |    / _ \\/ __/ __| |  _| | | | '_ \\ / __| __| |/ _ \\| '_ \\  #\n# | |___| (_) \\__ \\__ \\ | | | |_| | | | | (__| |_| | (_) | | | | #\n# \\_____/\\___/|___/___/ \\_|  \\__,_|_| |_|\\___|\\__|_|\\___/|_| |_| #\n##################################################################\n\n\ndef mean_squared_percentage_error(y_true, y_pred):\n    diff = K.square((y_true - y_pred) / K.clip(K.abs(y_true),\n                                               K.epsilon(),\n                                               None))\n    return 100. * K.mean(diff, axis=-1)\n\n\ndef accuracy(y_true, y_pred):\n    R = K.abs(y_pred)/K.clip(K.abs(y_true),\n                             K.epsilon(),\n                             None)\n    mu = K.abs(K.mean(R, axis=-1)-1.)\n    sigma = K.std(R, axis=-1)\n    return K.exp(-mu-sigma)\n\ndef make_binned_loss(res, pred):\n    x = binned_statistic(res, res, statistic='mean', bins=50)[0][10:40]\n    y = binned_statistic(res, pred, statistic='std', bins=50)[0][10:40]\n    fitfunc = lambda c , x: c[0]*np.sqrt(x)+c[1]*x+c[2]\n    errfunc = lambda c , x, y: (y - fitfunc(c, x))\n    out = leastsq(errfunc, [1., 0.1, 0.], args=(x, y), full_output=1)\n    c = out[0]\n    def binned_max_likelihood_loss(y_true, y_pred):\n        epsilon = tf.constant(0.0000001)\n        mu = y_pred\n        sigma = 0.9*tf.sqrt(y_true)\n        first_part = tf.divide(tf.square(mu - y_true),\n                               2.*tf.square(sigma)+epsilon)\n        a = tf.divide(mu-30., tf.sqrt(2.)*sigma+epsilon)\n        b = tf.divide(mu-150., tf.sqrt(2.)*sigma+epsilon)\n        penalty = tf.erf(a) - tf.erf(b)\n        loss = first_part + tf.log(penalty+epsilon) + tf.log(sigma+epsilon)\n        value_range = [30., 150.]\n        # binning in y_true\n        indices = tf.histogram_fixed_width_bins(y_true, value_range, nbins=50)\n        # build mean per bin\n        loss_per_bin = tf.math.unsorted_segment_mean(loss, indices, 50)\n        return tf.reduce_mean(loss_per_bin, axis=None, keepdims=False)\n    return binned_max_likelihood_loss\n\ndef make_loss(res, pred):\n    x = binned_statistic(res, res, statistic='mean', bins=50)[0]\n    y = binned_statistic(res, pred, statistic='std', bins=50)[0]\n    fitfunc = lambda c , x: c[0]*np.sqrt(x)+c[1]*x+c[2]\n    errfunc = lambda c , x, y: (y - fitfunc(c, x))\n    out = leastsq(errfunc, [1., 0.1, 0.], args=(x, y), full_output=1)\n    c = out[0]\n    \n    def likelihood_loss(y_true, y_pred):\n        epsilon = tf.constant(0.0000001)\n        mu = y_pred\n        sigma = c[0]*tf.sqrt(y_true)+c[1]*y_true+c[2]\n        first_part = tf.divide(tf.square(mu - y_true),\n                               2.*tf.square(sigma)+epsilon)\n        a = tf.divide(150.-mu, tf.sqrt(2.)*sigma+epsilon)\n        b = tf.divide(30.-mu, tf.sqrt(2.)*sigma+epsilon)\n        penalty = tf.erf(a) - tf.erf(b)\n        loss = first_part + tf.log(penalty+epsilon) + tf.log(tf.sqrt(2.*np.pi)*sigma+epsilon)\n        return tf.reduce_mean(loss)\n    return likelihood_loss\n\n##############################################################\n#                        _      _                            #\n#        /\\/\\   ___   __| | ___| |                           #\n#       /    \\ / _ \\ / _` |/ _ \\ |                           #\n#      / /\\/\\ \\ (_) | (_| |  __/ |                           #\n#      \\/    \\/\\___/ \\__,_|\\___|_|                           #\n##############################################################\n\n\n# network architecture parameters\nppm_sizes = (100, 100, 128)\ndense_sizes = (100, 100, 100)\n\npfn = PFN(input_dim=4,\n          ppm_sizes=ppm_sizes,\n          dense_sizes=dense_sizes,\n          output_dim=1,\n          output_act='linear',\n          loss='mse',\n          metrics=[accuracy])\n\nD = pfn.model\n\nn = 20 \ndef produce_results(save_name):\n    D.save_weights(\"pfnet_weights.h5\")\n    D.save_weights\n    pred = D.predict_generator(val_Gen)\n    pred = pred.reshape(len(pred),)\n    res = {'pred': pred,\n           'history' : history}\n    results[save_name] = res\n\n\n#############################################################\n#  _____           _       _                   __     _     #\n# /__   \\_ __ __ _(_)_ __ (_)_ __   __ _    /\\ \\ \\___| |_   #\n#   / /\\/ '__/ _` | | '_ \\| | '_ \\ / _` |  /  \\/ / _ \\ __|  #\n#  / /  | | | (_| | | | | | | | | | (_| | / /\\  /  __/ |_   #\n#  \\/   |_|  \\__,_|_|_| |_|_|_| |_|\\__, | \\_\\ \\/ \\___|\\__|  #\n#                                  |___/                    #\n#############################################################\n\nepochs = 5\ntrain_Gen = DataGenerator(batch_size=1024, train=True)\nval_Gen = DataGenerator(batch_size=1024, train=False)\nrmsprop = RMSprop(lr=0.0001)\n\nresults = {}\nresults['y_true'] = val_Gen.dump_res()\n\n###############################################################################\n\nD.compile(loss='mse', optimizer=rmsprop, metrics=[accuracy])\n\nhistory = D.fit_generator(train_Gen,\n                          epochs=epochs,\n                          validation_data=val_Gen,\n                          validation_steps=3).history\n\nproduce_results('first')\n\n###############################################################################\nepochs = 1\nfor i in range(10):\n    pred = D.predict_generator(train_Gen)\n    res = train_Gen.dump_res()\n    pred = pred.reshape(len(pred),)\n\n    D.compile(loss=make_binned_loss(res, pred), optimizer=rmsprop, metrics=[accuracy])\n\n    history = D.fit_generator(train_Gen,\n                              epochs=epochs,\n                              validation_data=val_Gen,\n                              validation_steps=len(val_Gen)).history\n\n    produce_results(str(i))\n\n################################################################################\n\npickle.dump(results, open(\"../results/pfnet_binned_2_results.p\", \"wb\"))\n","sub_path":"jets/pfnet.py","file_name":"pfnet.py","file_ext":"py","file_size_in_byte":8221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"544283951","text":"# Copyright (c) Microsoft Corporation.\n# Licensed under the MIT License.\n\nfrom azure.cli.core import get_default_cli\nfrom typing import Dict, Union\nimport os\nimport shutil\nimport csv\nimport argparse\nimport sys\n\ndef azure_cli_run(cmd: str) -> Union[Dict, bool]:\n    \"\"\"Run Azure CLI command\n    \n    Returns\n    -------\n    cli.result\n        stout of Azure CLI command\n    \n    Raises\n    ------\n    cli.result.error\n        If sterror occurs due to azure CLI command\n        TODO: this type is highly variable, should we add some checks?\n    \"\"\"\n\n    args = cmd.split()\n    cli = get_default_cli()\n    cli.invoke(args)\n    if cli.result.result:\n        print(\"az {0}...\".format(cmd))\n        return cli.result.result\n    elif cli.result.error:\n        raise cli.result.error\n    return True\n\nclass AcrBuild:\n    def __init__(\n        self,\n        image_name: str,\n        image_version: str,\n        registry: str,\n        platform: str = None,\n        docker_path: str = \".\",\n        *args,\n        **kwargs,\n    ):\n        \"\"\"Build Docker image on Azure Container Registry.\n        \n        Parameters\n        ----------\n        image_name : str\n            Name of image\n        image_version : str\n            Version of image\n        registry : str\n            Name of ACR Repository\n        platform : str\n            Platform for Image (Windows, Ubuntu)\n        \"\"\"\n\n        self.image_name = image_name\n        self.image_version = image_version\n        self.registry = registry\n        if platform:\n            self.platform = platform\n        else:\n            docker_file = open(\"/\".join([docker_path, \"Dockerfile\"]))\n            docker_lines = docker_file.readlines()\n            if \"windows\" in docker_lines[0]:\n                self.platform = \"windows\"\n            else:\n                self.platform = \"linux\"\n        self.docker_path = docker_path\n\n        return super().__init__(*args, **kwargs)\n\n    def build_image_acr(\n        self, extra_build_args: Union[str, None], filename: str = \"Dockerfile\"\n    ):\n\n        if extra_build_args:\n            buildargs = \" --build-arg {0}\".format(extra_build_args)\n        else:\n            buildargs = \"\"\n        azure_cli_run(\n            \"acr build --image {0}:{1} --registry {2} --file {3}/{4} {3} --platform {5} {6}\".format(\n                self.image_name,\n                self.image_version,\n                self.registry,\n                self.docker_path,\n                filename,\n                self.platform,\n                buildargs,\n            )\n        )\n\ndef write_linux_python_dfile(sim_path, main_name, base_img):\n    with open(sim_path+\"/Dockerfile\", 'w') as docker_fname:\n        if base_img:\n            docker_fname.write('FROM {}\\n'.format(base_img))\n        else:\n            docker_fname.write('FROM python:3.7.4\\n')\n        docker_fname.write('RUN apt-get update && apt-get install vim -y --no-install-recommends && rm -rf /var/lib/apt/lists/*\\n')\n        docker_fname.write('WORKDIR /src\\n')\n        docker_fname.write('COPY . /src\\n')\n        docker_fname.write('RUN pip3 install setuptools wheel\\n')\n        docker_fname.write('RUN pip3 install -r requirements.txt\\n')\n        docker_fname.write('CMD [\"python3\", \"{}\"]\\n'.format(main_name))\n\ndef write_windows_python_dfile(sim_path, main_name, base_img):\n    with open(sim_path+\"/Dockerfile\", 'w') as docker_fname:\n        if base_img:\n            docker_fname.write('FROM {}\\n'.format(base_img))\n        else:\n            docker_fname.write('FROM mcr.microsoft.com/windows:10.0.17763.1040\\n')\n        docker_fname.write('SHELL' + ' [\"powershell\", \"-Command\", \"$ErrorActionPreference = ' + \"'Stop'\" + \"; $ProgressPreference = 'SilentlyContinue';\" '\"]\\n')\n        docker_fname.write('ENV PYTHON_VERSION 3.7.6rc1\\n')\n        docker_fname.write('ENV PYTHON_RELEASE 3.7.6\\n')\n        docker_fname.write(\"RUN $url = ('https://www.python.org/ftp/python/{0}/python-{1}-amd64.exe' -f $env:PYTHON_RELEASE, $env:PYTHON_VERSION); Write-Host ('Downloading {0} ...' -f $url); [Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; Invoke-WebRequest -Uri $url -OutFile 'python.exe'; Write-Host 'Installing ...'; Start-Process python.exe -Wait -ArgumentList @('/quiet', 'InstallAllUsers=1', 'TargetDir=C:\\\\Python', 'PrependPath=1', 'Shortcuts=0', 'Include_doc=0', 'Include_pip=0','Include_test=0'); $env:PATH = [Environment]::GetEnvironmentVariable('PATH', [EnvironmentVariableTarget]::Machine); Write-Host 'Verifying install ...'; Write-Host '  python --version'; python --version; Write-Host 'Removing ...'; Remove-Item python.exe -Force; Write-Host 'Complete.'\\n\")\n        docker_fname.write('ENV PYTHON_PIP_VERSION 19.2.1\\n')\n        docker_fname.write('ENV PYTHON_GET_PIP_URL https://github.com/pypa/get-pip/raw/404c9418e33c5031b1a9ab623168b3e8a2ed8c88/get-pip.py\\n')\n        docker_fname.write('ENV PYTHON_GET_PIP_SHA256 56bb63d3cf54e7444351256f72a60f575f6d8c7f1faacffae33167afc8e7609d\\n')\n        docker_fname.write(\"RUN Write-Host ('Downloading get-pip.py ({0}) ...' -f $env:PYTHON_GET_PIP_URL); \\ \\n\")\n        docker_fname.write(\"    [Net.ServicePointManager]::SecurityProtocol = [Net.SecurityProtocolType]::Tls12; \\ \\n\")\n        docker_fname.write(\"    Invoke-WebRequest -Uri $env:PYTHON_GET_PIP_URL -OutFile 'get-pip.py'; \\ \\n\")\n        docker_fname.write(\"    Write-Host ('Verifying sha256 ({0}) ...' -f $env:PYTHON_GET_PIP_SHA256); \\ \\n\")\n        docker_fname.write(\"    if ((Get-FileHash 'get-pip.py' -Algorithm sha256).Hash -ne $env:PYTHON_GET_PIP_SHA256) { \\ \\n\")\n        docker_fname.write(\"        Write-Host 'FAILED!'; \\ \\n\")\n        docker_fname.write('        exit 1; \\ \\n')\n        docker_fname.write('    }; \\ \\n')\n        docker_fname.write(\"    Write-Host ('Installing pip=={0} ...' -f $env:PYTHON_PIP_VERSION); \\ \\n\")\n        docker_fname.write(\"    python get-pip.py --disable-pip-version-check --no-cache-dir ('pip=={0}' -f $env:PYTHON_PIP_VERSION) ; \\ \\n\")\n        docker_fname.write('    Remove-Item get-pip.py -Force; \\ \\n')\n        docker_fname.write(\"    Write-Host 'Verifying pip install ...'; \\ \\n\")\n        docker_fname.write('    pip --version; \\ \\n')\n        docker_fname.write(\"    Write-Host 'Complete.'\\n\")\n        docker_fname.write('WORKDIR /src\\n')\n        docker_fname.write('COPY . /src\\n')\n        docker_fname.write('RUN pip3 install setuptools wheel\\n')\n        docker_fname.write('RUN pip3 install -r requirements.txt\\n')\n        docker_fname.write(\"RUN Set-ExecutionPolicy Bypass -Scope Process -Force; iex ((New-Object System.Net.WebClient).DownloadString('https://chocolatey.org/install.ps1'))\\n\")\n        docker_fname.write('RUN choco install git -y\\n')\n        docker_fname.write('CMD [\"python\", \"{}\"]\\n'.format(main_name))\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description='Run the application by using `sim-pack <language_api>` in a CLI to package the simulators for scaling using the Bonsai Service. The <language_api> modes can be switched by using: \\n\\n    python_api\\n    java_api\\n    C#_api\\n\\n Follow the requested prompts. Make sure to create a requirements.txt file with pinned versions of dependencies of the simulator.\\n\\n requirements.txt\\n    numpy==1.16.4\\n    matplotlib==3.0.3\\n    ...\\n',\n        allow_abbrev=False,\n        formatter_class=argparse.RawTextHelpFormatter,\n    )\n    parser.add_argument(\n        'language', \n        help='Provide a Microsoft Bonsai API mode (e.g., python_api, java_api, C#_api)'\n    )\n    parser.add_argument(\n        '--platform',\n        type=str, \n        help='Type the platform the simulator must run on, (e.g., windows or linux)',\n    )\n    parser.add_argument(\n        '--base-img',\n        type=str, \n        help='Type the base image to build from to override (e.g., mcr.microsoft.com/windows:10.0.17763.1040)',\n    )\n    args = parser.parse_args()\n\n    if args.platform:\n        platform = args.platform\n    else:\n        platform = input('Is windows or linux required to run Simulator?: ')\n\n    base_img = args.base_img\n\n    sim_path = input(\"Directory of Simulator integration files: \")\n    main_name = input(\"What is the name of the file with microsoft bonsai api? (e.g., main.py): \")\n    \n    if args.language == 'python_api':\n        if platform == 'windows':\n            write_windows_python_dfile(sim_path, main_name, base_img)\n        elif platform == 'linux':\n            write_linux_python_dfile(sim_path, main_name, base_img)\n        else:\n            print('Please type in correct OS type')\n            exit()\n        req_path = input(\"Directory of requirements.txt: \")\n        if not os.path.isfile(req_path+'/requirements.txt'):\n            shutil.copy(req_path+'/requirements.txt', sim_path)\n    elif args.language == 'java_api':\n        print('This path has not been programmed yet...')\n        exit()\n    elif args.language == 'C#_api':\n        language = 'C#'\n        print('This path has not been programmed yet...')\n        exit()\n    else:\n        print('Please choose one the modes (e.g., python_api, java_api, C#_api)')\n        exit()\n\n    img_name = input(\"Image Name (No underscores): \")\n    registry_name = input(\"Name of the ACR (without azurecr.io): \")\n\n    acr_build_image = AcrBuild(\n        image_name=img_name,\n        image_version='latest',\n        registry=registry_name,\n        platform=platform,\n        docker_path=sim_path,\n    )\n    \n    acr_build_image.build_image_acr(\n        filename='Dockerfile', extra_build_args=None\n    )\n\nif __name__ == '__main__':\n    main()","sub_path":"sim_packager.py","file_name":"sim_packager.py","file_ext":"py","file_size_in_byte":9500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"524132721","text":"import math\ndef is_penta(n):\n\tif (1+math.sqrt(1+24*n)) % 6 == 0:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef is_tri(n):\n\tif (1+math.sqrt(1+8*n)) % 2 == 0:\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef is_hex(n):\n\tif (1+math.sqrt(1+8*n)) % 4 == 0:\n\t\treturn True\n\telse:\n\t\treturn False\nnum = 1\nwhile True:\n\tif is_penta(num) and is_tri(num) and is_hex(num):\n\t\tprint('found one: {}'.format(num))\n\tnum += 1\n","sub_path":"Euler45/Euler45.py","file_name":"Euler45.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"140735567","text":"import random\n\n\ndef get_words(words, min_word_length=None):\n    with open(words, encoding='utf-8') as file:\n        wordlist = []\n        for line in file:\n            words = line.split()  # split words into a list\n\n            if min_word_length is not None:  # if min length parameter is given, take only words that are long enough\n                words = [word for word in words if len(word) >= min_word_length]\n\n            wordlist += words\n    return wordlist\n\n\ndef guess_letters():\n    word_to_guess = random.choice(get_words('data/words.txt', min_word_length=6))\n    wrong_guesses_made = 0\n    max_incorrect_guesses = 20\n    used_letters = []\n    word_progress = (word_to_guess[0] + ' ' + \"_ \" * (len(word_to_guess) - 2) + word_to_guess[-1])\n    print(\n        f\"Guess this word letter by letter! The game ends after you have made {max_incorrect_guesses} incorrect guesses.\")\n    print()\n    print(word_progress)\n    print()\n    while wrong_guesses_made <= max_incorrect_guesses:\n        guess = input(\"Insert one letter and press Enter: \")\n        while True:\n            if len(guess) != 1 or guess.isalpha() == False:\n                guess = input('You entered something which is not ONE LETTER. Try again: ')\n                continue\n            else:\n                break\n        print()\n        if guess in word_to_guess and guess not in used_letters:\n            print(\"Correct!\")\n            used_letters.append(guess)\n            i = 0\n            word_progress_split = word_progress.split()\n            while i < len(word_to_guess):\n                if guess == word_to_guess[i]:\n                    word_progress_split[i] = guess\n                    i = i + 1\n                else:\n                    i = i + 1\n            word_progress = ' '.join(word_progress_split)\n            if '_' not in word_progress:\n                print('..and you have guessed the word: ' + word_to_guess)\n                break\n\n        elif guess in word_to_guess and guess in used_letters:\n            print(\"Correct, but you already guessed that letter. Try something else!\")\n\n        elif guess not in word_to_guess and guess in used_letters:\n            print(\"Nope, and you already tried that!\")\n\n        elif guess not in word_to_guess and guess not in used_letters:\n            print('Nope!')\n            used_letters.append(guess)\n            wrong_guesses_made = wrong_guesses_made + 1\n\n        print(f'You have {max_incorrect_guesses - wrong_guesses_made} incorrect guesses left.')\n        print(\"You have already tried these letters: \" + str(used_letters))\n        print()\n        print(word_progress)\n        print()\n    else:\n        print(\"Out of guesses!\")\n        print('The word: ' + word_to_guess)\n\n\nif __name__ == '__main__':\n    guess_letters()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"198848159","text":"import board\nimport busio\nfrom digitalio import DigitalInOut\nfrom adafruit_pn532.spi import PN532_SPI\n\n# harware reset\nreset_pin = DigitalInOut(board.D6)\n# On Raspberry Pi, you must also connect a pin to P32 \"H_Request\" for hardware\n# wakeup! this means we don't need to do the I2C clock-stretch thing\nreq_pin = DigitalInOut(board.D12)\n\n# SPI connection:\nspi = busio.SPI(board.SCK, board.MOSI, board.MISO)\ncs_pin = DigitalInOut(board.D5)\npn532 = PN532_SPI(spi, cs_pin, debug=False)\n\nic, ver, rev, support = pn532.get_firmware_version()\nprint('Found PN532 with firmware version: {0}.{1}'.format(ver, rev))\n\n# Configure PN532 to communicate with MiFare cards\ndef scan_id():\n    pn532.SAM_configuration()\n\n    print('Waiting for RFID/NFC card...')\n    while True:\n        # Check if a card is available to read\n        uid = pn532.read_passive_target(timeout=0.5)\n        # print('.', end=\"\")\n        # Try again if no card is available.\n        if uid is None:\n            continue\n        print('Found card with UID:', [hex(i) for i in uid])\n        card_id = hex(uid[0]).split('x')[1]+':'+hex(uid[1]).split('x')[1]+':'+hex(uid[2]).split('x')[1]+':'+hex(uid[3]).split('x')[1]\n        break\n    return card_id","sub_path":"nfctoid.py","file_name":"nfctoid.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"115582494","text":"from django.shortcuts import render, redirect, render_to_response\nimport json\nfrom django.http import HttpResponse, JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework import permissions\nfrom rest_framework.permissions import AllowAny\nfrom ninjaapp.models import ServerStatus, Phase_Status,ChatHistory,Draft,Ninja, Post_Log, Coach, Organization, Quest, Quest_Status, Badge, Phase, Quest_Page,SurveyAnswers,SurveyQuestion\nfrom imagekit.models import ImageSpecField\nfrom imagekit import ImageSpec, register\nfrom imagekit import processors\nfrom imagekit.processors import ResizeToFill\nfrom django.contrib.auth import login, authenticate\nfrom django.contrib.auth.forms import UserCreationForm, PasswordResetForm\nfrom ninjaapp.forms import SignUpForm, SignUpNinjaForm, SignUpCoach\nfrom ninjaapp.ninjaapp_imagekit import *\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\n\nfrom django.views import View\nfrom django.views.generic.base import TemplateView\nimport sys\nimport os\n\nimport requests\nfrom actstream.models import user_stream\nfrom actstream import action\nfrom actstream.models import Action\n\nfrom django.shortcuts import get_object_or_404\n\n#imports for NINJAPI\nfrom rest_framework.response import Response\nfrom rest_framework import viewsets\nfrom ninjaapp.serializers import ServerStatusSerializer,Phase_StatusSerializer,ChatHistorySerializer,SurveyQuestionSerializer,SurveyAnswersSerializer,DraftSerializer,Quest_PageSerializer,UserSerializer, NinjaSerializer, CoachSerializer, OrganizationSerializer, QuestSerializer, Quest_StatusSerializer, Post_LogSerializer, BadgeSerializer, PhaseSerializer, ActionSerializer\n\n\n\n# def index(request):\n\n#     ninjas = Ninja.objects.all()\n    \n    \n#     print('CABEZAAAA' + settings.SCHOOLFORNINJA_VERSION)\n\n#     context = { 'ninjas': ninjas,\n#                 'SCHOOLFORNINJA_VERSION': settings.SCHOOLFORNINJA_VERSION }\n#     return render(request, 'index.html', context)\n\n\n\n# def password_reset(email, from_email, template='registration/password_reset_email.html'):\n@csrf_exempt\n@api_view(['POST'])\n@permission_classes((permissions.AllowAny,))\ndef password_reset(request, template='emails/password_reset_email.html'):\n    \"\"\"\n    Reset the password for all (active) users with given E-Mail adress\n    \"\"\"\n    data=json.loads(request.body)\n    model = User\n    email=data['email']\n    from_email=data['from_email']\n    pagination_class = None\n    message = None\n    explanation = None\n    status_code = 500\n\n    def handle_exception(message, explanation, status_code):\n        responseData = {\n            'message': message,\n            'explanation': explanation,\n        }\n        return HttpResponse(json.dumps(responseData), content_type=\"application/json\", status=status_code)\n    \n    try:\n        match = User.objects.get(email=email)\n        if match:\n            form = PasswordResetForm({'email': email})\n            if form.is_valid():\n                form.save(from_email=from_email, email_template_name=template, subject_template_name='registration/password_reset_subject.txt')\n                print(\"email has been accepted, all ok <<<<<<<<<<<<<<<<<<<<<<<<<\")\n                return HttpResponse(\"Email is valid!\")\n                \n            else:\n                print(\"form is not valid, else statement <<<<<<<<<<<<<<<<<<<<<<<<<\")\n                message = \"Email doesn't exists\"\n                explanation = \"This user email doesn't exists in the database\"\n                status_code = 400\n                handle_exception(message, explanation, status_code)\n    except:\n        print(\"except has been triggered! <<<<<<<<<<<<<<<<<<<<<<<<<\")\n        message = \"User doesn't match\"\n        explanation = \"The user email doesn't exists in the database\"\n        status_code = 400\n    \n        responseData = {\n            'message': message,\n            'explanation': explanation,\n        }\n    \n        print(json.dumps(responseData) + \" status code: \" + str(status_code))\n        \n        return HttpResponse(json.dumps(responseData), content_type=\"application/json\", status=status_code)\n    \n\n\n\n# ==================================\n# HTML page to test Codrops elements\n# ==================================\ndef index_test(request):\n    pagination_class = None\n    context = { 'user' : request.user }\n\n    return render(request, 'index-test.html', context)\n\n\n\ndef test_actions(request):\n    pagination_class = None\n    if request.user.is_authenticated:\n        action.send(request.user, verb='action_test', description='Welcome to Ninjaapp!', shown='no')\n        return HttpResponse(\"Action_test created on the DB, you should check it out on the admin...\")\n    else:\n        return HttpResponse(\"You are not logged in\")\n\n\ndef signup(request):\n    pagination_class = None\n    if request.method == 'POST':\n        form = SignUpForm(request.POST)\n        ninjaform = SignUpNinjaForm(request.POST)\n        if all([form.is_valid() and ninjaform.is_valid()]):\n            user = form.save()\n            ninja = ninjaform.save(False)\n            ninja.user = user\n            ninjaform.save()\n            username = form.cleaned_data.get('username')\n            raw_password = form.cleaned_data.get('password1')\n            email = form.cleaned_data.get(\"email\")\n            user = authenticate(username=username, email=email, password=raw_password)\n            login(request, user)\n            return redirect('index')\n    else:\n        form = SignUpForm()\n        ninjaform = SignUpNinjaForm()\n    return render(request, 'signup.html', {'form': form , 'ninjaform':ninjaform })\n\ndef signupcoach(request):\n    pagination_class = None\n    if request.method == 'POST':\n        form = SignUpForm(request.POST)\n        coachform = SignUpCoach(request.POST)\n        if all([form.is_valid() and coachform.is_valid()]):\n            user = form.save()\n            coach = coachform.save(False)\n            coach.user = user\n            coachform.save()\n            username = form.cleaned_data.get('username')\n            raw_password = form.cleaned_data.get('password1')\n            email = form.cleaned_data.get(\"email\")\n            user = authenticate(username=username, email=email, password=raw_password)\n            login(request, user)\n            return redirect('index')\n    else:\n        form = SignUpForm()\n        coachform = SignUpCoach()\n    return render(request, 'signupcoach.html', {'form': form , 'coachform':coachform })\n\n\n\n\nclass NinjaView(View):\n    pagination_class = None\n    def get(self,request):\n        return HttpResponse(Ninja.objects.all())\n\nclass SfN_IndexView(TemplateView):\n    pagination_class = None\n    \n    def get(sef,request):\n        ninjas = Ninja.objects.all()\n        \n        print(' ')\n        print(' ')\n        print('INDEX SfN :: ' + settings.SCHOOLFORNINJA_VERSION )\n        print(' ')\n        print(' ')\n\n        context = { 'ninjas': ninjas,\n                    'SCHOOLFORNINJA_VERSION': settings.SCHOOLFORNINJA_VERSION }\n        return render(request, 'index.html', context)\n\n\n\ndef debug(request):\n\n    print(\"BASE_DIR: \" + settings.BASE_DIR + \"   <<<<<<<<<<<<<<<<<\")\n    print(\"SITE_ROOT: \" + settings.SITE_ROOT + \"   <<<<<<<<<<<<<<<<<\")\n    return HttpResponse(\"MIRA TU CONSOLA AMIGOOO\")\n\n\n\n\n\ndef profile(request):\n    pagination_class = None\n    if request.user.is_authenticated():\n\n        local_ninja = get_object_or_404(Ninja,user_id=request.user.id)\n        local_friends = Ninja.objects.filter(has_friends=request.user.id)\n        local_logs = Post_Log.objects.filter(belongs_to_ninja=request.user.id)\n        print(local_ninja)\n        print(local_friends)\n        print(local_logs)\n        #sys.exit()\n\n        context = {'ninja':local_ninja,\n                   'friends':local_friends,\n                   \n                   }\n\n        return render(request,'profile.html',context)\n\n    else:\n        return HttpResponseRedirect('index')\n\n\n\n\n\n\n#def SignupViewSet(viewsets.ModelViewSet):\n\n#    serializer_class = UserSerializer\n    \n#    def post(self, request, format=None):\n\n#        serialized = UserSerializer(data=request.data)\n#        if serialized.is_valid():\n#            serialized.save()\n#            return Response(serialized.data, status=status.HTTP_201_CREATED)\n#        else:\n#            return Response(serialized._errors, status=status.HTTP_400_BAD_REQUEST)\n@csrf_exempt\n@api_view(['POST'])\n@permission_classes((permissions.AllowAny,))\ndef signup_user(request):\n\n    queryset = User.objects.all()\n    serializer = UserSerializer(data=request.data, context={'request': request})\n    pagination_class = None\n    if serializer.is_valid():\n       # User.objects.create(\n       #     serializer.data['email'],\n       #     serializer.data['username'],\n       #     serializer.data['password']\n       # )\n       serializer.save()\n       #return Response(serializer.data, status=status.HTTP_201_CREATED)\n       return Response(serializer.data)\n    else:\n#        return Response(serializer._errors, status=status.HTTP_400_BAD_REQUEST)\n        return Response(serializer._errors)\n\n#############\n##### NINJAPI\n#############\n# We have imported already the serializers for the API representation\n\nclass UserViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows users to be viewed or edited.\n    \"\"\"\n    queryset = User.objects.all()\n    serializer_class = UserSerializer\n    pagination_class = None\n#    @permission_classes((AllowAny, ))\n #   def post(self,request,format=None):\n  #      serializer = UserSerializer(data=request.data)\n   #     if serializer.is_valid():\n    #        serializer.save()\n     #       return Response(serializer.data)\n      #  else:\n       #     return Response(serializer.errors)\n\nclass NinjaViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows ninjas to be viewed or edited.\n    \"\"\"\n    permission_classes = (AllowAny,)\n    queryset = Ninja.objects.all()\n    serializer_class = NinjaSerializer\n    filter_fields = ('user','has_coaches','ninja_name')\n    pagination_class = None\n    def put(self,request,pk,format=None):\n        if request.user.is_authenticated():\n            ninja = Ninja.objects.filter(user_stream=request.user)\n            searchNinja = Ninja.get_object(pk)\n            if ninja.user == searchNinja.user:\n                serializer = NinjaSerializer(searchNinja,data=request.data)\n                if serializer.is_valid():\n                    serializer.save()\n                    return Response(serializer.data)\n                else:\n                    return Response(serializer.errors)\n    # def post(self,request,format=None):\n    #     serializer = NinjaSerializer(data=request.data)\n    #     if serializer.is_valid():\n    #         serializer.save()\n    #         return Response(serializer.data)\n    #     else:\n    #         return Response(serializer.errors)\n\nclass CoachViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows coaches to be viewed or edited.\n    \"\"\"\n    queryset = Coach.objects.all()\n    serializer_class = CoachSerializer\n    filter_fields = ('user',)\n    pagination_class = None\n\nclass OrganizationViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows organizations to be viewed or edited.\n    \"\"\"\n    queryset = Organization.objects.all()\n    serializer_class = OrganizationSerializer\n    pagination_class = None\n\nclass QuestPagesViewSet(viewsets.ModelViewSet):\n    queryset = Quest_Page.objects.all()\n    serializer_class = Quest_PageSerializer\n    filter_fields = ('belongs_to_quest',)\n    pagination_class = None\n\nclass QuestViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows quests to be viewed or edited.\n    \"\"\"\n    queryset = Quest.objects.all()\n    serializer_class = QuestSerializer\n    filter_fields = ('is_from_phase',)\n    pagination_class = None\n\n    def put(self,request,pk,format=None):\n        quest = Quest.get_object(pk)\n        serializer = QuestSerializer(quest,data=request.data, many=True)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        else:\n            return Response(serializer.errors)\n\n    def post(self,request,format=None):\n        serializer = QuestSerializer(data=request.data,many=True)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        else:\n            return Response(serializer.errors)\n\nclass DraftViewSet(viewsets.ModelViewSet):\n    queryset = Draft.objects.all()\n    serializer_class = DraftSerializer\n    filter_fields = ('belongs_to_ninja', 'belongs_to_quest')\n    pagination_class = None\n\n\nclass Quest_StatusViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows quest_status to be viewed or edited.\n    \"\"\"\n    queryset = Quest_Status.objects.all()\n    serializer_class = Quest_StatusSerializer\n    filter_fields = ('belongs_to_ninja','belongs_to_quest',)\n    pagination_class = None\n\n    def put(self,request,pk,format=None):\n        quest_status = Quest_Status.get_object(pk)\n        serializer = Quest_StatusSerializer(quest_status,data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        else:\n            return Response(serializer.errors)\n\n    def post(self,request,format=None):\n        serializer = Quest_StatusSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        else:\n            return Response(serializer.errors)\n\n\nclass Post_LogViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows post_logs to be viewed or edited.\n    \"\"\"\n    queryset= Post_Log.objects.all()\n    serializer_class = Post_LogSerializer\n    filter_fields = ('belongs_to_ninja', 'belongs_to_quest','privacy')\n    pagination_class = None\n\n    def put(self,request,pk,format=None):\n        if request.user.is_authenticated():\n            ninja = Ninja.objects.filter(user_stream=request.user)\n            post = Post_Log.get_object(pk)\n            if ninja == post.belongs_to_ninja:\n                serializer = Post_LogSerializer(post,data=request.data)\n                if serializer.is_valid():\n                    serializer.save()\n                    return Response(serializer.data)\n                else:\n                    return Response(serializer.errors)\n\n    def post(self,request,format=None):\n        if request.user.is_authenticated():\n            serializer = Post_LogSerializer(data=request.data)\n            if serializer.is_valid():\n                serializer.save()\n                return Response(serializer.data)\n            else:\n                return Response(serializer.errors)\n\nclass BadgeViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows badges to be viewed or edited.\n    \"\"\"\n    queryset = Badge.objects.all()\n    serializer_class = BadgeSerializer\n    pagination_class = None\n\nclass Phase_StatusViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows quest_status to be viewed or edited.\n    \"\"\"\n    queryset = Phase_Status.objects.all()\n    serializer_class = Phase_StatusSerializer\n    filter_fields = ('belongs_to_ninja','belongs_to_phase','completed')\n    pagination_class = None\n\n    def put(self,request,pk,format=None):\n        phase_status = Phase_Status.get_object(pk)\n        serializer = Phase_StatusSerializer(phase_status,data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        else:\n            return Response(serializer.errors)\n\n    def post(self,request,format=None):\n        serializer = Phase_StatusSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        else:\n            return Response(serializer.errors)\n\n\nclass PhaseViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows phases to be viewed or edited.\n    \"\"\"\n    queryset = Phase.objects.all()\n    serializer_class = PhaseSerializer    \n    pagination_class = None\n\nclass ServerStatusViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows phases to be viewed or edited.\n    \"\"\"\n    permission_classes = (AllowAny,)\n    queryset = ServerStatus.objects.all()\n    serializer_class = ServerStatusSerializer    \n    pagination_class = None\n\nclass ActionViewSet(viewsets.ModelViewSet):\n    \"\"\"\n    API endpoint that allows actions to be viewed.\n    \"\"\"\n    queryset = Action.objects.all()\n    serializer_class = ActionSerializer\n    filter_fields = ('actor_object_id','verb', 'target_object_id')\n    pagination_class = None\nclass SurveyQuestionViewSet(viewsets.ModelViewSet):\n\n    queryset = SurveyQuestion.objects.all()\n    serializer_class = SurveyQuestionSerializer\n    pagination_class = None\n\nclass ChatHistoryViewSet(viewsets.ModelViewSet):\n    queryset = ChatHistory.objects.all()\n    serializer_class = ChatHistorySerializer\n    filter_fields = ('sender','channel',)\n\nclass SurveyAnswersViewSet(viewsets.ModelViewSet):\n    queryset= SurveyAnswers.objects.all()\n    serializer_class = SurveyAnswersSerializer\n    filter_fields = ('belongs_to_ninja',)\n    pagination_class = None\n    \n    def post(self,request,format=None):\n        if request.user.is_authenticated():\n            serializer = SurveyAnswersSerializer(data=request.data)\n            if serializer.is_valid():\n                serializer.save()\n                return Response(serializer.data)\n            else:\n                return Response(serializer.errors)\n\n\nregister.generator('ninjaapp:thumbnail30x30', Thumbnail30x30)\nregister.generator('ninjaapp:thumbnail50x50', Thumbnail50x50)\n\n\n","sub_path":"webfaction_schoolforninja/sfn_ronin_site/ninjaapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":17753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"106466775","text":"from flask import Flask\n\napp = Flask(__name__)\n\n\ndef get_message():\n    \"\"\"\n    :return: the text from the text file services/message.txt\n    \"\"\"\n\n    with open('services/web/message.txt', 'r') as file:\n        msg = \"\"\n        for line in file:\n            msg = msg + line + '</br>'\n\n    return msg\n\n\n@app.route('/')\ndef hello_world():\n    \"\"\"\n    Reads the text from a file which is assigned as a Docker volume.\n    This is a contrived way to make Flask reactive to changes on the local file system.\n    :return:\n    \"\"\"\n    return get_message()\n\n","sub_path":"2.nginx_flask_integration/services/web/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"294850166","text":"#!/usr/bin/env python3.4\n\nfrom __future__ import division, print_function, absolute_import\n\nimport csv\nimport math\nimport argparse\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom types import *\nfrom Bio import SeqIO\nfrom itertools import product\n\nPURINES = ['A', 'G']\nPYRIMIDINES = ['C', 'T']\nBASES = sorted(PURINES + PYRIMIDINES)\n\n\nclass muparse(object):\n\n    def __init__(self, mutpos_file, clonality=(0, 1), rev=False,\n                 record_dict=None):\n\n        assert type(mutpos_file) is StringType\n        assert not isinstance(clonality, basestring)\n\n        self.mutpos_file = mutpos_file\n        self.c = min(clonality)\n        self.C = max(clonality)\n        self.record_dict = record_dict\n        self.rev = rev\n\n        # A list of all unique possible trinucleotide contexts, nothing more\n        self.tri = [''.join(_) for _ in list(product(BASES, repeat=3))]\n\n        # The labels for pyrimidine and purine notation\n        self.pu_labels = [\"A>C\", \"A>G\", \"A>T\", \"G>A\", \"G>C\", \"G>T\"]\n        self.py_labels = [\"C>A\", \"C>G\", \"C>T\", \"T>A\", \"T>C\", \"T>G\"]\n\n    def fasta_to_dict(self, ref_file):\n        self.ref_file = ref_file\n        with open(self.ref_file, 'r') as handle:\n            self.record_dict = SeqIO.to_dict(SeqIO.parse(handle, 'fasta'))\n\n    def flatten(self):\n        self.heights = []\n        self.xlabels = []\n        self.panel_titles = sorted(self.results.keys())\n\n        for i in self.panel_titles:\n            for (label, height) in sorted(self.results[i].items()):\n                self.xlabels.append(label)\n                self.heights.append(height)\n\n        return(self.xlabels, self.heights, self.panel_titles)\n\n    def get_trinucleotide(self, chromosome, position):\n        # Python starts counting at 0, saving slice indexes\n        chromosome_length = len(self.record_dict[chromosome].seq)\n        position -= 1\n        start = position - 1\n        end = position + 1\n\n        # It is necessary to protect for the two edge cases now\n        if start >= 0 and end <= chromosome_length:\n            return self.record_dict[chromosome].seq[start:end + 1]\n        else:\n            return None\n\n    def process(self):\n        self.results = {}\n        self.totals = {_: 0 for _ in BASES}\n\n        # We use six keys defaulted to pyrimidines with purines optional.\n        # For each key in our results dictionary we keep a separate dictionary\n        # of all trinucleotides that have the right pyrimidine or purine base\n        # in the center position.\n        for l in self.py_labels if self.rev is False else self.pu_labels:\n            self.results[l] = {tri: 0 for tri in self.tri if tri[1] is l[0]}\n\n        # Open the mutpos_file and read data line by line\n        with open(self.mutpos_file, 'r') as handle:\n            for line in handle:\n                # Strip newline characters and split on tabs\n                line = line.strip().split('\\t')\n\n                # Unpack line and change datatype accordingly\n                chromosome, ref_base = [str(_) for _ in line[0:2]]\n                position, depth, observed = [int(_) for _ in line[2:5]]\n                T, C, G, A, insert, delete, N = [float(_) for _ in line[5:12]]\n\n                # Get the trinucleotide context at the given position in the\n                # given chromosome as loocked up in record_dict\n                context = self.get_trinucleotide(chromosome, position)\n\n                # If we encounter an edge case we'll skip this iteration\n                if context is None:\n                    continue\n\n                # If the base is a not in our intended labeling set, let's get\n                # the compliment of the reference base, the reverse compliment\n                # of the trinucleotide context, and the mutation counts for the\n                # compliment of the base substitution observed\n                if not self.rev and ref_base in PURINES:\n                    ref_base = ref_base.replace('G', 'C').replace('A', 'T')\n                    context = context.reverse_complement()\n                    A, G, C, T = T, C, G, A\n                elif self.rev and ref_base in PYRIMIDINES:\n                    ref_base = ref_base.replace('C', 'G').replace('T', 'A')\n                    context = context.reverse_complement()\n                    A, G, C, T = T, C, G, A\n\n                # Clonality of mutation at this chromosome position is defined\n                # as the maximum base substitution frequency of any of the\n                # four possible nucleotide bases. This can be used to filter\n                # out ultra-ultra rare mutations or hyper clonal mutations\n                clonality = max([_ / depth for _ in [A, C, G, T]])\n\n                for (base, mutations) in zip(BASES, [A, C, G, T]):\n                    label = ref_base + '>' + base\n                    if label in self.results and self.c <= clonality <= self.C:\n                        # Generic mutations are counted or debugging\n                        self.totals[ref_base] += mutations\n                        # Trinucleotide context mutations are stored\n                        self.results[label][context] += mutations\n\n        self.report = (\"\\nMutation Report:\\n\"\n                       \"AT Total: {0}\\nGC Total: {1}\"\n                       \"\\nGrand total: {2}\\n\")\n        self.report = self.report.format(self.totals['A'] + self.totals['T'],\n                                         self.totals['C'] + self.totals['G'],\n                                         sum(self.totals.values()))\n\n    def reverse(self):\n        self.rev = not self.rev\n        self.process()\n\n    def to_csv(self, output_file):\n        self.output_file = output_file\n\n        with open(self.output_file, 'w') as handle:\n            writer = csv.writer(handle)\n            writer.writerow(['Type', 'Context', 'Count', 'Proportion'])\n\n            for label in sorted(self.results.keys()):\n                for context, count in sorted(self.results[label].items()):\n                    proportion = count / sum(self.totals.values())\n                    writer.writerow([label, context, count, proportion])\n\n\nclass reactivity(object):\n\n    \"\"\"\n    g_reactivity data acquired from this BibTex entry:\n\n    @article{Benasutti1988,\n    author = {Benasutti, Matt and Ejadi, Samuel and Whitlow,\n              M D and Loechler, E L},\n    journal = {Biochemistry},\n    mendeley-groups = {Signatures,Liver Cancer,Liver Cancer/Aflatoxin},\n    pages = {472--481},\n    title = {{Mapping the Binding Site of Aflatoxin B1 in DNA: Systematic\n              Analysis of the Reactivity of Aflatoxin B1 with Guanines in\n              Different DNA Sequences}},\n    volume = {27},\n    year = {1988}}\n    \"\"\"\n\n    def __init__(self):\n        self.g_reactivity = np.array([[0.25, 0.32],\n                                      [0.82, 0.42],\n                                      [1.00, 1.00],\n                                      [0.17, 0.75]])\n\n    @staticmethod\n    def expand_reactivity(input_array):\n        \"\"\"\n        reactivity = 5' side * 3 'side = X * Y\n        \"\"\"\n        expanded = np.empty([4, 4])\n        for i in range(4):\n            expanded[i, :] = input_array[i, 0] * input_array[:, 1]\n\n        # Weigh data to scale back to a sum of 1.0 and return\n        return expanded / expanded.sum()\n\n\nclass sigplot(object):\n\n    def __init__(self, xlabels, heights, proportional=True, title='',\n                 panes=6, perpane=16, inches=18, rev=False,\n                 title_fontsize=11, xlabel_fontsize=10, ylabel_fontsize=12):\n        self.proportional = proportional\n\n        if self.proportional:\n            self.heights = heights / sum(np.array(heights))\n        else:\n            self.heights = np.array(heights)\n\n        self.xlabels = xlabels\n        self.title = title\n        self.panes = panes\n        self.perpane = perpane\n        self.inches = inches\n        self.rev = rev\n\n        self.title_fontsize = title_fontsize\n        self.xlabel_fontsize = xlabel_fontsize\n        self.ylabel_fontsize = ylabel_fontsize\n\n        # Standard color library and spectrum color order\n        self.color_dict = {'almost_black': '#262626',\n                           'black': '#231F20',\n                           'blue': '#52C3F1',\n                           'darkgrey': '#8E8F8F',\n                           'green': '#97D54C',\n                           'litegrey': '#CBC9C8',\n                           'pink': '#EDBFC2',\n                           'red': '#E62223'}\n        self.bar_colors = ['blue', 'black', 'red', 'litegrey', 'green', 'pink']\n\n        # bar_width scale depending on amount of panes being presented\n        self.bar_width = 0.95 - (self.panes / 6 * 0.55)\n\n        # Change the overall figure dimensions scaling to the number of panes\n        self.aspect = 80 - (self.panes / 6 * 50)\n        self.y_inches = self.aspect / 100 * self.inches\n\n        # Initialize barplot\n        self.fig = plt.figure(figsize=(self.inches, self.y_inches))\n        self.ax = plt.subplot2grid((6, 6), (1, 0), colspan=6, rowspan=4)\n        bar_plot = self.ax.bar(left=np.arange(self.panes * self.perpane),\n                               height=self.heights, width=self.bar_width,\n                               zorder=2)\n\n        # Remove plot ticks on x and y axis and edit plot spines\n        for tic in self.ax.xaxis.get_major_ticks():\n            tic.tick1On = tic.tick2On = False\n        for tic in self.ax.yaxis.get_major_ticks():\n            tic.tick1On = tic.tick2On = False\n        self.ax.spines['bottom'].set_color(self.color_dict['darkgrey'])\n        self.ax.spines['left'].set_color(self.color_dict['darkgrey'])\n        self.ax.spines['right'].set_visible(False)\n        self.ax.spines['top'].set_visible(False)\n\n        # Color each self.perpane group a color\n        if self.rev:\n            self.bar_colors = np.repeat('black', len(self.bar_colors))\n\n        for i in range(self.panes):\n            for j in range(self.perpane):\n                bar_number = j + (i * self.perpane)\n                c = self.color_dict[self.bar_colors[i]]\n                bar_plot[bar_number].set_color(c)\n\n        # Trim a little xlim off the left and right for better symmetry overall\n        self.ax.set_xlim([-0.5, self.panes * self.perpane])\n\n        # Add xtick indexes and labels using monospace font\n        self.ax.set_xticks(np.arange(self.panes * self.perpane) +\n                           self.bar_width - 0.15)\n        self.ax.set_xticklabels(self.xlabels, fontsize=self.xlabel_fontsize,\n                                family='monospace', rotation='vertical',\n                                color=self.color_dict['almost_black'])\n\n        # m is a multiplier that sets tick position and labeling in either 0.1\n        # increments or 25 increments depending on if we are plotting a\n        # proportional plot or a strictly numeric plot\n        m = 0.1 if self.proportional else 25\n        by = 10 if self.proportional else 25\n\n        # The maximum height rounded up to the nearest m\n        self.max_height = (math.ceil(max(self.heights) / m) * m) + m\n\n        # Create tick locations every 'm' and labels every 'by'\n        self.y_ticks = np.arange(0, self.max_height, m)\n        self.y_labels = np.arange(0, int(self.max_height * 100), by)\n\n        # Add ytick indexes and labels according style of plot.\n        self.ax.set_yticks(self.y_ticks)\n        self.ax.set_yticklabels(labels=self.y_labels,\n                                fontsize=self.ylabel_fontsize,\n                                color=self.color_dict['almost_black'])\n\n        # Plot a grey horizontal line every y tick behind bars\n        for tick in self.y_ticks:\n            self.ax.axhline(tick, linestyle='-', zorder=1,\n                            color=self.color_dict['litegrey'])\n\n        # Give all labels an almost black color\n        self.ax.xaxis.label.set_color(self.color_dict['almost_black'])\n        self.ax.yaxis.label.set_color(self.color_dict['almost_black'])\n\n        # Include self.figure title\n        self.fig.text(0.5, .87, title, ha='left', va='center',\n                      fontsize=self.title_fontsize)\n\n    def export_fig(self, outfile, filetype, dpi, padding=0.05):\n        self.outfile = outfile\n        self.filetype = filetype\n        self.dpi = dpi\n        self.padding = padding\n        self.fig_name = self.outfile + '.' + self.filetype\n        self.fig.savefig(self.fig_name, format=filetype, transparent=False,\n                         bbox_inches='tight', dpi=self.dpi,\n                         pad_inches=self.padding)\n\n    def show(self):\n        plt.show()\n\n\ndef main():\n    parser = argparse.ArgumentParser(description=\"Find trinucleotide mutation \\\n                                     frequencies from a .mutpos file as \\\n                                     outputted from the duplex sequencing \\\n                                     pipeline\")\n    parser.add_argument(\"-r\", \"--ref_file\",\n                        action=\"store\",\n                        type=str,\n                        dest=\"ref_file\",\n                        help=\"The reference genome in FASTA format.\",\n                        required=True)\n    parser.add_argument(\"-m\", \"--mutpos_file\",\n                        action=\"store\",\n                        type=str,\n                        dest=\"mutpos_file\",\n                        help=\"The mutpos file.\",\n                        required=True)\n    parser.add_argument(\"-o\", \"--output_file\",\n                        action=\"store\",\n                        type=str,\n                        dest=\"output_file\",\n                        help=\"The file results will be written to.\",\n                        required=True)\n    parser.add_argument(\"-c\", \"--minClonality\",\n                        action=\"store\",\n                        type=float,\n                        dest=\"min_clonality\",\n                        default=0,\n                        help=\"The minimum clonality for counting a mutation.\",\n                        required=False)\n    parser.add_argument(\"-C\", \"--maxClonality\",\n                        action=\"store\",\n                        type=float,\n                        dest=\"max_clonality\",\n                        default=1,\n                        help=\"The maximum clonality for counting a mutation.\",\n                        required=False)\n    parser.add_argument(\"-f\", \"--format\",\n                        action=\"store\",\n                        type=str,\n                        default='png',\n                        help=\"The format of the output image file. Popular \\\n                              options are png or svg\",\n                        required=False)\n    parser.add_argument(\"-n\", \"--no_save\",\n                        action=\"store\",\n                        type=bool,\n                        default=False,\n                        nargs='?',\n                        const=True,\n                        help=\"Add -n or --no_save to disable saving the \\\n                              spectrum plot after the program has run.\",\n                        required=False)\n    parser.add_argument(\"-t\", \"--title\",\n                        type=str,\n                        default='',\n                        help=\"Plot title to be overlayed if supplied.\",\n                        required=False)\n    parser.add_argument(\"-l\", \"--labels_off\",\n                        type=bool,\n                        default=False,\n                        nargs='?',\n                        const=True,\n                        help=\"Add -l or --labels_off to remove x-axis labels \")\n    parser.add_argument(\"-rev\", \"--rev\",\n                        type=bool,\n                        default=False,\n                        nargs='?',\n                        const=True,\n                        help=\"Add -r or --rev to reverse spectrum notation. \\\n                              This is useful for notating mutations as \\\n                              purines or pyrimidines.\",\n                        required=False)\n    parser.add_argument(\"-v\", \"--verbose\",\n                        type=bool,\n                        default=False,\n                        nargs='?',\n                        const=True,\n                        help=\"Add -v or --verbose to generate a summary of \\\n                              mutations found while scanning mutpos.\",\n                        required=False)\n\n    args = parser.parse_args()\n\n    clonality = (args.min_clonality, args.max_clonality)\n\n    data = muparse(args.mutpos_file, clonality, args.rev)\n    data.fasta_to_dict(args.ref_file)\n    data.process()\n    data.to_csv(args.output_file)\n\n    if args.verbose:\n        print(data.report)\n\n    if args.no_save:\n        xlabels, heights, panel_titles = data.flatten()\n        z = sigplot(xlabels, heights, title=args.title, rev=args.rev)\n        z.export_fig(args.output_file, args.format, dpi=500)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"siglib.py","file_name":"siglib.py","file_ext":"py","file_size_in_byte":16895,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"638549964","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n\n\turl(r'^$',views.quiz_list), # to list all the quiz\n\turl(r'^(\\d+)/$', views.quiz_view), # to view the quiz\n\turl(r'^list/$',views.quiz_list), # to list all the quiz\n\turl(r'^create/$',views.quiz_create), # to create the quiz\n\turl(r'^delete/(\\d+)/$',views.quiz_delete), # to delete the quiz\n\turl(r'^modify/(\\d+)/$',views.quiz_modify), # to modify the quiz\n\turl(r'^description/(\\d+)/$',views.show_description), # to show description the quiz\n\t\n\n\turl(r'^runjudge/(\\d+)/$',views.run_judge), # to run evaluations on quiz\n\turl(r'^updateratings/(\\d+)/$',views.update_ratings), # to update the ratings of the quiz\n\turl(r'^statistics/(\\d+)/$',views.quiz_statistics), # to show statistics the quiz\n\turl(r'^leaderboard/(\\d+)/$',views.show_leaderboard), # to show description the quiz\n\n\turl(r'^graph/(\\d+)/submission/$',views.submission_graph), # to show submission graph on the quiz\n\turl(r'^graph/(\\d+)/solved/$',views.solved_graph), # to show solved questions graph the quiz\n\n\turl(r'^(\\d+)/question/(\\d+)/$', views.quiz_question_view), # to view the option\n\turl(r'^(\\d+)/question/create/$', views.quiz_question_create), # to create the question\n\turl(r'^(\\d+)/question/delete/(\\d+)/$', views.quiz_question_delete), # to delete the question\n\turl(r'^(\\d+)/question/modify/(\\d+)/$', views.quiz_question_modify), # to modify the question\n\turl(r'^(\\d+)/question/submit/(\\d+)/$', views.quiz_question_submit), # to submit answer\n\n\turl(r'^(\\d+)/question/(\\d+)/option/create/$', views.quiz_option_create), # to create the option\n\turl(r'^(\\d+)/question/(\\d+)/option/delete/(\\d+)/$', views.quiz_option_delete), # to delete the option\n\turl(r'^(\\d+)/question/(\\d+)/option/modify/(\\d+)/$', views.quiz_option_modify), # to modify the option\n\n]\n","sub_path":"quizzes/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"40042980","text":"import os\nimport pid\nimport daemon\n\ndef get_logging_streams():\n    import logging\n    streams = []\n    for handler in logging._handlerList:\n        # in python2.7, they cache handlers in weakref\n        if type(handler).__name__ == 'weakref':\n            handler = handler()\n        if isinstance(handler, logging.FileHandler):\n            streams.append(handler.stream)\n    return streams\n\ndef run_daemon(main, pid_name='daemon', pid_dir='pid', log_dir='log', *args, **kwargs):\n    for path in pid_dir, log_dir:\n        if not os.path.exists(path):\n            os.mkdir(path)\n\n    # make logging stream working in daemon mode\n    logging_streams = get_logging_streams()\n\n    pid_path = os.path.join(pid_dir, '%s.pid' % pid_name)\n    log_path = os.path.join(log_dir, '%s.log' % pid_name)\n    error_path = os.path.join(log_dir, '%s.error.log' % pid_name)\n\n    log = open(log_path, 'a+')\n    error = open(error_path, 'a+')\n    \n    # check pidfile\n    pid.check(pid_path)\n\n    daemon_context = daemon.DaemonContext(stdout=log, stderr=error, files_preserve=logging_streams, working_directory='.')\n    with daemon_context:\n        # write the pidfile\n        pid.write(pid_path)\n        \n        try:\n            main(*args, **kwargs)\n        finally:\n            # ensure we remove the pidfile\n            pid.remove(pid_path)\n","sub_path":"backend/daemon_mode.py","file_name":"daemon_mode.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"233053656","text":"def DaA(Iv, Lungh): #Come Dividere in intervalli qualunque altro numero\n        CifraFissa=Iv/Lungh #Iv è uguale al numero da dividere in n intervalli\n        for i in range(Lungh):#Per tutti i numeri nel raggio di Lungh fai ciò:\n            limiteinferiore=CifraFissa*i#limiteinferiore sarà il prodotto tra una cifra fissa per tutti e i\n            limitesuperiore=limiteinferiore+CifraFissa#limitesuperiore somma tra limiteinferiore e una cifra fissa\n            print(\"da\",limiteinferiore, \"a\", limitesuperiore)\n\n\n\n\n        \n","sub_path":"Python Project/Madeby.py","file_name":"Madeby.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"416782502","text":"import os\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom zentral.utils.osx_package import EnrollmentForm, PackageBuilder\nfrom zentral.contrib.santa.releases import Releases\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\n\n\nclass SantaEnrollmentForm(EnrollmentForm):\n    MONITOR_MODE = 1\n    LOCKDOWN_MODE = 2\n    release = forms.ChoiceField(\n        label=_(\"Release\"),\n        choices=[],\n        initial=\"\",\n        help_text=\"Choose a santa release to be installed with the enrollment package.\",\n        required=False\n    )\n    mode = forms.ChoiceField(\n        label=_(\"Mode\"),\n        choices=((MONITOR_MODE, _(\"Monitor\")),\n                 (LOCKDOWN_MODE, _(\"Lockdown\"))),\n        initial=MONITOR_MODE,\n        help_text=\"In Monitor mode, only blacklisted binaries will be blocked. \"\n                  \"In Lockdown mode, only whitelisted binaries will be allowed to run.\")\n    whitelist_regex = forms.CharField(\n        label=_(\"Whitelist regex\"),\n        help_text=\"In Lockdown mode, executables whose paths are a matched by this regex will be allowed to run.\",\n        required=False\n    )\n    blacklist_regex = forms.CharField(\n        label=_(\"Blacklist regex\"),\n        help_text=\"In Monitor mode, executables whose paths are matched by this regex will be blocked.\",\n        required=False\n    )\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        release_field = self.fields[\"release\"]\n        if self.update_for:\n            release_field.widget = forms.HiddenInput()\n        else:\n            choices = []\n            if not self.standalone:\n                choices.append((\"\", \"Do not include santa\"))\n            # TODO: Async or cached to not slow down the web page\n            r = Releases()\n            for filename, version, created_at, download_url, is_local in r.get_versions():\n                choices.append((filename, filename))\n            release_field.choices = choices\n\n    def get_build_kwargs(self):\n        kwargs = super().get_build_kwargs()\n        kwargs[\"mode\"] = mode = int(self.cleaned_data.get(\"mode\", self.MONITOR_MODE))\n        if not self.update_for:\n            kwargs[\"release\"] = self.cleaned_data[\"release\"]\n        if mode == self.MONITOR_MODE:\n            blacklist_regex = self.cleaned_data.get(\"blacklist_regex\")\n            if blacklist_regex:\n                kwargs[\"blacklist_regex\"] = blacklist_regex\n        elif mode == self.LOCKDOWN_MODE:\n            whitelist_regex = self.cleaned_data.get(\"whitelist_regex\")\n            if whitelist_regex:\n                kwargs[\"whitelist_regex\"] = whitelist_regex\n        return kwargs\n\n    def clean(self):\n        cleaned_data = super().clean()\n        mode = cleaned_data.get(\"mode\")\n        if mode:\n            mode = int(mode)\n            whitelist_regex = cleaned_data.get(\"whitelist_regex\")\n            blacklist_regex = cleaned_data.get(\"blacklist_regex\")\n            if mode == self.LOCKDOWN_MODE and blacklist_regex:\n                self.add_error(\"blacklist_regex\",\n                               \"Can't use a blacklist regex in Lockdown mode.\")\n            elif mode == self.MONITOR_MODE and whitelist_regex:\n                self.add_error(\"whitelist_regex\",\n                               \"Can't use a whitelist regex in Monitor mode.\")\n\n\nclass SantaZentralEnrollPkgBuilder(PackageBuilder):\n    standalone = True\n    name = \"Zentral Santa Enrollment\"\n    form = SantaEnrollmentForm\n    zentral_module = \"zentral.contrib.santa\"\n    package_name = \"zentral_santa_enroll.pkg\"\n    base_package_identifier = \"io.zentral.santa_enroll\"\n    build_tmpl_dir = os.path.join(BASE_DIR, \"build.tmpl\")\n\n    def get_product_archive_title(self):\n        if self.build_kwargs.get(\"release\"):\n            return self.build_kwargs.get(\"product_archive_title\",\n                                         self.name)\n\n    def get_extra_packages(self):\n        extra_packages = []\n        release = self.build_kwargs.get(\"release\")\n        if release:\n            r = Releases()\n            extra_packages.append(r.get_requested_package(release))\n        return extra_packages\n\n    def extra_build_steps(self, mode=None, blacklist_regex=None, whitelist_regex=None, **kwargs):\n        if mode is None:\n            mode = self.form.MONITOR_MODE\n        elif mode not in {self.form.MONITOR_MODE, self.form.LOCKDOWN_MODE}:\n            raise ValueError(\"Unknown monitor mode {}\".format(mode))\n        config_plist = self.get_root_path(\"var/db/santa/config.plist\")\n        self.replace_in_file(config_plist,\n                             ((\"%TLS_HOSTNAME%\", self.get_tls_hostname()),\n                              (\"%MODE%\", str(mode))))\n        postinstall_script = self.get_build_path(\"scripts\", \"postinstall\")\n        self.replace_in_file(postinstall_script,\n                             ((\"%API_SECRET%\", self.make_api_secret()),\n                              (\"%TLS_HOSTNAME%\", self.get_tls_hostname())))\n        tls_server_certs_install_path = self.include_tls_server_certs()\n        self.set_plist_keys(config_plist,\n                            [(\"ServerAuthRootsFile\", tls_server_certs_install_path)])\n        if blacklist_regex:\n            self.set_plist_keys(config_plist, [(\"BlacklistRegex\", blacklist_regex)])\n        if whitelist_regex:\n            self.set_plist_keys(config_plist, [(\"WhitelistRegex\", whitelist_regex)])\n","sub_path":"zentral/contrib/santa/osx_package/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":5399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"608158089","text":"from .models import * \nfrom django.views.generic import ListView, DetailView, View, CreateView\nfrom django.views.generic.detail import SingleObjectMixin\n\nclass ContextMixin(object):\n\tdef get_context_data(self, **kwargs):\n\t\tcontext = super(ContextMixin, self).get_context_data(**kwargs)\n\t\tganres = Ganre.objects.all()\n\t\tyears = Year.objects.all()\n\t\tsearch = self.request.GET.get('search')\n\t\tsearch_year = self.request.GET.get('year')\n\t\tquery = \"\"\n\t\tif search:\n\t\t\tquery += f'search={search}&'\n\t\tif search_year:\n\t\t\tquery += f'year={search_year}&'\t\n\n\t\tcontext['years'] = years\n\t\tcontext['search'] = query\n\t\tcontext['ganres'] = ganres\n\t\treturn context\n\n","sub_path":"movies/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"532996319","text":"# API\nfrom secondguard import get_encryption_info, get_decryption_info\n\n# Utility Functions\nfrom secondguard.utils import is_valid_seed_hex, is_seed_hash_pair, derive_child_key\n\n# Crypto\nfrom secondguard.crypto import encrypt, decrypt\n\n\nKEY_LENGTH_IN_BYTES = 16\n\n\ndef was_aescfb_sg_encrypted(text):\n    base_err = 'This string not originally encrypted with AES CFB using SG and this module'\n    if not text.startswith('SG-AESCFB-v1$'):\n        return False, base_err + ' (it would start with `SG-AESCFB-v1$`)'\n    if not text.count('$') == 5:\n        return False, base_err + ' (it would have 5 $s)'\n\n\ndef assert_was_aescfb_encrypted(text):\n    was_encrypted, err_msg = was_aescfb_sg_encrypted(text=text)\n    if not was_encrypted:\n        raise Exception(err_msg)\n\n\ndef sg_encrypt_secret(secret_to_encrypt, seed_pub_hash_hex, sg_api_token):\n    '''\n    Encrypt a list of secrets_to_encrypt using a unique key from secondguard for each one\n\n    Must supply an sg_api_token and seed_pub_hash_hex to use.\n    '''\n    return sg_batch_encrypt_secrets(\n            secrets_to_encrypt=[secret_to_encrypt, ],\n            seed_pub_hash_hex=seed_pub_hash_hex,\n            sg_api_token=sg_api_token,\n            )[0]\n\n\ndef sg_batch_encrypt_secrets(secrets_to_encrypt, seed_pub_hash_hex, sg_api_token):\n    '''\n    Batch method for sg_encrypt_secret\n\n    Local encryption takes longer the longer your data to encrypt is.\n\n    If you're encrypting a large document, batching will not really\n    speed up the whole process (and complicates your code).\n\n    If you're encrypting lots of small data points (say SSNs), then batching\n    your API calls in groups of 5-500 can have large performance increases.\n    '''\n\n    assert sg_api_token, 'sg_api_token required'\n    is_valid, err_msg = is_valid_seed_hex(seed_pub_hash_hex)\n    if not is_valid:\n        raise Exception('Invalid `seed_pub_hash_hex`: %s' % err_msg)\n\n    assert type(secrets_to_encrypt) in (list, tuple), \"secrets_to_encrypt must be a list or tuple\"\n    assert len(secrets_to_encrypt) < 1000, \"Max of 1000 secrets to encrypt\"\n\n    api_response = get_encryption_info(\n            seed_pub_hash=seed_pub_hash_hex,\n            api_token=sg_api_token,\n            num_keys=len(secrets_to_encrypt),\n            version='v1',\n            )\n\n    if 'error' in api_response:\n        raise Exception(api_response['error'])\n    if 'errors' in api_response:\n        raise Exception(api_response['errors'])\n\n    to_store_list = []\n    for cnt, obj in enumerate(api_response):\n        seed_and_nonce = '%s@%s' % (seed_pub_hash_hex, obj['nonce'])\n\n        secret_message = secrets_to_encrypt[cnt]\n\n        to_store = encrypt(\n                secret_message=secret_message,\n                key=obj['key'][:KEY_LENGTH_IN_BYTES],\n                iv=None,\n                )\n\n        prefix, encoding, b64_iv_and_ciphertext = to_store.split('$')\n\n        # add seed_pub_hash_hex & nonce\n        to_store_with_sg_data = '$'.join(\n                (\n                    prefix,\n                    encoding,\n                    seed_and_nonce,  # added\n                    b64_iv_and_ciphertext,\n                    )\n                )\n        to_store_list.append(to_store_with_sg_data)\n\n    return to_store_list\n\n\ndef sg_decrypt_secret(to_decrypt, api_token):\n    '''\n    Decypt an item from your database using your api_token.\n    '''\n\n    prefix, encoding, seed_and_nonce, b64_iv_and_ciphertext = to_decrypt.split('$')\n\n    seed_pub_hash_hex, nonce = seed_and_nonce.split('@')\n\n    is_valid, err_msg = is_valid_seed_hex(seed_pub_hash_hex)\n    if not is_valid:\n        raise Exception('Invalid `seed_pub_hash_hex`: %s' % err_msg)\n\n    # TODO: batch these API calls\n    api_response = get_decryption_info(\n            seed_pub_hash=seed_pub_hash_hex,\n            api_token=api_token,\n            nonce=nonce,\n            version='v1',\n            )\n\n    if 'error' in api_response:\n        raise Exception(api_response['error'])\n    if 'errors' in api_response:\n        raise Exception(api_response['errors'])\n\n    key = api_response['key'][:KEY_LENGTH_IN_BYTES]\n\n    b64_text_to_decrypt = '$'.join(\n            (\n                prefix,\n                encoding,\n                # no seed_and_nonce\n                b64_iv_and_ciphertext\n                )\n            )\n\n    return decrypt(\n            b64_text_to_decrypt=b64_text_to_decrypt,\n            key=key,\n            )\n\n\ndef sg_decrypt_from_priv_seed(to_decrypt, private_seed_hex):\n    '''\n    For use with a private seed on your local server.\n\n    WARNING: placing the private seed on your local server defeats the whole\n    purpose of rate limiting. If an attacker has access to your encrypted\n    database and gets access to this seed on your server, they can easily\n    decrypt the whole database. Only use this method if you know what you're\n    doing. It's main purpose is recovery in case SecondGuard is down.\n    '''\n    prefix, encoding, seed_and_nonce_str, b64_iv_and_ciphertext = to_decrypt.split('$')\n    seed_pub_hash_hex, nonce = seed_and_nonce_str.split('@')\n\n    valid_pair = is_seed_hash_pair(\n            private_seed_hex=private_seed_hex,\n            seed_public_hash_hex=seed_pub_hash_hex,\n            )\n    if not valid_pair:\n        err_msg = seed_pub_hash_hex\n        err_msg += ' is not a valid private seed for the data you are trying to decrypt.'\n        raise Exception(err_msg)\n\n    unique_key = derive_child_key(\n            private_seed_hex=private_seed_hex,\n            nonce=nonce,\n            )\n\n    b64_text_to_decrypt = '$'.join(\n            (\n                prefix,\n                encoding,\n                # no seed_and_nonce_str\n                b64_iv_and_ciphertext,\n                )\n            )\n\n    return decrypt(\n            b64_text_to_decrypt=b64_text_to_decrypt,\n            key=unique_key[:KEY_LENGTH_IN_BYTES],\n            )\n","sub_path":"secondguard/xlimit.py","file_name":"xlimit.py","file_ext":"py","file_size_in_byte":5869,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"545890297","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('tragopan', '0080_auto_20150824_1047'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='wimsnuclidedata',\n            name='id_self_defined',\n            field=models.PositiveSmallIntegerField(blank=True, null=True, unique=True),\n        ),\n    ]\n","sub_path":"tragopan/migrations/0081_wimsnuclidedata_id_self_defined.py","file_name":"0081_wimsnuclidedata_id_self_defined.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"334288691","text":"\"\"\"Generic CTR plugin class.\n\nCopyright 2013 Aviat Networks\n\nThis file contains class for generic CTR plugin. All CTR plugins are inherited from this class.\"\"\"\n\nimport time\nimport logging\nimport re\nimport components.common.entityindex as EntityModule\n\n\nclass CtrPluginException():\n    '''Exception object for CSR plugins'''\n    def __init__(self, component, message):\n        self.component = component\n        self.message = message\n\n    def __str__(self):\n        return self.component.__class__.__name__ + ': ' + str(self.message)\n\n\nclass CtrPluginAlarm(object):\n    '''Alarm object for CSR plugins'''\n    def __init__(self, entity_id,\n                       alarm_id,\n                       description,\n                       event_type,\n                       severity,\n                       cause,\n                       last_change,\n                       change_count):\n        self.entity_id = entity_id\n        self.alarm_id = alarm_id\n        self.description = description\n        self.event_type = event_type\n        self.severity = severity\n        self.cause = cause\n        self.last_change = last_change\n        self.change_count = change_count\n\n    def __repr__(self):\n        return \"%s\" % (self.description)\n\n    def __str__(self):\n        return \"%s\" % (self.description)\n\n\nclass CtrPlugin(object):\n    ''' CSR plugin object.'''\n    def __init__(self, csr, name, slot, initial):\n        self.name = name\n        self.slot = slot\n        self.csr = csr\n        self.initial = initial\n\n    def __str__(self):\n        return \"%s: %s at %s\" % (self.csr.name, self.name, self.slot)\n\n    @staticmethod\n    def get_plugin(csr, name, slot, initial=True):\n        '''returns CTR plugin object instance per name/slot'''\n        CtrPlugin.initial = initial\n        '''returns CTR plugin object instance per name/slot'''\n        if name == 'MB':\n            from mainboard import Mainboard\n            return Mainboard(csr, slot, initial)\n        elif name == 'PWR':\n            from pwr import PwrPlugin\n            return PwrPlugin(csr, slot, initial)\n        elif name == 'POEx2':\n            from poex2 import Poex2Plugin\n            return Poex2Plugin(csr, slot, initial)\n        elif name == 'RACx2':\n            from racx2 import Racx2Plugin\n            return Racx2Plugin(csr, slot, initial)\n        elif name == 'RACx1':\n            from racx1 import Racx1Plugin\n            return Racx1Plugin(csr, slot, initial)\n        else:\n            raise\n\n    def configure(self):\n        ''' perform configuration routine per this CSR plugin.'''\n        if self._detect():\n            if self.initial: #if desired re-initialize the Plug-in\n                logging.info('send plug-in unset')\n                self._unset()\n                time.sleep(3)\n            # set it if expected\n            \n            if not self._expect():\n                logging.info('send plug-in set')\n                self._set()\n                logging.info('wait 30 sec')\n                time.sleep(30)\n            # check if set fails\n            if not self._expect():\n                msg = 'Failed to set plugin %s in slot %d' % (self.name, self.slot)\n                logging.error(msg)\n                raise CtrPluginException(self, msg)\n        else:\n            msg = 'Failed to detect plugin %s in slot %d' % (self.name, self.slot)\n            logging.error(msg)\n            raise CtrPluginException(self, msg)\n\n    def _detect(self):\n        '''call detect cli command on this CSR plugin.'''\n        cmd = 'show slot ' + str(self.slot)\n        slot_info = self.send(cmd).split('\\n')\n        return self.name in slot_info[1]\n\n    def _expect(self):\n        '''call expect cli command on this CSR plugin.'''\n        cmd = 'show slot ' + str(self.slot)\n        slot_info = self.send(cmd).split('\\n')\n        return self.name in slot_info[0]\n\n    def _set(self):\n        '''trigger slot initialization per CSR plugin.'''\n        cmd = ['c t', 'slot ' + str(self.slot) + ' ' + self.name, 'end']\n        return self.send(cmd, timeout=10)\n\n    def _unset(self):\n        '''trigger slot removal per CSR plugin.'''\n        cmd = ['c t', 'no slot ' + str(self.slot), 'end']\n        self.send(cmd, timeout=10)\n\n    def send(self, command, itype='cli', timeout=None):\n        '''send a command for requested interface type.'''\n        return self.csr.sendcli(command, timeout)\n\n    def get_alarms(self, entity=None, timeout=None):\n        '''returns dictionary of alarms (keys = severity, items = list of Alarm objects) per requested entity id.'''\n        '''severity = 1 - cleared, 2 - indetermined, 3 - critical, 4 - major, 5 - minor, 6 - warning'''\n        alarms_dict = {n: [] for n in range(1, 7)}\n\n        if entity is not None:\n            entity_index = entity\n        else:\n            entity_index = self.entity_index\n\n        # skip headers\n        all_alarms = self.send('show alarm brief entity ' + str(entity_index), 'cli', timeout).split('\\n')[2:]\n        for line in all_alarms:\n            #print line\n            if line == '':\n                continue\n            # arguments for Alarm object\n            args = []\n            alarm_info = self.send('show alarm id ' + str(line.split()[1]) + ' entity '+ str(entity_index), 'cli', timeout).split('\\n')\n            if 'No matching alarm instances found.' in alarm_info:\n                continue\n            args.append(entity_index)\n            # skip headers\n            for aline in alarm_info[2:]:\n                #print aline\n                if aline == '':\n                    continue\n                args.append(re.split(':|=', aline)[1].strip())\n            # create a new Alarm instance\n            alarm = CtrPluginAlarm(*args)\n            # add alarm instance to dictionary \n            alarms_dict[int(alarm.severity.split(' - ')[0])].append(alarm)\n\n        return alarms_dict\n","sub_path":"components/plugins/ctr_plugin.py","file_name":"ctr_plugin.py","file_ext":"py","file_size_in_byte":5845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"635056641","text":"import inspect\n\nPOS_TYPE = 'misc::position const&'\nINT_TYPE = 'int'\nSTR_TYPE = 'std::string const&'\nPOS_LIST_TYPE = 'std::vector<misc::position> const&'\n\nMAP_FIELD_TYPENAME = {\n    POS_TYPE : 'misc::position const ',\n    INT_TYPE : 'int const ',\n    STR_TYPE : 'std::string const ',\n    POS_LIST_TYPE: 'std::vector<misc::position> const ',\n}\n\nERROR_VAR = 'has_error'\nMARK_ERROR = ERROR_VAR + '=true;'\n\ndef lineno():\n    return '''\\n#line {line} \"{file}\"\\n'''.format(\n                line=inspect.currentframe().f_back.f_lineno, file=__file__)\n\nclass Param:\n    def __init__(self, tp, name):\n        self.tp = tp\n        self.name = name\n\n    def build(self):\n        return self.tp + ' ' + self.name\n\nclass ReportFunc:\n    def __init__(self, name, implements, *params):\n        self.name = name\n        self.implements = implements\n        self.params = params\n\n    def rec_name(self):\n        return self.name[0].upper() + self.name[1:]\n\n    def class_name(self):\n        return self.rec_name() + 'Rec'\n\n    def storage_name(self):\n        return self.name + 'Recs'\n\n    def build_decl(self):\n        return 'void {name}({params});'.format(\n                    name=self.name, params=','.join([ p.build() for p in self.params ]))\n\n    def build_impl(self):\n        return ('void error::{name}({params})'.format(\n                    name=self.name, params=','.join([ p.build() for p in self.params ])) +\n                '{' + MARK_ERROR + self.implements + '}')\n\n    def build_test_decl(self):\n        return ('''struct ''' + self.class_name() + ''' {\\n''' +\n                '''{explicit}{class_name}({params}){colon}{init_list}\\n'''.format(\n                    explicit=('explicit ' if len(self.params) == 1 else ''),\n                    class_name=self.class_name(),\n                    params=','.join([ p.build() for p in self.params ]),\n                    colon=(':' if len(self.params) > 0 else ''),\n                    init_list=','.join([ (p.name + '(' + p.name + ')') for p in self.params ])) +\n                '''{}\\n''' +\n                '\\n'.join([ (MAP_FIELD_TYPENAME[p.tp] + p.name + ';') for p in self.params ]) +\n                '''};\\n''' +\n                '''std::vector<{class_name}> get{class_name}s();'''.format(\n                                                                class_name=self.class_name()))\n\n    def build_test_clearer(self):\n        return self.storage_name() + '.clear();'\n\n    def build_test_impl(self):\n        return ('std::list<{class_name}> {storage_name};\\n'.format(\n                    class_name=self.class_name(), storage_name=self.storage_name()) +\n                'void error::{name}({params})\\n'.format(\n                    name=self.name, params=','.join([ p.build() for p in self.params ])) +\n                '{' + MARK_ERROR +\n                '\\n{storage_name}.push_back({class_name}({arguments}));\\n'.format(\n                    storage_name=self.storage_name(),\n                    class_name=self.class_name(),\n                    arguments=','.join([ p.name for p in self.params ])) +\n                '}\\n' +\n                'std::vector<{class_name}> test::get{class_name}s()\\n'\n                        .format(class_name=self.class_name()) + '{' +\n                'return std::vector<{class_name}>({storage_name}.begin(), {storage_name}.end());'\n                        .format(class_name=self.class_name(), storage_name=self.storage_name()) +\n                '}\\n')\n\nERROR_REPORTS = [\n\nReportFunc(\n'tabAsIndent',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    use tab as indent is forbidden.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'invalidChar',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    invalid character \" << char(character)\n          << \" (decimal value: \" << character << \").\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(INT_TYPE, 'character')),\n\nReportFunc(\n'reservedWord',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    use reserved word: \" << token << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'token')),\n\nReportFunc(\n'exportToIdent',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    export to identifier is invalid: \" << ident << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'ident')),\n\nReportFunc(\n'unexpectedToken',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    unexpected \" << image << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'image')),\n\nReportFunc(\n'emptyLookupKey',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    an expression is supposed as lookup key.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'invalidEmptyExpr',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    expression omitted.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'excessiveExpr',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    more than one expressions in parentheses.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'tooManySliceParts',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    more than 3 expressions as list slice.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'invalidName',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    invalid name; an identifier is supposed here.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'invalidLeftValue',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    invalid left value.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'sliceStepOmitted',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    slice step omitted.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'elseNotMatchIf',\nlineno() + '''\nstd::cerr << else_pos.str() << std::endl;\nstd::cerr << \"    `else' does not match an `if'.\" << std::endl;\n'''\n, Param(POS_TYPE, 'else_pos')),\n\nReportFunc(\n'ifAlreadyMatchElse',\nlineno() + '''\nstd::cerr << this_else_pos.str() << std::endl;\nstd::cerr << \"    another `else' already matches the `if' at \" << prev_else_pos.str() << std::endl;\n'''\n, Param(POS_TYPE, 'prev_else_pos'), Param(POS_TYPE, 'this_else_pos')),\n\nReportFunc(\n'incompleteConditional',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    incomplete conditional expression, `else' is expected\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'invalidIndent',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    invalid indentation\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'unexpectedEof',\nlineno() + '''\nstd::cerr << \"Unexpected end of file; expression not finished\" << std::endl;\n'''\n),\n\nReportFunc(\n'asyncPlaceholderNotArgument',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    asynchronous placeholder should appear as an arugment.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'asyncParamNotExpr',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    asynchronous parameter should appear as an expression.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'moreThanOneAsyncPlaceholder',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    more than one asynchronous placeholders in one call.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'divZero',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    divided by zero.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'flowTerminated',\nlineno() + '''\nstd::cerr << this_pos.str() << std::endl;\nstd::cerr << \"    flow already terminated at \" << prev_pos.str() << std::endl;\n'''\n, Param(POS_TYPE, 'this_pos'), Param(POS_TYPE, 'prev_pos')),\n\nReportFunc(\n'forbidDefFunc',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    \" << \"attempt define Function `\" << name << \"' but forbidden here.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'name')),\n\nReportFunc(\n'forbidDefName',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    \" << \"attempt define name `\" << name << \"' but forbidden here.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'name')),\n\nReportFunc(\n'nameAlreadyInLocal',\nlineno() + '''\nstd::cerr << this_def_pos.str() << std::endl;\nstd::cerr << \"    name `\" << name << \"' already defined.\" << std::endl;\nstd::cerr << \"    see previous definition in local at \" << prev_def_pos.str() << std::endl;\n'''\n, Param(POS_TYPE, 'prev_def_pos'), Param(POS_TYPE, 'this_def_pos'), Param(STR_TYPE, 'name')),\n\nReportFunc(\n'nameRefBeforeDef',\nlineno() + '''\nstd::cerr << def_pos.str() << std::endl;\nstd::cerr << \"    name `\" << name << \"' definition after reference. see references at:\"\n          << std::endl;\nstd::for_each(ref_positions.begin()\n            , ref_positions.end()\n            , [&](misc::position const& pos)\n              {\n                  std::cerr << \"    - \" << pos.str() << std::endl;\n              });\n'''\n, Param(POS_TYPE, 'def_pos'), Param(POS_LIST_TYPE, 'ref_positions'), Param(STR_TYPE, 'name')),\n\nReportFunc(\n'nameNotDef',\nlineno() + '''\nstd::cerr << ref_pos.str() << std::endl;\nstd::cerr << \"    name `\" << name << \"' not defined.\" << std::endl;\n'''\n, Param(POS_TYPE, 'ref_pos'), Param(STR_TYPE, 'name')),\n\nReportFunc(\n'binaryOpNotAvai',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    no available binary operation \" << op_img << \" for type `\" << lhst_name\n          << \"' and `\" << rhst_name << \"'.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'op_img'), Param(STR_TYPE, 'lhst_name'),\n  Param(STR_TYPE, 'rhst_name')),\n\nReportFunc(\n'preUnaryOpNotAvai',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    no available prefix unary operation \" << op_img << \" for type `\" << rhst_name\n          << \"'.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'op_img'), Param(STR_TYPE, 'rhst_name')),\n\nReportFunc(\n'condNotBool',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    condition type is not boolean, actual type: \" << actual_type << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'actual_type')),\n\nReportFunc(\n'returnNotAllowedInPipe',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    return statement not allowed in pipeline.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'pipeReferenceNotInListContext',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    pipeline reference not in list context.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos')),\n\nReportFunc(\n'invalidPropertyName',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    invalid property name: \" << expr << std::endl;\nstd::cerr << \"    the expression could not be folded.\" << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'expr')),\n\nReportFunc(\n'importReservedWord',\nlineno() + '''\nstd::cerr << pos.str() << std::endl;\nstd::cerr << \"    import reserved word as name: \" << name << std::endl;\n'''\n, Param(POS_TYPE, 'pos'), Param(STR_TYPE, 'name')),\n\n]\n\ndef write_errors_header():\n    with open('report/errors.h', 'w') as out:\n        out.write(lineno() + '''\n            #ifndef __STEKIN_REPORT_ERRORS_H__\n            #define __STEKIN_REPORT_ERRORS_H__\n\n            #include <vector>\n            #include <string>\n\n            #include <misc/pos-type.h>\n\n            namespace error {\n                bool hasError();''' +\n                '\\n'.join([ r.build_decl() for r in ERROR_REPORTS ]) +\n            '''}\n\n            #endif /* __STEKIN_REPORT_ERRORS_H__ */''')\n        out.flush()\n\ndef write_errors_impl():\n    with open('report/errors.cpp', 'w') as out:\n        out.write(lineno() + '''\n            #include <iostream>\n            #include <algorithm>\n\n            #include \"errors.h\"\n\n            static bool has_error = false;\n\n            namespace grammar { extern int lineno; }\n\n            bool error::hasError()\n            {\n                return has_error;\n            }\n\n            void yyerror(std::string const& msg)\n            {\n                has_error = true;\n                std::cerr << \"Line \" << grammar::lineno << \":\" << std::endl;\n                std::cerr << \"    \" <<  msg << std::endl;\n            }''' +\n            '\\n'.join([ r.build_impl() for r in ERROR_REPORTS ]))\n        out.flush()\n\ndef write_errors_test_header():\n    with open('test/phony-errors.h', 'w') as out:\n        out.write('''\n            #ifndef __STEKIN_TEST_PHONY_ERRORS_H__\n            #define __STEKIN_TEST_PHONY_ERRORS_H__\n\n            #include <vector>\n\n            #include <report/errors.h>\n\n            namespace test {\n                void clearErr();\n            ''' +\n            ''.join([ r.build_test_decl() for r in ERROR_REPORTS ]) +\n            '''}\n\n            #endif /* __STEKIN_TEST_PHONY_ERRORS_H__ */''')\n        out.flush()\n\ndef write_errors_test_impl():\n    with open('test/phony-errors.cpp', 'w') as out:\n        out.write('''\n            #include <list>\n\n            #include \"phony-errors.h\"\n\n            using namespace test;\n\n            static bool has_error = false;\n\n            void yyerror(std::string const&)\n            {\n                has_error = true;\n            }\n\n            bool error::hasError()\n            {\n                return has_error;\n            }''' +\n            '\\n'.join([ r.build_test_impl() for r in ERROR_REPORTS ]) +\n            '''void test::clearErr()\n            {\n                has_error = false;''' +\n            '\\n'.join([ r.build_test_clearer() for r in ERROR_REPORTS ]) +\n            '''}''')\n        out.flush()\n\nwrite_errors_header()\nwrite_errors_impl()\nwrite_errors_test_header()\nwrite_errors_test_impl()\n","sub_path":"codegen/gen-report.py","file_name":"gen-report.py","file_ext":"py","file_size_in_byte":13727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"465193980","text":"\n# import numpy as np\n# test=np.load('./data/x_train.npy',encoding = \"latin1\")  #加载文件\n# doc = open('1.txt', 'a')  #打开一个存储文件，并依次写入\n# print(test, file=doc)\n\n\nimport numpy as np\nfrom math import log\nimport matplotlib.pyplot as plt\ntemp = np.zeros((10))\ntrain_set=np.load('./data/x_train.npy')\ntrain_label=np.load('./data/y_train.npy')\ntest_set=np.load('./data/x_test.npy')\ntest_label=np.load('./data/y_test.npy')\nlikelihood=np.zeros((784,256,10))\nold_w = np.zeros((785,10))\nw = np.transpose(old_w)\nnew_train_set=train_set.reshape((50000,784))\nnew_test_set=test_set.reshape((10000,784))\naccuracy=0.0\nscore = 0\nimage_type = 0\nnew_train_set = train_set.reshape((50000,784))\nARRAY = np.ones(50000)\nnew_train_set = np.insert(new_train_set,784,values=ARRAY, axis=1)\nfor i in range(new_train_set.shape[0]):\n    #print(i)\n    score = 0\n    for j in range(w.shape[0]):\n        # print(1)\n        new_score = 0\n        new_score_product = np.multiply(w[j],new_train_set[i])\n        new_score = np.sum(new_score_product)\n        if new_score > score:\n                # print(3)\n                score = new_score\n                image_type = j\n    if image_type!=train_label[i]:\n            # print(4)\n            for p in range(w.shape[1]):\n                # print(5)\n                w[image_type][p] -= new_train_set[i][p]\n                w[train_label[i]][p] += new_train_set[i][p]\n\naccuracy = 0\npred_label = np.zeros((len(test_set)))\nnew_test_set = test_set.reshape((10000,784))\nbias = np.ones(10000)\nmax_list = []\nmin_list = []\nnew_test_set = np.insert(new_test_set,784,values=bias,axis=1)\n# for i in range(new_test_set.shape[0]):\n#     score = 0\n#     for j in range(w.shape[0]):\n#         new_score = 0\n#         new_score_product = np.multiply(w[j],new_test_set[i])\n#         new_score = np.sum(new_score_product)\n#         if new_score>score:\n#             image_type = j\n#             score = new_score\n#     pred_label[i] = image_type\n# for i in range(pred_label.size):\n#     if pred_label[i] == test_label[i]:\n#         accuracy += 1\nclasses = np.array([\"T-shirt/top\",\"Trouser\",\"Pullover\",\"Dress\",\n           \"Coat\",\"Sandal\",\"Shirt\",\"Sneaker\",\"Bag\",\"Ankle boot\"])\nfor j in range(w.shape[0]):\n    max_score = float(\"-inf\")\n    min_score = float(\"inf\")\n\n    for i in range(new_test_set.shape[0]):\n        if test_label[i] == j:\n            new_score_product = np.multiply(w[j],new_test_set[i])\n            new_score = np.sum(new_score_product)\n        if max_score<new_score:\n            max_score = new_score\n            max_index = i\n        if min_score>new_score:\n            min_score = new_score\n            min_index = i\n    max_list.append(max_index)\n    min_list.append(min_index)\nfig, ax = plt.subplots(2, 5, figsize=(12, 5))\nfor i in range(10):\n        ax[i%2, i//2].imshow(new_test_set[min_list[i],:784].reshape((28, 28)), cmap=\"Greys\")\n        ax[i%2, i//2].set_xticks([])\n        ax[i%2, i//2].set_yticks([])\n        ax[i%2, i//2].set_title(classes[i])\nplt.show()\naccuracy = accuracy/10000\nprint(accuracy)\n","sub_path":"mp3/part1/plot_perceptron.py","file_name":"plot_perceptron.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"161809780","text":"import ftplib\nimport os\nfrom pathlib import Path\nimport tempfile\n\nfrom django.conf import settings\nimport pytest\n\n\n@pytest.fixture\ndef localfile():\n    with tempfile.NamedTemporaryFile() as f:\n        f.write(b\"some foo bar content\")\n        f.seek(0)\n        # before test\n        yield f\n        # after test\n\n\n@pytest.fixture\ndef file_url(file_on_storage, storage_service_instance):\n    root_url = storage_service_instance[\"root_url\"]\n    fname = os.path.basename(file_on_storage)\n    return root_url + fname + \"/\"\n\n\ndef ftp_checker(ip_address, port):\n    try:\n        ftplib.FTP(ip_address)\n        return True\n    except Exception:\n        return False\n\n\n@pytest.fixture\ndef storage_service_instance(docker_services):\n    # remove all files in root dir\n    root_dir = \"/tmp/test-ftpdserver-pyconfr\"\n    Path(root_dir).mkdir(parents=True, exist_ok=True)\n    for f in os.listdir(root_dir):\n        os.remove(root_dir + \"/\" + f)\n\n    docker_services.start(\"ftpd_server\")\n    docker_services.wait_for_service(\n        \"ftpd_server\", 21, check_server=ftp_checker\n    )\n\n    return {\"root_dir\": root_dir, \"root_url\": \"/api/v1/files/\"}\n\n\n@pytest.fixture\ndef file_on_storage(storage_service_instance):\n    root_dir = storage_service_instance[\"root_dir\"]\n    fpath = os.path.join(root_dir, \"file-to-test.txt\")\n    with open(fpath, \"wb\") as f:\n        f.write(b\"some foo bar content\")\n\n    return fpath\n\n\n@pytest.fixture\ndef mocked_conf(monkeypatch):\n    monkeypatch.setattr(\n        settings,\n        \"DEFAULT_FILE_STORAGE\",\n        \"storages.backends.ftp.FTPStorage\",\n        raising=False,\n    )\n    monkeypatch.setattr(\n        settings,\n        \"FTP_STORAGE_LOCATION\",\n        \"ftp://pyconfr:pyconfr@localhost:21\",\n        raising=False,\n    )\n","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"411793563","text":"from django.conf.urls import url\n\nfrom .views import LoginView, RegistrationView, LogoutView, GetUserView\n\nurlpatterns = [\n    url(r'^login/$', LoginView.as_view(), name=\"login\"),\n    url(r'^registration/$', RegistrationView.as_view(), name=\"registration\"),\n    url(r'^logout/$', LogoutView.as_view(), name=\"logout\"),\n    url(r'^user/$', GetUserView.as_view(), name=\"get_user\"),\n]","sub_path":"server/burgers/auth_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"8542475","text":"# encoding:utf-8\r\n\"\"\"\r\n@author:Liwenhao\r\n@e-mail:wh.chnb@gmail.com\r\n@file:productionFollowUpDetails.py\r\n@time:2019/6/5 19:29\r\n@desc:\r\n@fwd : 修改urllib3 下fileds.py 源码(45,46)\r\n\"\"\"\r\nimport os\r\nimport sys\r\nimport time\r\nimport json\r\nimport urllib3\r\nimport requests\r\nfrom alibaba.public import Public\r\nfrom urllib.parse import urlencode\r\n# urllib3.contrib.pyopenssl.extract_from_urllib3()\r\n# requests.packages.urllib3.disable_warnings()\r\n# ssl._create_default_https_context = ssl._create_unverified_context\r\nurllib3.disable_warnings()\r\n\r\n\r\nclass ProductionFollowUpDetails(Public):\r\n    def __init__(self, account, order_id, sku, express_info):\r\n        self.account = account\r\n        self.sku = sku\r\n        self.order_id = order_id\r\n        self.express_info = express_info\r\n        super(ProductionFollowUpDetails, self).__init__(self.account)\r\n        self.ctoken = self.get_ctoken()\r\n        self.code_status_dict = self.get_column_code()\r\n\r\n    # 获取本地图片信息\r\n    def get_local_img(self, sku):\r\n        local_path = r'\\\\192.168.1.98\\公共共享盘\\1_产品文档\\实物\\{}'.format(sku)\r\n        img_files = os.listdir(local_path)\r\n        img_files_dict = {\r\n            # 正面图\r\n            'picturesOfSampleProductsFront': [],\r\n            # 背面图\r\n            'picturesOfSampleProductsBack': [],\r\n            # 侧面图\r\n            'picturesOfSampleProductsSide': [],\r\n            # 视频\r\n            'videoOfSampleProducts': [],\r\n            # 细节图\r\n            'picturesOfProductsDetails': [],\r\n            # 远景图\r\n            'picturesOfAllLabeledProductsLongShot': [],\r\n            # 包装后成品图\r\n            'picturesOfPackingFinishedProduct':[],\r\n            # 批量成品堆放图\r\n            'picturesOfFinishedProducts': [],\r\n            # 产品装箱未封箱图\r\n            'picturesOfPackedProducts': [],\r\n            # 产品装箱封箱图\r\n            'picturesOfLabeledBoxesOrPallets': [],\r\n            # 产品外箱图贴仓库标签或托盘图\r\n            'pictureOfProdcutsInPalletWithShippingLabels': [],\r\n        }\r\n        for img_file in img_files:\r\n            img_path = local_path + r'\\%s' % img_file\r\n            img_size = os.path.getsize(img_path)\r\n            img_data = (img_file, img_size, img_path)\r\n            if '正面' in img_file:\r\n                img_files_dict['picturesOfSampleProductsFront'].append(img_data)\r\n            elif '背面' in img_file:\r\n                img_files_dict['picturesOfSampleProductsBack'].append(img_data)\r\n            elif '侧面' in img_file:\r\n                img_files_dict['picturesOfSampleProductsSide'].append(img_data)\r\n            elif '视频' in img_file:\r\n                img_files_dict['videoOfSampleProducts'].append(img_data)\r\n            elif '包装图1' in img_file or '组合图' in img_file or '配件图' in img_file:\r\n                img_files_dict['picturesOfProductsDetails'].append(img_data)\r\n            elif '包装底部' in img_file:\r\n                img_files_dict['picturesOfAllLabeledProductsLongShot'].append(img_data)\r\n            elif '包装图2' in img_file:\r\n                img_files_dict['picturesOfPackingFinishedProduct'].append(img_data)\r\n            elif '堆货图1' in img_file:\r\n                img_files_dict['picturesOfFinishedProducts'].append(img_data)\r\n            elif '一号箱空箱照' in img_file:\r\n                img_files_dict['picturesOfPackedProducts'].append(img_data)\r\n            elif '一号箱满箱照' in img_file:\r\n                img_files_dict['picturesOfLabeledBoxesOrPallets'].append(img_data)\r\n            elif '一号箱封箱照' in img_file:\r\n                img_files_dict['pictureOfProdcutsInPalletWithShippingLabels'].append(img_data)\r\n        return img_files_dict\r\n\r\n    # 检测图片\r\n    def check_file(self, mainCrmFileId):\r\n        url = 'https://crm-file.alibaba-inc.com/crmfile/token/checkFile.json'\r\n        data = {\r\n            'chunks': 1,\r\n            'mainCrmFileId': mainCrmFileId\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(self.order_id)\r\n        headers['Origin'] = 'onetouch.alibaba.com'\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n\r\n    # 上传图片\r\n    def upload_img(self, img_data, chunk=0, chunkIndex=0, chunks=1, size=None, full_size=None, img_ali_name=None, t='image/jpeg'):\r\n        # 上传视频需要的参数为chunk chunkIndex chunks size full_size img_ali_name\r\n        # 若传入img_ali_name 则表明上传视频\r\n        img_ali_name = self.get_ali_name(img_data) if img_ali_name is None else img_ali_name\r\n        img_name = img_data[0]\r\n        img_size = img_data[1] if size is None else size\r\n        full_size = img_data[1] if full_size is None else full_size\r\n        img_path = img_data[2]\r\n        url = 'https://crm-file.alibaba-inc.com/crmfile/token/doUpload.json'\r\n        boundry = '------WebKitFormBoundaryJz3PjcmgHvVYpfcc'\r\n        fireFox_headers = {\r\n            # \"Accept\": \"application/json, text/plain, */*\",\r\n            # \"Accept-Encoding\": \"gzip, deflate, br\",\r\n            # \"Accept-Language\": \"zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2\",\r\n            # \"Host\": \"crm-file.alibaba-inc.com\",\r\n            # \"Origin\": \"https://onetouch.alibaba.com\",\r\n            # \"Content-Type\":\"multipart/form-data; boundary={}\".format(boundry),\r\n            # \"Pragma\": \"no-cache\",\r\n            # \"Referer\": \"https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}\".format(self.order_id),\r\n            # \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.169 Safari/537.36\",\r\n        }\r\n        fr = open(img_path, 'rb').read()\r\n        data = {\r\n            'id': 5400369145282851,\r\n            'chunk': chunk,\r\n            'chunkIndex': chunkIndex,\r\n            'chunks': chunks,\r\n            'loadsize': str(img_size),\r\n            'big': True,\r\n            'size': str(full_size),\r\n            'type': t,\r\n            'ownerApp': 'crmfile',\r\n            'sceneCode': 'default_chunk',\r\n            'mainCrmFileId': img_ali_name,\r\n        }\r\n        print(data)\r\n        fields = {\r\n            'file': (img_name, fr, 'application/octet-stream')\r\n        }\r\n        response = requests.post(url, headers=fireFox_headers, files=fields, data=data, verify=False)\r\n        # 若上传视频，直接返回img_ali_name\r\n        if t != 'image/jpeg':\r\n            return img_ali_name\r\n        status = json.loads(response.text)['hasError']\r\n        # status 为 false 表明上传成功\r\n        if status is False:\r\n            # self.check_file(img_ali_name)\r\n            return img_ali_name\r\n        else:\r\n            print(response.text)\r\n\r\n    # 添加正面图片\r\n    def add_front_img(self, img_datas):\r\n        subtemp_2_group_1_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfSampleProductsFront01'\r\n            else:\r\n                name = 'picturesOfSampleProductsFrontOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_group_1_1_mediaset.append(front_dict)\r\n        if len(img_datas) < 4:\r\n            front_dict = {\r\n                'picturesOfSampleProductsBackOption04': {\r\n                    \"value\": \"\",\r\n                    \"title\": \"样品确认图（正面）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_group_1_1_mediaset.append(front_dict)\r\n        return subtemp_2_group_1_1_mediaset\r\n\r\n    # 添加背面图片\r\n    def add_back_img(self, img_datas):\r\n        subtemp_2_2_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfSampleProductsBack01'\r\n            else:\r\n                name = 'picturesOfSampleProductsBackOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_2_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) < 4:\r\n            front_dict = {\r\n                'picturesOfSampleProductsBackOption04': {\r\n                    \"value\": \"\",\r\n                    \"title\": \"样品确认图（背面）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_2_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_2_group_1_mediaset\r\n\r\n    # 添加侧面图片\r\n    def add_side_img(self, img_datas):\r\n        subtemp_2_3_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfSampleProductsSide01'\r\n            else:\r\n                name = 'picturesOfSampleProductsSideOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_3_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) < 4:\r\n            front_dict = {\r\n                'picturesOfSampleProductsSideOption04': {\r\n                    \"value\": \"\",\r\n                    \"title\": \"样品确认图（侧面）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_3_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_3_group_1_mediaset\r\n\r\n    # 添加视频\r\n    def get_video(self, img_datas):\r\n        subtemp_2_4_group_1_mediaset = []\r\n        # 若视频大于5M 需分开上传\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_size = img_data[1]\r\n            chunks = int(img_size) // 5120000 if int(img_size) % 5120000 == 0 else int(img_size) // 5120000 + 1\r\n            img_ali_name = self.get_ali_name(img_data, t='mp4')\r\n            for chunk in range(chunks):\r\n                size = 5120000\r\n                if chunk == chunks - 1:\r\n                    size = img_size - chunk * 5120000\r\n                img_ali_name = self.upload_img(img_data, chunk=chunk, chunkIndex=chunk, chunks=chunks, size=size, full_size =img_size, img_ali_name=img_ali_name, t='video/mp4')\r\n            if index == 0:\r\n                name = 'videoOfSampleProducts01'\r\n            else:\r\n                name = 'videoOfSampleProductsOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_4_group_1_mediaset.append(front_dict)\r\n        for i in range(2,4):\r\n            front_dict = {\r\n                'videoOfSampleProductsOption0{}'.format(i): {\r\n                    \"value\": \"\",\r\n                    \"title\": \"样品确认图（侧面）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_4_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_4_group_1_mediaset\r\n\r\n    # 添加资源包分享\r\n    def add_resource_pack(self):\r\n        resource_path = r'\\\\192.168.1.98\\公共共享盘\\1_产品文档\\营销资料\\下载说明'\r\n        img_files = os.listdir(resource_path)\r\n        img_list = []\r\n        for img_file in img_files:\r\n            img_path = resource_path + '/' + img_file\r\n            img_size = os.path.getsize(img_path)\r\n            img_list.append((img_file, img_size, img_path))\r\n        subtemp_2_group_1_2_mediaset = []\r\n        for index, img_data in enumerate(img_list):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'exclusiveProductPicturesOrVideos01'\r\n            else:\r\n                name = 'exclusiveProductPicturesOrVideosOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_group_1_2_mediaset.append(front_dict)\r\n        if len(img_list) < 3:\r\n            front_dict = {\r\n                'picturesOfSampleProductsSideOption03': {\r\n                    \"value\": \"\",\r\n                    \"title\": \"资源包分享图\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_group_1_2_mediaset.append(front_dict)\r\n        return subtemp_2_group_1_2_mediaset\r\n\r\n    # 添加产品细节图\r\n    def add_detail_img(self, img_datas):\r\n        subtemp_2_group_1_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfProductsDetails01'\r\n            else:\r\n                name = 'picturesOfProductsDetailsOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_group_1_1_mediaset.append(front_dict)\r\n        if len(img_datas) < 4:\r\n            front_dict = {\r\n                'picturesOfProductsDetailsOption04': {\r\n                    \"value\": \"\",\r\n                    \"title\": \"产成品细节图（近景）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_group_1_1_mediaset.append(front_dict)\r\n        return subtemp_2_group_1_1_mediaset\r\n\r\n    # 添加出产检测报告\r\n    def add_product_report(self):\r\n        reports_path = r'\\\\192.168.1.98\\公共共享盘\\@ 公司内务文档\\资质手续\\质检认证\\证书文档\\{}\\CE'.format(self.sku)\r\n        report_files = os.listdir(reports_path)\r\n        report_name = [report_file for report_file in report_files if 'jpg' in report_file][0]\r\n        report_path = reports_path + '/' + report_name\r\n        report_size = os.path.getsize(report_path)\r\n        report_datas = [(report_name, report_size, report_path)]\r\n        subtemp_2_4_group_1_mediaset = []\r\n        for index, img_data in enumerate(report_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n\r\n            front_dict = {\r\n                'picturesOfProductsTestReport01': {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_4_group_1_mediaset.append(front_dict)\r\n        for i in range(2, 5):\r\n            name = 'picturesOfProductsTestReportOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"出厂检测报告\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_4_group_1_mediaset.append(front_dict)\r\n\r\n        return subtemp_2_4_group_1_mediaset\r\n\r\n    # 单位贴标图(近景)\r\n    def add_close_shot(self):\r\n        close_host_path = r'\\\\192.168.1.98\\公共共享盘\\@ 公司内务文档\\资质手续\\质检认证\\证书文档\\{}\\EAN'.format(self.sku)\r\n        close_host_files = os.listdir(close_host_path)\r\n        img_datas = []\r\n        for close_host_file in close_host_files:\r\n            if 'jpg' in close_host_file:\r\n                img_name = close_host_file\r\n                img_path = close_host_path + '/' + img_name\r\n                img_size = os.path.getsize(img_path)\r\n                img_datas.append((img_name, img_size, img_path))\r\n        picturesOfLabeledProductsCloseShot = []\r\n        for index, img_data in enumerate(img_datas):\r\n            if index == 0:\r\n                name = 'picturesOfLabeledProductsCloseShot01'\r\n            else:\r\n                name =  'picturesOfLabeledProductsCloseShotOption0{}'.format(index + 1)\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            picturesOfLabeledProductsCloseShot.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return picturesOfLabeledProductsCloseShot\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'picturesOfLabeledProductsCloseShotOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"最小销售单位贴标图(近景)\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            picturesOfLabeledProductsCloseShot.append(front_dict)\r\n        return picturesOfLabeledProductsCloseShot\r\n\r\n    # 销售单位贴标图(远景)\r\n    def add_vision(self, img_datas):\r\n        subtemp_2_2_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            if index == 0:\r\n                name = 'picturesOfAllLabeledProductsLongShot01'\r\n            else:\r\n                name = 'picturesOfAllLabeledProductsLongShotOption0{}'.format(index + 1)\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_2_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return subtemp_2_2_group_1_mediaset\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'picturesOfAllLabeledProductsLongShotOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"最小销售单位贴标图(远景)\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_2_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_2_group_1_mediaset\r\n\r\n    # 包装后成品图（近景）\r\n    def add_packaged_product_img(self, img_datas):\r\n        subtemp_2_group_1_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfPackingFinishedProduct01'\r\n            else:\r\n                name = 'picturesOfPackingFinishedProductOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_group_1_1_mediaset.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return subtemp_2_group_1_1_mediaset\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'picturesOfAllLabeledProductsLongShotOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"包装后成品图（近景）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_group_1_1_mediaset.append(front_dict)\r\n        return subtemp_2_group_1_1_mediaset\r\n\r\n    # 成品堆放图（远景）\r\n    def add_product_stacking_img(self, img_datas):\r\n        subtemp_2_2_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfFinishedProducts01'\r\n            else:\r\n                name = 'picturesOfFinishedProductsOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_2_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return subtemp_2_2_group_1_mediaset\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'picturesOfFinishedProductsOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"批量成品堆放图（远景）\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_2_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_2_group_1_mediaset\r\n\r\n    # 装箱未封箱图\r\n    def add_unsealed_img(self, img_datas):\r\n        subtemp_2_3_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfPackedProducts01'\r\n            else:\r\n                name = 'picturesOfPackedProductsOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_3_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return subtemp_2_3_group_1_mediaset\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'picturesOfPackedProductsOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"产品装箱未封箱图\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_3_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_3_group_1_mediaset\r\n\r\n    # 产品装箱封箱图\r\n    def add_sealed_img(self, img_datas):\r\n        subtemp_2_4_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'picturesOfLabeledBoxesOrPallets01'\r\n            else:\r\n                name = 'picturesOfLabeledBoxesOrPalletsOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_4_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return subtemp_2_4_group_1_mediaset\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'picturesOfLabeledBoxesOrPalletsOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"产品装箱封箱图\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_4_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_4_group_1_mediaset\r\n\r\n    # 产品外箱图贴仓库标签或托盘图\r\n    def add_warehouse_label_img(self, img_datas):\r\n        subtemp_2_5_group_1_mediaset = []\r\n        for index, img_data in enumerate(img_datas):\r\n            img_name = img_data[0]\r\n            img_ali_name = self.upload_img(img_data)\r\n            if index == 0:\r\n                name = 'pictureOfProdcutsInPalletWithShippingLabels01'\r\n            else:\r\n                name = 'pictureOfProdcutsInPalletWithShippingLabelsOption0{}'.format(index + 1)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": img_ali_name,\r\n                    \"title\": img_name,\r\n                    \"from\": \"all\",\r\n                    \"url\": img_ali_name\r\n                }\r\n            }\r\n            subtemp_2_5_group_1_mediaset.append(front_dict)\r\n        if len(img_datas) > 3:\r\n            return subtemp_2_5_group_1_mediaset\r\n        for i in range(len(img_datas) + 1, 5):\r\n            name = 'pictureOfProdcutsInPalletWithShippingLabelsOption0{}'.format(i)\r\n            front_dict = {\r\n                name: {\r\n                    \"value\": \"\",\r\n                    \"title\": \"产品外箱图贴仓库标签或托盘图\",\r\n                    \"from\": \"all\",\r\n                    \"url\": \"\"\r\n                }\r\n            }\r\n            subtemp_2_5_group_1_mediaset.append(front_dict)\r\n        return subtemp_2_5_group_1_mediaset\r\n\r\n    # 获取图片阿里名称\r\n    def get_ali_name(self, img_data, t=None):\r\n        url = 'https://crm-file.alibaba-inc.com/crmfile/token/createWygMainFile.json'\r\n        img_name = img_data[0]\r\n        img_size = img_data[1]\r\n        if t is None:\r\n            data = {\r\n                'addFileParam': '{\"fileType\":\"online\",\"fileName\":\"%s\",\"fileSize\":%d,\"categoryId\":1501,\"templateId\":\"1701\",\"fileExts\":null}' % (\r\n                    img_name, img_size),\r\n                'context': '{\"tenantId\":\"301\",\"operator\":\"223324529\",\"originSystem\":\"onetouch-proofcenter\"}'\r\n            }\r\n        else:\r\n            data = {\r\n                'addFileParam': json.dumps({\"fileType\":\"online\",\"fileName\":img_name,\"fileSize\":img_size,\"categoryId\":1501,\"templateId\":\"1701\",\"fileExts\":None,\"contentType\":\"video/mp4\"}),\r\n                'context': json.dumps({\"tenantId\":\"301\",\"operator\":\"223324529\",\"originSystem\":\"onetouch-proofcenter\"})\r\n            }\r\n        print(data)\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['accept'] = 'application/json, text/plain, */*'\r\n        headers['origin'] = 'https://onetouch.alibaba.com'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(\r\n            self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        img_name = json.loads(response.text)['content']['result']\r\n        return img_name\r\n\r\n    # 获取标签id\r\n    def get_column_code(self):\r\n        url = 'https://onetouch.alibaba.com/moSurvey/seller/detail.json'\r\n        params = {\r\n            'tradeOrderId': self.order_id,\r\n            '_tb_token_': self.tb_token,\r\n            'ctoken':self.ctoken\r\n        }\r\n        headers = self.headers\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(self.order_id)\r\n        headers['authority'] = 'onetouch.alibaba.com'\r\n        headers.pop('origin')\r\n        headers.pop('content-type')\r\n        response = requests.get(url, headers=self.headers, params=params, verify=False)\r\n        response_datas = json.loads(response.text)['data']['taScheduleTaskItemDTOList']\r\n        code_status_dict = {}\r\n        for index, data in enumerate(response_datas):\r\n            taskId = data['authTemplateDTO']['taskId']\r\n            version = data['authTemplateDTO']['splitTaskTemplateData']['version']\r\n            taskSnapshotId = data['authTemplateDTO']['splitTaskTemplateData']['taskSnapshotId']\r\n            if index == 0:\r\n                code_status_dict['save_sample_photo'] = {\r\n                    'taskId': taskId,\r\n                    'taskSnapshotId': taskSnapshotId,\r\n                    'version': version\r\n                }\r\n            elif index == 1:\r\n                code_status_dict['save_resource_pack'] = {\r\n                    'taskId': taskId,\r\n                    'taskSnapshotId': taskSnapshotId,\r\n                    'version': version\r\n                }\r\n            elif index == 2:\r\n                code_status_dict['save_detail_img'] = {\r\n                    'taskId': taskId,\r\n                    'taskSnapshotId': taskSnapshotId,\r\n                    'version': version\r\n                }\r\n            elif index == 3:\r\n                code_status_dict['internal_standard'] = {\r\n                    'taskId': taskId,\r\n                    'taskSnapshotId': taskSnapshotId,\r\n                    'version': version\r\n                }\r\n            elif index == 4:\r\n                code_status_dict['external_standard'] = {\r\n                    'taskId': taskId,\r\n                    'taskSnapshotId': taskSnapshotId,\r\n                    'version': version\r\n                }\r\n            elif index == 5:\r\n                code_status_dict['get_information'] = {\r\n                    'taskId': taskId,\r\n                    'taskSnapshotId': taskSnapshotId,\r\n                    'version': version\r\n                }\r\n        return code_status_dict\r\n\r\n    # 获取备注\r\n    def get_remarks(self, t):\r\n        url = 'http://py1.jakcom.it:5000/get/info/sku_detail/{}'.format(self.sku)\r\n        response = requests.get(url)\r\n        data = eval(response.text)\r\n        if t != 'Barcode':\r\n            remakr_dict = {i.split(':')[0]:i.split(':')[1].strip() for i in data['Description'].split('\\n')[1:]}\r\n            return remakr_dict[t]\r\n        else:\r\n            return data[t]\r\n\r\n    # 样品照片上传\r\n    def save_sample_photo(self, url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset, subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset):\r\n        subtemp_2_5_group_1_textarea = self.get_remarks('Inner White Box')\r\n        taskId = self.code_status_dict['save_sample_photo']['taskId']\r\n        version = self.code_status_dict['save_sample_photo']['version']\r\n        taskSnapshotId = self.code_status_dict['save_sample_photo']['taskSnapshotId']\r\n        # 保存草稿\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/saveSplitTaskDraft.json?_tb_token_={}&ctoken={}'.format(\r\n        #     self.tb_token, self.ctoken)\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/commitToBuyerTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        templateStr = {\r\n            \"subtemp_2_group_1_1_mediaset\":subtemp_2_group_1_1_mediaset,\r\n            \"subtemp_2_2_group_1_mediaset\":subtemp_2_2_group_1_mediaset,\r\n            \"subtemp_2_3_group_1_mediaset\":subtemp_2_3_group_1_mediaset,\r\n            \"subtemp_2_4_group_1_mediaset\":subtemp_2_4_group_1_mediaset,\r\n            \"subtemp_2_5_group_1_textarea\":subtemp_2_5_group_1_textarea\r\n        }\r\n        data = {\r\n            'json': json.dumps({\"authTaskParamUpdateSimpleDTO\":{\"taskId\":taskId,\"version\":version},\"templateStr\":json.dumps(templateStr),\"taskSnapshotId\":taskSnapshotId})\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        print(response)\r\n        print(response.text)\r\n\r\n    # 电商资源包分享\r\n    def save_resource_pack(self, url, subtemp_2_group_1_2_mediaset):\r\n        subtemp_2_2_group_1_textarea = 'Please follow this below link to view and download all the marketing material of your goods;\\nhttp://file.jakcom.com'\r\n        taskId = self.code_status_dict['save_resource_pack']['taskId']\r\n        version = self.code_status_dict['save_resource_pack']['version']\r\n        taskSnapshotId = self.code_status_dict['save_resource_pack']['taskSnapshotId']\r\n        # 保存草稿\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/saveSplitTaskDraft.json?_tb_token_={}&ctoken={}'.format(\r\n        #     self.tb_token, self.ctoken)\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/commitToBuyerTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        templateStr = {\"subtemp_2_group_1_2_mediaset\":subtemp_2_group_1_2_mediaset,\"subtemp_2_2_group_1_textarea\":subtemp_2_2_group_1_textarea}\r\n        data = {\r\n            'json': json.dumps({\"authTaskParamUpdateSimpleDTO\":{\"taskId\":taskId,\"version\":version},\"templateStr\":json.dumps(templateStr),\"taskSnapshotId\":taskSnapshotId})\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        print(response)\r\n        print(response.text)\r\n\r\n    # 检测完成\r\n    def save_detail_img(self, url, subtemp_2_group_1_1_mediaset, subtemp_2_4_group_1_mediaset):\r\n        subtemp_2_2_group_1_textarea = 'Warranty is 1 year.\\nhere are any problems within one year, we will fix or change it for you, and we are responsible for the freight when we send to you.'\r\n        taskId = self.code_status_dict['save_detail_img']['taskId']\r\n        version = self.code_status_dict['save_detail_img']['version']\r\n        taskSnapshotId = self.code_status_dict['save_detail_img']['taskSnapshotId']\r\n        # 保存草稿\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/saveSplitTaskDraft.json?_tb_token_={}&ctoken={}'.format(\r\n        #     self.tb_token, self.ctoken)\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/commitToBuyerTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        templateStr = {\r\n            \"subtemp_2_group_1_1_mediaset\":subtemp_2_group_1_1_mediaset,\r\n            \"subtemp_2_2_group_1_mediaset\": [{\"videoOWholeInspectionProgress01\": {\"value\": \"\", \"title\": \"全检视频\", \"url\": \"\"}},\r\n                                  {\"videoOWholeInspectionProgressOption02\": {\"value\": \"\", \"title\": \"全检视频\", \"url\": \"\"}},\r\n                                  {\"videoOWholeInspectionProgressOption03\": {\"value\": \"\", \"title\": \"全检视频\", \"url\": \"\"}}],\r\n            \"subtemp_2_3_group_1_mediaset\": [{\"videoOfInspection01\": {\"value\": \"\", \"title\": \"检测过程视频\", \"url\": \"\"}},\r\n                                  {\"videoOfInspectionOption02\": {\"value\": \"\", \"title\": \"检测过程视频\", \"url\": \"\"}},\r\n                                  {\"videoOfInspectionOption03\": {\"value\": \"\", \"title\": \"检测过程视频\", \"url\": \"\"}}],\r\n            \"subtemp_2_4_group_1_mediaset\": subtemp_2_4_group_1_mediaset,\r\n            \"subtemp_2_5_group_1_textarea\":subtemp_2_2_group_1_textarea}\r\n        data = {\r\n            'json': json.dumps({\"authTaskParamUpdateSimpleDTO\":{\"taskId\":taskId,\"version\":version},\"templateStr\":json.dumps(templateStr),\"taskSnapshotId\":taskSnapshotId})\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        print(response)\r\n        print(response.text)\r\n\r\n    # 帖内标\r\n    def internal_standard(self, url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset):\r\n        subtemp_2_4_group_1_textarea = self.get_remarks('Barcode')\r\n        taskId = self.code_status_dict['internal_standard']['taskId']\r\n        version = self.code_status_dict['internal_standard']['version']\r\n        taskSnapshotId = self.code_status_dict['internal_standard']['taskSnapshotId']\r\n        # 保存草稿\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/saveSplitTaskDraft.json?_tb_token_={}&ctoken={}'.format(\r\n        #     self.tb_token, self.ctoken)\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/commitToBuyerTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        templateStr = {\r\n             \"subtemp_2_group_1_1_mediaset\": subtemp_2_group_1_1_mediaset,\r\n             \"subtemp_2_2_group_1_mediaset\": subtemp_2_2_group_1_mediaset,\r\n             \"subtemp_2_3_group_1_mediaset\": [{\"VideoOfAllLabeledProducts01\": {\"value\": \"\", \"title\": \"产品贴标视频\", \"url\": \"\"}},\r\n                                              {\"VideoOfAllLabeledProductsOption02\": {\"value\": \"\", \"title\": \"产品贴标视频\", \"url\": \"\"}},\r\n                                              {\"VideoOfAllLabeledProductsOption03\": {\"value\": \"\", \"title\": \"产品贴标视频\", \"url\": \"\"}}],\r\n             \"subtemp_2_4_group_1_textarea\": subtemp_2_4_group_1_textarea}\r\n\r\n        data = {\r\n            'json': json.dumps({\"authTaskParamUpdateSimpleDTO\": {\"taskId\": taskId, \"version\": version},\r\n                                \"templateStr\": json.dumps(templateStr), \"taskSnapshotId\": taskSnapshotId})\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(\r\n            self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        print(response)\r\n        print(response.text)\r\n\r\n    # 打包贴外标\r\n    def external_standard(self, url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset, subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset, subtemp_2_5_group_1_mediaset):\r\n        subtemp_2_7_group_1_textarea = self.get_remarks('Outer Brown Box')\r\n        taskId = self.code_status_dict['external_standard']['taskId']\r\n        version = self.code_status_dict['external_standard']['version']\r\n        taskSnapshotId = self.code_status_dict['external_standard']['taskSnapshotId']\r\n        # 保存草稿\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/saveSplitTaskDraft.json?_tb_token_={}&ctoken={}'.format(\r\n        #     self.tb_token, self.ctoken)\r\n        # url = 'https://onetouch.alibaba.com/moSurvey/seller/commitToBuyerTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        templateStr = {\r\n             \"subtemp_2_group_1_1_mediaset\": subtemp_2_group_1_1_mediaset,\r\n             \"subtemp_2_2_group_1_mediaset\": subtemp_2_2_group_1_mediaset,\r\n             \"subtemp_2_3_group_1_mediaset\": subtemp_2_3_group_1_mediaset,\r\n             \"subtemp_2_4_group_1_mediaset\": subtemp_2_4_group_1_mediaset,\r\n             \"subtemp_2_5_group_1_mediaset\": subtemp_2_5_group_1_mediaset,\r\n             \"subtemp_2_6_group_1_mediaset\": [{\"videoOfPackedAndLabeled01\": {\"value\": \"\", \"title\": \"包装贴标视频\", \"url\": \"\"}},\r\n                                              {\"videoOfPackedAndLabeledOption02\": {\"value\": \"\", \"title\": \"包装贴标视频\", \"url\": \"\"}},\r\n                                              {\"videoOfPackedAndLabeledOption03\": {\"value\": \"\", \"title\": \"包装贴标视频\", \"url\": \"\"}}],\r\n             \"subtemp_2_7_group_1_textarea\": subtemp_2_7_group_1_textarea\r\n        }\r\n\r\n\r\n        data = {\r\n            'json': json.dumps({\"authTaskParamUpdateSimpleDTO\": {\"taskId\": taskId, \"version\": version},\r\n                                \"templateStr\": json.dumps(templateStr), \"taskSnapshotId\": taskSnapshotId})\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(\r\n            self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        print(response)\r\n        print(response.text)\r\n\r\n    # 发货信息采集\r\n    def get_information(self, url):\r\n        subtemp_2_4_group_1_textarea = self.express_info\r\n        taskId = self.code_status_dict['get_information']['taskId']\r\n        version = self.code_status_dict['get_information']['version']\r\n        taskSnapshotId = self.code_status_dict['get_information']['taskSnapshotId']\r\n        templateStr = {\r\n            \"subtemp_2_3_group_1_mediaset\": [{\"videoLogisticFowarderTookAllTheProducts01\": {\"title\": \"包装贴标视频\"}},\r\n                                  {\"videoLogisticFowarderTookAllTheProductsOption02\": {\"title\": \"物流发货视频\"}},\r\n                                  {\"videoLogisticFowarderTookAllTheProductsOption03\": {\"title\": \"物流发货视频\"}}],\r\n            \"subtemp_2_4_group_1_textarea\": subtemp_2_4_group_1_textarea}\r\n        data = {\r\n            'json': json.dumps({\"authTaskParamUpdateSimpleDTO\": {\"taskId\": taskId, \"version\": version},\r\n                                \"templateStr\": json.dumps(templateStr), \"taskSnapshotId\": taskSnapshotId})\r\n        }\r\n        headers = self.headers\r\n        headers['content-type'] = 'application/x-www-form-urlencoded; charset=UTF-8'\r\n        headers['referer'] = 'https://onetouch.alibaba.com/moSurvey/seller/detail.htm?tradeOrderId={}'.format(\r\n            self.order_id)\r\n        response = requests.post(url, headers=headers, data=data, verify=False)\r\n        print(response)\r\n        print(response.text)\r\n\r\n    # 数据库更新\r\n    def update_database(self):\r\n        url = 'https://onetouch.alibaba.com/moSurvey/schedule/list2.json?'\r\n        params = {\r\n            '_t': str(time.time()).replace('.', '')[:13],\r\n            'json': json.dumps({\"taOrderNo\":str(self.order_id),\"currentPage\":1,\"pageSize\":10,\"sort\":{},\"orderBy\":\"RANK\",\"secondRankName\":\"tracking_service_warning_order\",\"descSort\":True})\r\n        }\r\n        url += urlencode(params)\r\n        response = requests.get(url, headers=self.headers, verify=False)\r\n        responseDatas = json.loads(response.text)['data']\r\n        postUrl = 'http://py1.jakcom.it:5000/alibaba/post/order/update_process_info'\r\n        datas = responseDatas['dataList']\r\n        templateItemNameKey_dict = {\"placeholder.expectFinishedDate\": \"请选择计划完成日期\", \"placeholder.remark\": \"请输入备注\",\r\n                                    \"orderSchedule.task.templateName.taScheduleCloth.packageCompletion\": \"包装完成\",\r\n                                    \"orderSchedule.task.templateName.taScheduleCloth.detectionCompletion\": \"检测完成\",\r\n                                    \"orderSchedule.task.templateName.taScheduleCloth.materialPreparing\": \"备料入仓\",\r\n                                    \"orderSchedule.task.templateName.taScheduleCloth.productionCompletion\": \"生产完成\",\r\n                                    \"orderSchedule.task.templateName.taScheduleCloth.shipment\": \"出货\",\r\n                                    \"orderSchedule.task.templateName.taScheduleCloth.productionStart\": \"生产开始\",\r\n                                    \"orderSchedule.task.templateName.amzs.yangpin\": \"样品照片上传\",\r\n                                    \"orderSchedule.task.templateName.amzs.dabaotiewaibia\": \"打包贴外标\",\r\n                                    \"orderSchedule.task.templateName.amzs.dianshangziyuanbao\": \"电商资源包分享\",\r\n                                    \"orderSchedule.task.templateName.amzs.tieneibia\": \"帖内标\",\r\n                                    \"orderSchedule.task.templateName.amzs.jiancha\": \"检测完成\",\r\n                                    \"orderSchedule.task.templateName.amzs.fahuo\": \"发货信息采集\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.detectionCompletion\": \"检测完成\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.packageCompletion\": \"包装完成\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.productionCompletion\": \"生产完成\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.shipment\": \"��货信息采集\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.shipment.button\": \"去发货\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.materialPreparing\": \"备料入仓\",\r\n                                    \"orderSchedule.task.templateName.taScheduleGeneral.productionStart\": \"开始生产\",\r\n                                    \"orderSchedule.task.templateName.loaded\": \"发货\",\r\n                                    \"orderSchedule.task.templateName.knitting.zhuanghuo\": \"装货\",\r\n                                    \"orderSchedule.task.templateName.knitting.xishui\": \"洗水\",\r\n                                    \"orderSchedule.task.templateName.knitting.beiliao\": \"备料\",\r\n                                    \"orderSchedule.task.templateName.knitting.fenghe\": \"套口缝合/手缝\",\r\n                                    \"orderSchedule.task.templateName.knitting.baozhuang\": \"包装\",\r\n                                    \"orderSchedule.task.templateName.knitting.bianzhi\": \"横机编织\",\r\n                                    \"orderSchedule.task.templateName.knitting.houdao\": \"后道\",\r\n                                    \"orderSchedule.task.templateName.3c.zhusu\": \"注塑\",\r\n                                    \"orderSchedule.task.templateName.3c.baozhuang\": \"包装\",\r\n                                    \"orderSchedule.task.templateName.3c.zhuanghuo\": \"装货\",\r\n                                    \"orderSchedule.task.templateName.3c.tiepian\": \"贴片\",\r\n                                    \"orderSchedule.task.templateName.3c.beiliao\": \"备料\",\r\n                                    \"orderSchedule.task.templateName.3c.test\": \"老化测试\",\r\n                                    \"orderSchedule.task.templateName.3c.zuzhuang\": \"组装\",\r\n                                    \"orderSchedule.task.templateName.start30\": \"成品制定完成\",\r\n                                    \"orderSchedule.task.templateName.start10\": \"布匹裁剪完成\",\r\n                                    \"orderSchedule.task.templateName.clothing.zhuanghuo\": \"装货\",\r\n                                    \"orderSchedule.task.templateName.clothing.houdao\": \"后道\",\r\n                                    \"orderSchedule.task.templateName.clothing.baozhuang\": \"包装\",\r\n                                    \"orderSchedule.task.templateName.clothing.beiliao\": \"备料\",\r\n                                    \"orderSchedule.task.templateName.clothing.caifeng\": \"剪裁\",\r\n                                    \"orderSchedule.task.templateName.clothing.fengren\": \"缝纫\",\r\n                                    \"orderSchedule.task.templateName.start50\": \"烫染完成\",\r\n                                    \"orderSchedule.task.templateName.start\": \"开始\",\r\n                                    \"orderSchedule.task.templateName.default.packing\": \"包装\",\r\n                                    \"orderSchedule.task.templateName.default.progressA\": \"生产A\",\r\n                                    \"orderSchedule.task.templateName.default.progressB\": \"生产B\",\r\n                                    \"orderSchedule.task.templateName.default.start\": \"备料\",\r\n                                    \"orderSchedule.task.templateName.default.loading\": \"装货\",\r\n                                    \"orderSchedule.task.templateName.finish\": \"打包装箱完成\",\r\n                                    \"orderSchedule.task.buyer.view\": \"此环节买家查看次数\",\r\n                                    \"orderSchedule.taOrderList.tracking_service_finished\": \"已完成\",\r\n                                    \"orderSchedule.taOrderList.paymentStatus.ADVANCE\": \"预付款\",\r\n                                    \"orderSchedule.taOrderList.paymentStatus.FULL\": \"全款\",\r\n                                    \"orderSchedule.taOrderList.paymentStatus.BALANCE\": \"未付款\",\r\n                                    \"orderSchedule.taOrderList.tracking_service_wait_for_checking\": \"进行中\",\r\n                                    \"orderSchedule.firstLevel.desc.Agriculture\": \"农业\",\r\n                                    \"orderSchedule.firstLevel.desc.Machinery\": \"机械\",\r\n                                    \"orderSchedule.firstLevel.desc.Electrical_Equipment_Supplies\": \"电气设备及用品\",\r\n                                    \"orderSchedule.firstLevel.desc.Lights_Lighting\": \"灯光和照明\",\r\n                                    \"orderSchedule.firstLevel.desc.Office_School_Supplies\": \"办公文教用品\",\r\n                                    \"orderSchedule.firstLevel.desc.Fashion_Accessories\": \"时尚饰品\",\r\n                                    \"orderSchedule.firstLevel.desc.Chemicals\": \"化学物质\",\r\n                                    \"orderSchedule.firstLevel.desc.Apparel\": \"服装\",\r\n                                    \"orderSchedule.firstLevel.desc.Minerals_Metallurgy\": \"矿产和冶金\",\r\n                                    \"orderSchedule.firstLevel.desc.Health_Medical\": \"健康与医疗\",\r\n                                    \"orderSchedule.firstLevel.desc.Business_Services\": \"商业服务\",\r\n                                    \"orderSchedule.firstLevel.desc.Sports_Entertainment\": \"体育和娱乐\",\r\n                                    \"orderSchedule.firstLevel.desc.Fabrication_Services\": \"制造服务\",\r\n                                    \"orderSchedule.firstLevel.desc.Textiles_Leather_Products\": \"纺织及皮革制品\",\r\n                                    \"orderSchedule.firstLevel.desc.Food_Beverage\": \"食品和饮料\",\r\n                                    \"orderSchedule.firstLevel.desc.Rubber_Plastics\": \"橡塑原料及制品\",\r\n                                    \"orderSchedule.firstLevel.desc.Beauty_Personal_Care\": \"美容及个人护理\",\r\n                                    \"orderSchedule.firstLevel.desc.Service_Equipment\": \"维修设备\",\r\n                                    \"orderSchedule.firstLevel.desc.Furniture\": \"家具\",\r\n                                    \"orderSchedule.firstLevel.desc.Gifts_Crafts\": \"礼品和工艺品\",\r\n                                    \"orderSchedule.firstLevel.desc.Timepieces_Jewelry_Eyewear\": \"钟表、珠宝、眼镜\",\r\n                                    \"orderSchedule.firstLevel.desc.Construction_Real_Estate\": \"建筑与房地产\",\r\n                                    \"orderSchedule.firstLevel.desc.Electronic_Components_Supplies\": \"电子元件及用品\",\r\n                                    \"orderSchedule.firstLevel.desc.Home_Appliances\": \"家用电器\",\r\n                                    \"orderSchedule.firstLevel.desc.Luggage_Bags_Cases\": \"行李，袋子和箱子\",\r\n                                    \"orderSchedule.firstLevel.desc.Packaging_Printing\": \"包装与印刷\",\r\n                                    \"orderSchedule.firstLevel.desc.Toys_Hobbies\": \"玩具\",\r\n                                    \"orderSchedule.firstLevel.desc.Environment\": \"环境\",\r\n                                    \"orderSchedule.firstLevel.desc.Vehicles_Accessories\": \"车辆及配件\",\r\n                                    \"orderSchedule.firstLevel.desc.Home_Garden\": \"家居与园艺\",\r\n                                    \"orderSchedule.firstLevel.desc.Telecommunications\": \"电信\",\r\n                                    \"orderSchedule.firstLevel.desc.Energy\": \"能源\",\r\n                                    \"orderSchedule.firstLevel.desc.Security_Protection\": \"安全防护\",\r\n                                    \"orderSchedule.firstLevel.desc.Shoes_Accessories\": \"鞋子和配件\",\r\n                                    \"orderSchedule.firstLevel.desc.Consumer_Electronics\": \"消费电子\",\r\n                                    \"orderSchedule.firstLevel.desc.Tools_Hardware\": \"工具和硬件\",\r\n                                    \"orderSchedule.taskStatus.task_finished\": \"完成\",\r\n                                    \"orderSchedule.taskStatus.wait_for_checking\": \"等待中\",\r\n                                    \"orderSchedule.taskStatus.task_fail\": \"无法办理\",\r\n                                    \"orderSchedule.taskStatus.all\": \"全部\",\r\n                                    \"orderSchedule.produceProgressStatus.shipping_finish\": \"-\",\r\n                                    \"orderSchedule.produceProgressStatus.order_finish\": \"-\",\r\n                                    \"orderSchedule.produceProgressStatus.warning\": \"预警\",\r\n                                    \"orderSchedule.produceProgressStatus.delay\": \"超期\",\r\n                                    \"orderSchedule.produceProgressStatus.normal\": \"正常\",\r\n                                    \"orderSchedule.reviewTaskStatus.wait_for_feedback\": \"待反馈\",\r\n                                    \"orderSchedule.reviewTaskStatus.finish_evaluate\": \"已评价\",\r\n                                    \"orderSchedule.reviewTaskStatus.wait_for_evaluate\": \"未评价\",\r\n                                    \"orderSchedule.reviewTaskStatus.finished\": \"已反馈\",\r\n                                    \"orderSchedule.rejectReason.image_not_true\": \"虚假拍摄\",\r\n                                    \"orderSchedule.rejectReason.other_reason\": \"其他原因\",\r\n                                    \"orderSchedule.rejectReason.location_distance_terrible\": \"地址偏差过大\",\r\n                                    \"orderSchedule.rejectReason.inventory_product\": \"产品是库存产品\",\r\n                                    \"orderSchedule.rejectReason.not_suit_for_order\": \"拍摄内容与订单不符\",\r\n                                    \"orderSchedule.rejectReason.over_produt_time\": \"实际生产进度已超过当前阶段\",\r\n                                    \"orderSchedule.rejectReason.image_or_video_not_uploaded\": \"未上传图片或视频\",\r\n                                    \"orderSchedule.rejectReason.uploaded_image_or_video_unqualified\": \"已上传图片或视频不合格\",\r\n                                    \"orderSchedule.rejectReason.no_enough_photo_employee\": \"未能安排拍摄人员\",\r\n                                    \"orderSchedule.rejectReason.schedule_dely\": \"进度延期\",\r\n                                    \"orderSchedule.rejectReason.not_ship_to_specified_warehouse\": \"未发货至指定海外仓\",\r\n                                    \"orderSchedule.fail.mustLogin\": \"必须先登录才能操作\",\r\n                                    \"orderSchedule.fail.noPermision\": \"无权操作该信保单\",\r\n                                    \"orderSchedule.fail.paramError\": \"参数错误\", \"orderSchedule.fail.sysException\": \"系统异常\",\r\n                                    \"orderSchedule.feedbackStatus.finished\": \"已反馈\",\r\n                                    \"orderSchedule.feedbackStatus.wait_for_feedback\": \"待反馈\",\r\n                                    \"orderSchedule.detail.product.name\": \"产品名称\",\r\n                                    \"orderSchedule.detail.product.quantity\": \"数量\",\r\n                                    \"orderSchedule.detail.product.description\": \"描述\",\r\n                                    \"ta.schedule.task.template.resolver.eCommerceService\": \"电商一站通\",\r\n                                    \"ta.schedule.task.template.resolver.eCommerceService.desc\": \"从定制、检测、打包贴标到发货一站式服务\",\r\n                                    \"ta.schedule.task.template.resolver.taScheduleGeneral\": \"生产可视化\",\r\n                                    \"ta.schedule.task.template.resolver.taScheduleGeneral.simple\": \"生产可视化\",\r\n                                    \"ta.schedule.task.template.resolver.taScheduleGeneral.desc\": \"生产型订单监控\",\r\n                                    \"ta.schedule.task.template.resolver.taScheduleGeneral.closed\": \"The order has been closed and order tracking service is not available. \\n\",\r\n                                    \"ta.schedule.task.template.resolver.taScheduleGeneral.simpleMode.desc\": \"生产可视化简易模版描述\",\r\n                                    \"ta.schedule.task.template.resolver.taScheduleGeneral.defaultMode.desc\": \"生产可视化模版描述\",\r\n                                    \"ta.schedule.task.template.resolver.1200000212\": \"生产可视化\",\r\n                                    \"ta.schedule.task.template.resolver.default\": \"默认类型\",\r\n                                    \"ta.schedule.task.template.resolver.clothing\": \"服装\",\r\n                                    \"ta.schedule.task.template.resolver.3c\": \"消费电子\",\r\n                                    \"ta.schedule.task.template.resolver.knitting\": \"针织工艺\",\r\n                                    \"ta.schedule.task.template.type\": \"可视化类型\",\r\n                                    \"ta.schedule.page.createOrder.buyer.deliver.content\": \"供应商已发货，服务无法开启，更多了解\",\r\n                                    \"ta.schedule.page.createOrder.buyer.deliver.content.rax\": \"供应商已发货，服务无法开启。\",\r\n                                    \"ta.schedule.page.createOrder.buyer.deliver.title\": \"提示\",\r\n                                    \"ta.schedule.page.createOrder.seller.deliver.content\": \"货物已发出，服务不能被开启。请在发货前开启订单可视化服务。\",\r\n                                    \"ta.schedule.ta.order.relative.prepayment.expect.shipping.time\": \"预付款收齐到账duration天后发货\",\r\n                                    \"ta.schedule.ta.order.relative.prepayment.expect.deliveryTime\": \"计划发货时间\",\r\n                                    \"ta.schedule.ta.order.relative.prepayment.expect.deliveryStatus\": \"发货状态\",\r\n                                    \"ta.schedule.ta.order.relative.prepayment.deliveryTime\": \"实际发货时间\",\r\n                                    \"ta.schedule.ta.order.relative.all.payment.expect.shipping.time\": \"全款到账收齐duration天后发货\",\r\n                                    \"ta.schedule.ta.order.relative.balance.payment.expect.shipping.time\": \"全款收齐后duration天后发货\",\r\n                                    \"seller.show.buyer.view.num\": \"买家访问次数\", \"rax.buyer.submitted\": \"供应商已敦促及时上传最新生产情况。\",\r\n                                    \"rax.buyer.header.title\": \"生产服务跟进\",\r\n                                    \"rax.buyer.header.content.second\": \"如果延期仍未收到状态更新，或有其他问题，请联系供应商。\",\r\n                                    \"rax.buyer.header.content.first\": \"供应商将按约定生产线上传图片。\",\r\n                                    \"rax.buyer.header.confirmed\": \"待卖家确认\",\r\n                                    \"rax.buyer.leave.comment\": \"联系供应商\",\r\n                                    \"rax.buyer.select.tracking.steps\": \"选择生产跟进服务类型（多选）\",\r\n                                    \"rax.buyer.select.one.multiple\": \"请选择一个或多个选项\",\r\n                                    \"rax.buyer.preview.tracking\": \"预览生产跟进步骤\",\r\n                                    \"rax.buyer.service.enabled\": \"服务未开启，联系供应商或者去PC端开启服务。\",\r\n                                    \"rax.buyer.estimated.time\": \"预计完成时间\",\r\n                                    \"rax.buyer.review\": \"评价\",\r\n                                    \"rax.buyer.reminderText\": \"Note that order tracking only begins once initial payment has been made. \",\r\n                                    \"rax.buyer.remind.supplier\": \"提醒供应商\",\r\n                                    \"deliveryCenter.deliveryHome.deliveryStatus.[ENUM].IN_SHIPPING\": \"发货中\",\r\n                                    \"deliveryCenter.deliveryHome.deliveryStatus.[ENUM].WAIT_TO_SHIPPING\": \"等待发货\",\r\n                                    \"deliveryCenter.deliveryHome.deliveryStatus.[ENUM].BUYER_SIGN\": \"买家签收\",\r\n                                    \"deliveryCenter.deliveryHome.deliveryStatus.[ENUM].CLOSE\": \"订单关闭\",\r\n                                    \"deliveryCenter.deliveryHome.deliveryStatus.[ENUM].FINISH\": \"发货完成\",\r\n                                    \"deliveryCenter.deliveryHome.deliveryStatus.ENUM].SELLER_SIGN\": \"卖家签收\"}\r\n        for data in datas:\r\n            taOrderNo = data['taOrderNo']  # 信单保号\r\n            buyerName = data['buyerName']  # 买家名称\r\n            gmtCreate = data['gmtCreate']  # 创建时间\r\n            templateItemNameKey = templateItemNameKey_dict.get(data['templateItemNameKey'], '未获取到样品状态')  # 样品状态\r\n            localTime = data['expectShippingTime']\r\n            expectShippingTime = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime(\r\n                int(str(localTime)[:10]))) if localTime is not None else ''  # 计划发货时间\r\n            status = templateItemNameKey_dict.get('orderSchedule.taOrderList.' + data['status'], '未获取到办理状态')  # 办理状态\r\n            currentOwner = data['currentOwner']  # 当前负责人\r\n            buyerViewOrNot = '是' if data['buyerViewOrNot'] is True else '否'  # 买家是否查看\r\n            paymentStatusKey = templateItemNameKey_dict[data['paymentStatusKey']]  # 付款状态\r\n            produceProgressStatus = templateItemNameKey_dict.get(\r\n                'orderSchedule.produceProgressStatus.' + data['produceProgressStatus'], '未获取到进度提醒')  # 进度提醒\r\n            gmtTaskExpectFinished = data['gmtTaskExpectFinished'] if data[\r\n                                                                         'gmtTaskExpectFinished'] is not None else ''  # 当前节点计划完成时间\r\n            postData = {\r\n                'Account': self.account,\r\n                'TA_Order_ID': taOrderNo,\r\n                'Buyer_Name': buyerName,\r\n                'Create_Time': gmtCreate,\r\n                'Progress_Rate': templateItemNameKey,\r\n                'Sent_Time': expectShippingTime,\r\n                'Process_Status': status,\r\n                'Principal': currentOwner,\r\n                'View_Status': buyerViewOrNot,\r\n                'Remind_Status': produceProgressStatus,\r\n                'Step_Time': gmtTaskExpectFinished,\r\n                'Payment_Status': paymentStatusKey\r\n            }\r\n            print(postData)\r\n            postResponse = requests.post(postUrl, postData)\r\n            print(postResponse)\r\n            print(postResponse.text)\r\n\r\n    # 类的主函数\r\n    def main(self):\r\n        # 保存草稿\r\n        draft_url = 'https://onetouch.alibaba.com/moSurvey/seller/saveSplitTaskDraft.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        # 提交给买家\r\n        buyer_url = 'https://onetouch.alibaba.com/moSurvey/seller/commitToBuyerTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        # 结束此环节\r\n        end_url = 'https://onetouch.alibaba.com/moSurvey/seller/commitAndEndTask.json?_tb_token_={}&ctoken={}'.format(self.tb_token, self.ctoken)\r\n        local_img_datas = self.get_local_img(self.sku)\r\n        # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # 样品照片上传\r\n        subtemp_2_group_1_1_mediaset = self.add_front_img(local_img_datas['picturesOfSampleProductsFront'])\r\n        subtemp_2_2_group_1_mediaset = self.add_back_img(local_img_datas['picturesOfSampleProductsBack'])\r\n        subtemp_2_3_group_1_mediaset = self.add_side_img(local_img_datas['picturesOfSampleProductsSide'])\r\n        subtemp_2_4_group_1_mediaset = self.get_video(local_img_datas['videoOfSampleProducts'])\r\n        # 保存草稿\r\n        self.save_sample_photo(draft_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset, subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset)\r\n        # # 提交给买家\r\n        # self.save_sample_photo(buyer_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset,subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset)\r\n        # # 结束此环节\r\n        # self.save_sample_photo(end_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset,subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset)\r\n        # # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # # 电商资源分享\r\n        # subtemp_2_group_1_2_mediaset = self.add_resource_pack()\r\n        # self.save_resource_pack(draft_url, subtemp_2_group_1_2_mediaset)\r\n        # self.save_resource_pack(buyer_url, subtemp_2_group_1_2_mediaset)\r\n        # self.save_resource_pack(end_url, subtemp_2_group_1_2_mediaset)\r\n        # # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # # 检测完成\r\n        # subtemp_2_group_1_1_mediaset = self.add_detail_img(local_img_datas['picturesOfProductsDetails'])\r\n        # subtemp_2_4_group_1_mediaset = self.add_product_report()\r\n        # self.save_detail_img(draft_url, subtemp_2_group_1_1_mediaset, subtemp_2_4_group_1_mediaset)\r\n        # self.save_detail_img(buyer_url, subtemp_2_group_1_1_mediaset, subtemp_2_4_group_1_mediaset)\r\n        # self.save_detail_img(end_url, subtemp_2_group_1_1_mediaset, subtemp_2_4_group_1_mediaset)\r\n        # # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # # 贴内标\r\n        # subtemp_2_group_1_1_mediaset = self.add_close_shot()\r\n        # subtemp_2_2_group_1_mediaset = self.add_vision(local_img_datas['picturesOfAllLabeledProductsLongShot'])\r\n        # self.internal_standard(draft_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset)\r\n        # self.internal_standard(buyer_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset)\r\n        # self.internal_standard(end_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset)\r\n        # # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # # 贴外标\r\n        # subtemp_2_group_1_1_mediaset = self.add_packaged_product_img(local_img_datas['picturesOfPackingFinishedProduct'])\r\n        # subtemp_2_2_group_1_mediaset = self.add_product_stacking_img(local_img_datas['picturesOfFinishedProducts'])\r\n        # subtemp_2_3_group_1_mediaset = self.add_unsealed_img(local_img_datas['picturesOfPackedProducts'])\r\n        # subtemp_2_4_group_1_mediaset = self.add_sealed_img(local_img_datas['picturesOfLabeledBoxesOrPallets'])\r\n        # subtemp_2_5_group_1_mediaset = self.add_warehouse_label_img(local_img_datas['pictureOfProdcutsInPalletWithShippingLabels'])\r\n        # self.external_standard(draft_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset, subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset, subtemp_2_5_group_1_mediaset)\r\n        # self.external_standard(buyer_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset, subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset, subtemp_2_5_group_1_mediaset)\r\n        # self.external_standard(end_url, subtemp_2_group_1_1_mediaset, subtemp_2_2_group_1_mediaset, subtemp_2_3_group_1_mediaset, subtemp_2_4_group_1_mediaset, subtemp_2_5_group_1_mediaset)\r\n        # # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # # 信息采集\r\n        # self.get_information(draft_url)\r\n        # self.get_information(buyer_url)\r\n        # self.get_information(end_url)\r\n        # # -------------------------------------------------------------------------------------------------------------------------------------------\r\n        # # 更新数据库\r\n        # self.update_database()\r\n\r\n\r\ndef main(account, order_id, sku, express_info):\r\n    productionFollowUpDetails = ProductionFollowUpDetails(account, order_id, sku, express_info)\r\n    productionFollowUpDetails.main()\r\n\r\n\r\nif __name__ == '__main__':\r\n    account = 'tx@jakcom.com'\r\n    order_id = 13904371001022502\r\n    sku = 'BH3'\r\n    express_info = 'unpaid'\r\n    main(account, order_id, sku, express_info)","sub_path":"python_flask/express/alibaba/productionFollowUpDetails.py","file_name":"productionFollowUpDetails.py","file_ext":"py","file_size_in_byte":69686,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"538448723","text":"# coding=utf-8\r\nimport datetime\r\nimport logging\r\n\r\nfrom django.db import transaction\r\n\r\nfrom applications.upload_resumable.err_code import *\r\nfrom applications.upload_resumable.models import *\r\nfrom applications.upload_resumable.utils_data import *\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\n# 针对每个文件的操作创建一个数据库锁\r\n# initiate_multipart_upload part_num=0\r\n# complete_upload part_num=99999999\r\n\r\n@transaction.atomic\r\ndef upload_lock_acquire(name, part_num):\r\n    created_file_part = None\r\n    ret_code = UPLOAD_ACQUIRE_NOT_IDLE\r\n\r\n    # 检查锁占用时间是否超时 , create_time__gt=due_time\r\n    now = datetime.datetime.now()\r\n    file_part = FilePart.objects.filter(name=name, part_num=part_num).first()\r\n    running_due_time = now - datetime.timedelta(seconds=UPLOAD_LOCK_RUNNING_TIMEOUT_SECONDS)\r\n    finish_due_time = now - datetime.timedelta(days=UPLOAD_LOCK_FINISH_TIMEOUT_DAYS)\r\n    if not file_part:\r\n        pass\r\n    elif file_part.status == UPLOAD_STATUS_RUNNING:\r\n        if file_part.update_time > running_due_time:\r\n            logger.info(\"upload_lock: file_name=%s, part_num=%d have running locked \" % (name, part_num))\r\n            return False, UPLOAD_ACQUIRE_RUNNING\r\n        else:\r\n            FilePart.objects.filter(name=name, part_num=part_num).delete()\r\n            logger.info(\"upload_lock: file_name=%s, part_num=%d is running timeout \" % (name, part_num))\r\n            ret_code = UPLOAD_ACQUIRE_RUNNING_TIME_OUT\r\n    elif file_part.status == UPLOAD_STATUS_FINISHED:\r\n        if file_part.update_time > finish_due_time:\r\n            logger.info(\"upload_lock: file_name=%s, part_num=%d have finish locked \" % (name, part_num))\r\n            return False, UPLOAD_ACQUIRE_FINISH\r\n        else:\r\n            FilePart.objects.filter(name=name).delete()\r\n            logger.info(\"upload_lock: file_name=%s, part_num=%d is finish timeout \" % (name, part_num))\r\n            ret_code = UPLOAD_ACQUIRE_FINISH_TIME_OUT\r\n\r\n    # 创建正在上传的文件块\r\n    try:\r\n        created_file_part = FilePart.objects.create(name=name, part_num=part_num)\r\n    except Exception as ex:\r\n        pass\r\n    if created_file_part:\r\n        logger.info(\"upload_lock: file_name=%s, part_num=%d acquire success \" % (name, part_num))\r\n        return True, ret_code\r\n    else:\r\n        logger.info(\"upload_lock: file_name=%s, part_num=%d doesn't get lock \" % (name, part_num))\r\n        return False, ret_code\r\n\r\n\r\n# 文件上传完成后，释放锁\r\n@transaction.atomic\r\ndef upload_lock_release(name, part_num):\r\n    FilePart.objects.filter(name=name, part_num=part_num).delete()\r\n    logger.info(\"upload_lock: file_name=%s, part_num=%d release lock \" % (name, part_num))\r\n\r\n# 文件上传出错后，释放所有锁\r\n@transaction.atomic\r\ndef upload_lock_release(name):\r\n    FilePart.objects.filter(name=name).delete()\r\n    logger.info(\"upload_lock: file_name=%s release lock \" % (name))\r\n\r\n@transaction.atomic\r\ndef upload_lock_finish(name, part_num):\r\n    FilePart.objects.filter(name=name, part_num=part_num).update(status=UPLOAD_STATUS_FINISHED, update_time=datetime.datetime.now())\r\n    logger.info(\"upload_lock: file_name=%s, part_num=%d finish lock \" % (name, part_num))\r\n\r\n\r\n@transaction.atomic\r\ndef upload_complete(user, file_name, src_file_name, file_size, md5sum=\"\", activity_id=0, cur_user_id=0):\r\n    file_type, ext = get_file_type(file_name)\r\n    url = file_name\r\n    file_obj = FileObj.objects.filter(url=url).first()\r\n\r\n    if not file_obj:\r\n        file_obj = FileObj.objects.create(name=src_file_name, url=url, size=file_size, type=file_type,\r\n                                          ext=ext, uploader_id=user.id, activity_id=activity_id, md5sum=md5sum)\r\n    elif file_obj.del_flag == 1:\r\n        file_obj.del_flag = 0\r\n        file_obj.name = src_file_name\r\n        file_obj.size = file_size\r\n        file_obj.type = file_type\r\n        file_obj.ext = ext\r\n        file_obj.uploader_id = user.id\r\n        file_obj.activity_id = activity_id\r\n        file_obj.md5sum = md5sum\r\n        file_obj.save()\r\n    abs_url = get_file_url(url)\r\n    ret_dict = {\"id\": file_obj.id, 'url': abs_url, 'type': file_type, 'size': file_size, \"name\": src_file_name}\r\n    dict_resp = {\"c\": UPLOAD_ERR_FILE_SUCCESS[0], \"m\": UPLOAD_ERR_FILE_SUCCESS[1], \"d\": [ret_dict]}\r\n    return dict_resp\r\n\r\n\r\n\r\n","sub_path":"applications/upload_resumable/utils_upload.py","file_name":"utils_upload.py","file_ext":"py","file_size_in_byte":4332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"515068368","text":"import sys\nfrom com.L2jHogwarts.gameserver.ai import CtrlIntention\nfrom com.L2jHogwarts.gameserver.model.quest import State\nfrom com.L2jHogwarts.gameserver.model.quest import QuestState\nfrom com.L2jHogwarts.gameserver.model.quest.jython import QuestJython as JQuest\nfrom com.L2jHogwarts.gameserver.network.serverpackets import CreatureSay\nfrom com.L2jHogwarts.util.random import Rnd\n\nPOLLEN = 6391\nSKILL_NECTAR = 9998\n\n# Regado\nWATERED_SQUASH = [12774,12775,12776,12777,12778,12779]\n\nclass squash(JQuest) :\n\n    def __init__(self,id,name,descr):\n        JQuest.__init__(self,id,name,descr)\n        # Crescido\n        self.adultSmallSquash = [12775,12776]\n        self.adultLargeSquash = [12778,12779]\n\n    def onAdvEvent(self,event,npc,player) :\n        objId = npc.getObjectId()\n        if event == \"Good By\" and npc and player :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Bom por !! RI MUITO.\"))\n            npc.onDecay()\n        elif event == \"Good By1\" and npc and player :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Adeus a todos ... A grande abóbora disse adeus ...\"))\n            npc.onDecay()\n        elif event == \"Good By2\" and npc and player :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Você pode mais rápido? Daqui a 30 segundos vou correr ...\"))\n        elif event == \"Good By3\" and npc and player :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Eu vou quebrar o relacionamento com você em 20 segundos!\"))\n        elif event == \"Good By4\" and npc and player :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Eu tenho apenas 10 segundos restantes! 9. 8. 7 ..!\"))\n        elif event == \"Good By5\" and npc and player :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Ei! Felizmente fique! Idiota, esqueça de mim!\"))\n        return\n\n    def onSkillUse(self,npc,player,skill):\n        npcId = npc.getNpcId()\n        skillId = skill.getId()\n        if skillId != SKILL_NECTAR : return\n        if npcId not in WATERED_SQUASH : return\n        objectId = npc.getObjectId()\n        if skillId == SKILL_NECTAR :\n            # Primeira rega\n            if npc.getNectar() == 0 :\n                if Rnd.get(2) == 1 :\n                    mytext = [\"Para poder crescer, eu tenho que beber apenas néctar ... e mais vezes\",\n                              \"Se você derramará néctar para mim mais rápido - eu crescerei mais rápido!\",\n                              \"Bem, acredite em mim, pule néctar! Eu posso me transformar em uma grande abóbora !!!\",\n                              \"Traga o néctar para cultivar uma abóbora!\",\n                              \"O fruto de uma linda jovem abóbora começa a brilhar quando a semente é enterrada! De agora em diante, será capaz de se tornar saudável e forte!\",\n                              \"Oh, muito tempo sem ver?\",\n                              \"Não esperava ver minha linda aparência?\",\n                              \"Ótimo! Isso é alguma coisa! Néctar\",\n                              \"Reabastecimento! Encha 5 garrafas para que eu possa me transformar em uma grande abóbora! Oh!\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n                    npc.addGood()\n                else :\n                    mytext = [\"Não se apresse! Demasiadas vezes, não tenho tempo!\",\n                              \"Eu não sou automático, não me dê uma taxa de fogo\",\n                              \"Mas onde você está com pressa! Demasiadas vezes, não tenho tempo!\",\n                              \"Oops, muito rápido novamente\",\n                              \"Vamos abrandar, não se apresse, lentamente, pegue a garrafa e, lentamente, despeje!\",\n                              \"Você não tem senso de velocidade? Mais devagar, vamos lá\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n            # Segunda rega\n            elif npc.getNectar() == 1 :\n                if Rnd.get(2) == 1 :\n                    mytext = [\"Eu quero ser uma grande abóbora!\",\n                              \"Yum, yum, yum! Saiu! Cuidado - bom!\",\n                              \"ocê acha que eu sou maduro e podre?\",\n                              \"Néctar - apenas o melhor! Hah Hah Hah\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n                    npc.addGood()\n                else :\n                    mytext = [\"Oh! Mais uma vez por! O néctar pode ser consumido muito rapidamente?\",\n                              \"Se eu morrer assim, você só pega uma abóbora jovem ...\",\n                              \"Cresça um pouco mais rápido! Seria bom ser uma abóbora grande, uma abóbora jovem não é boa!\",\n                              \"Que pequena abóbora você deve comer? Traga néctar, eu posso ser mais!\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n            # Terceira rega\n            elif npc.getNectar() == 2 :\n                if Rnd.get(2) == 1 :\n                    mytext = [\"Abóbora, faminta! Pedidos para saciar a sua sede!\",\n                              \"Bem, finalmente ... é muito gostoso! Existe mais?\",\n                              \"Você está cuidando de mim só para comer? Ótimo, é seu aleatório ... não dar maná para suicídio\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n                    npc.addGood()\n                else :\n                    mytext = [\"Você adiciona água? Que gosto?\",\n                              \"Mestre, salve-me ... Eu não tenho o sabor do néctar, devo morrer ...\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n            # Quarta rega\n            elif npc.getNectar() == 3 :\n                if Rnd.get(2) == 1 :\n                    mytext = [\"Muito bem, fazendo muito bem! Você sabe o que o próximo passo é fazer?\",\n                              \"Se você me pegar, eu te dou 10 milhões de adena !!! Você concorda?s\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n                    npc.addGood()\n                else :\n                    mytext = [\"Estou com fome Você quer que eu seque?\",\n                              \"Eu exijo que o néctar cresça um pouco mais rápido.\"]\n                    npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                    npc.addNectar()\n            # Quinta rega\n            elif npc.getNectar() == 4 :\n                if Rnd.get(2) == 1 :\n                    npc.addGood()\n                if npc.getGood() >= 3 :\n                    if npcId == 12774 :\n                        newGourd = self.addSpawn(12775,npc)\n                        newGourd.setOwner(player.getName())\n                        self.startQuestTimer(\"Good By\", 120000, newGourd, player)   # Após 2 minutos, o desaparecimento\n                        self.startQuestTimer(\"Good By2\", 90000, newGourd, player)   # 30 segundos antes de desaparecer\n                        self.startQuestTimer(\"Good By3\", 100000, newGourd, player)  # 20 segundos antes de desaparecer\n                        self.startQuestTimer(\"Good By4\", 110000, newGourd, player)  # 10 segundos antes de desaparecer\n                        mytext = [\"Abóbora jovem, sedenta! Como já cresceu?\",\n                                  \"Eu vou fugir em 2 minutos\"]\n                        npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                        npc.onDecay()\n                    else :\n                        newGourd = self.addSpawn(12778,npc)\n                        newGourd.setOwner(player.getName())\n                        self.startQuestTimer(\"Good By1\", 120000, newGourd, player)  # Após 2 minutos, o desaparecimento\n                        self.startQuestTimer(\"Good By2\", 90000, newGourd, player)   # 30 segundos antes de desaparecer\n                        self.startQuestTimer(\"Good By3\", 100000, newGourd, player)  # 20 segundos antes de desaparecer\n                        self.startQuestTimer(\"Good By4\", 110000, newGourd, player)  # 10 segundos antes de desaparecer\n                        mytext = [\"A caridade é uma característica muito boa. Agora olhe, me sinto mais bem\",\n                                  \"Eu vou fugir em 2 minutos\"]\n                        npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                        npc.onDecay()\n                else :\n                    if npcId == 12774 :\n                        newGourd = self.addSpawn(12776,npc)\n                        newGourd.setOwner(player.getName())\n                        mytext = [\"Eh! Foi - não foi! Existe! Agora! Você não pode cuidar corretamente? Estou tão podre!\",\n                                  \"Uau, pare? Por que obrigado\",\n                                  \"Jade de néctar ...\",\n                                  \"Você quer uma abóbora grande? Mas eu quero ficar um pouco de abóbora ...\"]\n                        npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                        npc.onDecay()\n                    if npcId == 12777 :\n                        newGourd = self.addSpawn(12779,npc)\n                        newGourd.setOwner(player.getName())\n                        mytext = [\"Eh! Foi - não foi! Existe! Agora! Você não pode cuidar corretamente? Estou tão podre!\",\n                                  \"Uau, pare? Por que obrigado\",\n                                  \"Jade de néctar ...\",\n                                  \"Você quer uma abóbora grande? Mas eu quero ficar um pouco de abóbora ...\"]\n                        npc.broadcastPacket(CreatureSay(objectId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n                        npc.onDecay()\n        return\n\n    def onAttack(self,npc,player,damage,isPet) :\n        npcId = npc.getNpcId()\n        objId = npc.getObjectId()\n        if npcId not in WATERED_SQUASH : return\n        if npcId  in self.adultLargeSquash :\n            if Rnd.get(30) < 2 :\n                mytext = [\"Mordidas tecer rendas ... para substituir ... corpo ...!\",\n                          \"Ha ha, cresci! Totalmente em tudo!\",\n                          \"Você não pode chtoli todo objetivo? Veja tudo para não fugir ...\",\n                          \"Eu conto seus socos! Oh, lembra soco de novo!\",\n                          \"Não perca seu tempo!\",\n                          \"Ha, esse som é realmente bom de se ouvir?\",\n                          \"Eu consumo seus ataques para crescer!\",\n                          \"Hora de bater de novo! Bata mais uma vez!\",\n                          \"Apenas música útil pode abrir uma grande abóbora ... Eu não posso ser aberto com uma arma!\"]\n                npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),mytext[Rnd.get(len(mytext))]))\n        return\n\n    def onKill(self,npc,player,isPet) :\n        npcId = npc.getNpcId()\n        objId = npc.getObjectId()\n        if npcId not in WATERED_SQUASH : return\n        if npcId in self.adultSmallSquash :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Abóbora abre !!\"))\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"eah! Abre! Muita coisa boa ...\"))\n        elif npcId in self.adultLargeSquash :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Abóbora abre !!\"))\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Yeah! Abre! Muita coisa boa ...\"))\n        else :\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Por que, mestre?\"))\n            npc.broadcastPacket(CreatureSay(objId,0,npc.getName(),\"Oh, coragem caiu !!\"))\n        return\n\nQUEST = squash(-1,\"group_template\",\"ai\")\n\nCREATED = State('Start', QUEST)\nQUEST.setInitialState(CREATED)\n\nfor i in WATERED_SQUASH:\n    QUEST.addSkillUseId(i)\n    QUEST.addAttackId(i)\n    QUEST.addKillId(i)","sub_path":"L2jHogwarts_DataPack/data/scripts/ai/group_template/squash.py","file_name":"squash.py","file_ext":"py","file_size_in_byte":12514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"447049285","text":"from os import sendfile\nfrom flask import Flask, render_template, request\nfrom flask_socketio import SocketIO\nfrom werkzeug.utils import send_file, send_from_directory\n\napp = Flask(__name__)\nsocketio = SocketIO(app)\n\nstatus = \"open\"\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route(\"/music/<path:filename>\")\ndef play(filename):\n    return send_from_directory(\"music\", filename, environ=request.environ)\n\n@socketio.on('open')\ndef open():\n    print('解錠します')\n    global status\n    status = \"open\"\n    socketio.emit('open_arduino')\n    socketio.emit('response_status', {\"status\": status})\n\n@socketio.on('close')\ndef close():\n    print('施錠します')\n    global status\n    status = \"close\"\n    socketio.emit('close_arduino')\n    socketio.emit('response_status', {\"status\": status})\n\n@socketio.on('request_status')\ndef status():\n    print('ステータスを送信します')\n    global status\n    socketio.emit('response_status', {\"status\": status})\n\n@socketio.on('bell')\ndef bell():\n    print('ベルを鳴らします')\n    socketio.emit('ring_bell')\n\nif __name__ == '__main__':\n    socketio.run(app, host='0.0.0.0', port=5000)","sub_path":"Server/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"254302486","text":"import numpy as np\nimport tensorflow as tf\nimport random\n\nfrom PIL import Image, ImageDraw\nfrom pycocotools.coco import COCO\nimport os\nimport psutil as psu\nimport urllib.request\nimport shutil\nimport zipfile\nimport itertools\nimport threading\n\nshape = (512,256)\n\ndef getImg(url, result, index):\n    global shape\n    try:\n        with urllib.request.urlopen(url, timeout=3) as resp:\n            img = Image.open(resp.fp)\n            i = img.resize(shape, Image.ANTIALIAS)\n            result[index] = np.asarray(i, dtype=float) / 255\n            if len(result[index].shape) != 3 or result[index].shape[-1] != 3:\n                result[index] = None\n            i.close()\n            img.close()\n    except:\n        result[index] = None\n    return 0\n\ndef getLbl(annotation, width, height, personID, result, index):\n    if result[index] is None:\n        img = Image.new(\"RGB\", (width, height), \"black\")\n    if annotation[\"category_id\"] == personID:\n        draw = ImageDraw.Draw(img)\n        if annotation[\"iscrowd\"] == 1:\n            draw.rectangle([(annotation[\"bbox\"][0], annotation[\"bbox\"][1]), (annotation[\"bbox\"][0] + annotation[\"bbox\"][2], annotation[\"bbox\"][1] + annotation[\"bbox\"][3])], fill=(255,255,255))\n        else:\n            for segment in annotation[\"segmentation\"]:\n                xy = []\n                for i in range(1, len(segment), 2):\n                    xy.append((segment[i - 1], segment[i]))\n                draw.polygon(xy, fill=(255,255,255))\n    i = img.resize(shape, Image.ANTIALIAS)\n    result[index] = np.ceil(np.asarray(i, dtype=float)[:,:,0] / 255)\n    i.close()\n    img.close()\n    return 0\n'''\nThe data generator will be responsible for subdividing the input data into training sets and test sets\nIt will subsample classes per batch to solve class imbalance issues.\n'''\nclass DataGenerator(tf.keras.utils.Sequence):\n    def __init__(self, annotationFile, validationSplit=0.3, samplesPerClass=50, batchCount=10, isValidation = False, validationPerson = None, validationBackground = None):\n\n        #store the samplesPerClass\n        self.samplesPerClass = samplesPerClass\n        self.batchCount = batchCount\n        #initialize coco api\n        self.coco = COCO(annotationFile)\n        self.isValidation = isValidation\n        person = self.coco.getCatIds(catNms=['person'])\n        self.personID = person[0]\n        self.classWeights = tf.keras.backend.variable(np.array([1, 1], dtype=\"float32\"), dtype=\"float32\")\n        self.epochCounter = 0\n        if isValidation == False:\n            cats = self.coco.loadCats(self.coco.getCatIds())\n            nms=[cat['name'] for cat in cats]\n            nms.remove(\"person\")\n\n            self.person = self.coco.getImgIds(catIds=person)\n            background = self.coco.getCatIds(catNms=nms)\n            backgrounds = itertools.combinations(background, 3)\n            self.background = []\n            for b in backgrounds:\n                self.background.extend(self.coco.getImgIds(catIds=b))\n            for p in self.person:\n                try:\n                    self.background.remove(p)\n                except:\n                    pass\n            #initial randomize\n            self.person = np.array(self.person, dtype=int)\n            self.background = np.array(self.background, dtype=int)\n\n            i = random.sample(range(len(self.person)), len(self.person))\n            self.person = self.person[i]\n            personValidationIndex = int(self.person.shape[0] * (1 - validationSplit))\n            personVal = self.person[personValidationIndex:]\n            self.person = self.person[:personValidationIndex]\n            i = random.sample(range(len(self.background)), len(self.background))\n            self.background = self.background[i]\n            backgroundValidationIndex = int(self.background.shape[0] * (1 - validationSplit))\n            backgroundVal = self.background[backgroundValidationIndex:]\n            self.background = self.background[:backgroundValidationIndex]\n            self.validationGenerator = DataGenerator(annotationFile, validationSplit, samplesPerClass, batchCount, True, personVal, backgroundVal)\n            self.sampleData()\n        else:\n            self.person = validationPerson\n            self.background = validationBackground\n\n    def sampleData(self):\n        global shape\n        self.images = []\n        self.labels = []\n        u = []\n        personsIndex = random.sample(range(self.person.shape[0]), self.samplesPerClass)\n        u.extend(self.person[personsIndex])\n        backgroundIndex = random.sample(range(self.background.shape[0]), self.samplesPerClass)\n        u.extend(self.background[backgroundIndex])\n\n        u = self.coco.loadImgs(u)\n        r = [None] * len(u)\n        threads = [threading.Thread(target=getImg, args=(url[\"coco_url\"],r,u.index(url))) for url in u]\n        for thread in threads:\n            thread.start()\n        while True:\n            recheck = False\n            for thread in threads:\n                if thread.isAlive():\n                    recheck = True\n            if recheck == False:\n                break\n        i = 0\n        totalPersonPixels = 0\n        for res in r:\n            if res is not None:\n                self.images.append(res)\n                a = self.coco.getAnnIds(imgIds=[u[i][\"id\"]], iscrowd=None)\n                a = self.coco.loadAnns(a)\n                r1 = [None] * len(a)\n                threads = [threading.Thread(target=getLbl, args=(annotation, u[i][\"width\"], u[i][\"height\"], self.personID, r1, a.index(annotation))) for annotation in a]\n                for thread in threads:\n                    thread.start()\n                while True:\n                    recheck = False\n                    for thread in threads:\n                        if thread.isAlive():\n                            recheck = True\n                    if recheck == False:\n                        break\n                r1 = np.stack(r1, axis=0)\n                label = np.amax(r1, axis=0)\n                totalPersonPixels += np.sum(label)\n                label = np.stack([label, 1 - label], axis=2)\n                self.labels.append(label)\n            i = i + 1\n        personWeight = (shape[0] * shape[1] * len(self.images)) / totalPersonPixels\n        tf.keras.backend.set_value(self.classWeights, np.array([personWeight, 1 / personWeight], dtype=\"float32\"))\n        self.images = np.stack(self.images, axis=0)\n        self.labels = np.stack(self.labels, axis=0)\n\n        #final shuffle\n        indexes = random.sample(range(self.images.shape[0]), self.images.shape[0])\n        self.images = self.images[indexes]\n        self.labels = self.labels[indexes]\n\n        self.len = int(self.images.shape[0] / self.batchCount)\n\n        if self.isValidation == False:\n            self.validationGenerator.sampleData()\n\n    def fit(self, model, epochs, callbacks=[]):\n        self.epochCounter = 0\n        model.fit_generator(generator=self, validation_data=self.validationGenerator, epochs=epochs, callbacks=callbacks, use_multiprocessing=False, workers=0, shuffle=True)\n\n    def __len__(self):\n        return self.batchCount\n\n    def __getitem__(self, index):\n        s = slice(index * self.len, (index + 1) * self.len)\n        return self.images[s], self.labels[s]\n\n    def on_epoch_end(self):\n        self.epochCounter = self.epochCounter + 1\n        if self.epochCounter % 20 == 0:\n            self.sampleData()\n        else:\n            #when we finish an epoch we will shuffle\n            indexes = random.sample(range(self.images.shape[0]), self.images.shape[0])\n            self.images = self.images[indexes]\n            self.labels = self.labels[indexes]\n\n    def pixelwise_crossentropy(self):\n        def loss(y_true, y_pred):\n            y_pred = tf.clip_by_value(y_pred, tf.keras.backend.epsilon(), 1. - tf.keras.backend.epsilon())\n            return - tf.reduce_sum(tf.multiply(y_true * tf.keras.backend.log(y_pred), self.classWeights))\n        return loss\n","sub_path":"cocoDataGenerator.py","file_name":"cocoDataGenerator.py","file_ext":"py","file_size_in_byte":7949,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"596951483","text":"import unittest\n\nfrom util import merge_dict, default_data\nfrom board import Board\nfrom a_star import a_star\n\nclass TestAStar(unittest.TestCase):\n  def test_safe_neighbor(self):\n    board = Board(default_data())\n    start = (0, 0)\n    goal = (1, 0)\n\n    \"\"\"\n    | | | |\n    | | | |\n    |S|G| |\n    \"\"\"\n\n    path = a_star(board, start, goal)\n    self.assertEqual(path, [start, goal])\n\n  def test_navigate_around_obstacle(self):\n    test_data = default_data()\n    merge_dict(test_data, {\n      \"board\": {\n        \"snakes\": [{\n          \"id\": \"them\",\n          \"health\": 100,\n          \"body\": [(1, 0), (1, 1)]\n        }]\n      }\n    })\n    board = Board(test_data)\n    start = (0, 0)\n    goal = (2, 0)\n\n    \"\"\"\n    | | | |\n    | |█| |\n    |S|█|G|\n    \"\"\"\n\n    path = a_star(board, start, goal)\n    self.assertEqual(path, [\n      start,\n      (0, 1),\n      (0, 2),\n      (1, 2),\n      (2, 2),\n      (2, 1),\n      goal\n    ])\n\nif __name__ == '__main__':\n  unittest.main()\n","sub_path":"test_a_star.py","file_name":"test_a_star.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"652932708","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nfrom glob import glob\n\n\nfrom PIL import Image\nfrom keras.utils.vis_utils import plot_model\nfrom numpy import zeros, ones, array\nfrom numpy.random import randint\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import (Activation, BatchNormalization, LeakyReLU, Dense, Reshape,\n                          Conv2D, Conv2DTranspose, Flatten, Input, UpSampling2D)\nfrom keras.optimizers import Adam\n\nimport numpy as np\n\n\nCONFIG = {\n    'size': 32,   # size of generated pictures\n    'ngf': 16,    # number of G filters for the first conv layer\n    'nx': 100,    # dimension for X\n    'ny': 100,    # dimension for Y\n    'nc': 3,      # number of output channels\n    'ndf': 64,    # number of D filters for the first conv layer\n    'lr': 0.0002, # initial learning rate for adam\n    'beta1': 0.5, # momentum term for adam\n    'alpha': 0.2, # LeakyReLU slope parameter\n    'lp':  0.0001 # Lambda parameter\n}\n\nclass CYCLEGAN:\n    def __init__(self, **cfg):\n        self.img_shape = (cfg['size'], cfg['size'], cfg['nc'])\n        self.nx = cfg['nx']\n        self.ny = cfg['ny']\n        self.ngf, self.ndf = cfg['ngf'], cfg['ndf']\n        self.alpha = cfg['alpha']\n        self.channels = cfg['nc']\n        self.nc = cfg['nc']\n        self.lp = cfg['lp']\n\n        optimizer = Adam(lr=cfg['lr'], beta_1=cfg['beta1'])\n\n        self.D_Y = self.build_discriminator()\n        self.D_Y.compile(loss='mean_squared_error', optimizer=optimizer,\n                            metrics=['accuracy'])\n        self.D_Y.trainable = False\n\n        self.D_X = self.build_discriminator()\n        self.D_X.compile(loss='mean_squared_error', optimizer=optimizer,\n                            metrics=['accuracy'])\n        self.D_X.trainable = False\n\n        self.G = self.build_generator()\n        self.F = self.build_generator()\n\n        x = Input(shape=self.img_shape)\n        y = Input(shape=self.img_shape)\n\n        G_x = self.G(x)\n        F_y = self.F(y)\n\n        F_G_x = self.F(G_x)\n        G_F_y = self.G(F_y)\n\n        valid_Y = self.D_Y(G_x)\n        valid_X = self.D_X(F_y)\n\n        self.combined = Model (inputs=[x, y],\n                outputs=[valid_Y, valid_X,\n                         F_G_x, G_F_y,\n                         G_x, F_y])\n\n        self.combined.compile (loss=['mean_squared_error',\n                                    'mean_squared_error',\n                                    'mean_absolute_error',\n                                    'mean_absolute_error',\n                                    'mean_absolute_error',\n                                    'mean_absolute_error'],\n                                loss_weights=[1, 1,\n                                            self.lp, self.lp,\n                                            self.lp, self.lp],\n                                optimizer=optimizer)\n\n        self.combined.summary()\n\n    def train(self, epochs, batch_size, save_interval):\n        # TODO: load images\n        x_train = self.load_images('dog')\n        y_train = self.load_images('cat')\n\n        half_batch = batch_size // 2\n\n        labels_true = np.ones((batch_size,))\n        labels_false = np.zeros((batch_size,))\n\n        for epoch in range(epochs):\n            ## Discriminator\n            print(\"Epoch %i\" % epoch)\n\n            real_x = x_train[randint(0, x_train.shape[0], half_batch)]\n            real_y = y_train[randint(0, y_train.shape[0], half_batch)]\n\n            d_loss_r1 = self.D_Y.train_on_batch(real_y, ones((half_batch, 1)))\n            d_loss_r2 = self.D_X.train_on_batch(real_x, ones((half_batch, 1)))\n\n            fake_Y = self.G.predict(real_x)\n            d_loss_f1 = self.D_Y.train_on_batch(fake_Y, zeros((half_batch, 1)))\n\n            fake_X = self.F.predict(real_y)\n            d_loss_f2 = self.D_X.train_on_batch(fake_X, zeros((half_batch, 1)))\n\n            print(\"Discriminator losses:\", d_loss_r1, d_loss_r2, d_loss_f1, d_loss_f2)\n\n            #print('[%04i] [D loss: %.3f, acc: %.2f%%]' %\n            #     (epoch, d_loss[0], 100*d_loss[1]))\n\n            print(\"\\nGlobal GAN\")\n            g_loss = self.combined.train_on_batch([real_x, real_y],\n                                                  [labels_true, labels_true,\n                                                   real_x, real_y,\n                                                   real_x, real_y])\n\n            if epoch % save_interval == 0:\n                self.save_images('cats', real_x, epoch)\n                self.save_images('dogs', real_y, epoch)\n\n    def load_images(self, animals):\n        xs = []\n\n        for path in glob('input/' + animals + '.*.jpg'):\n            img = array(Image.open(path))\n            if img.shape == self.img_shape:\n                xs.append(img)\n            else:\n                print('bad shape for %s: %s' % (path, img.shape))\n\n        return array(xs)\n\n    def save_images(self, name, inpt, epoch, n=(5, 5)):\n        if name == 'cats':\n            imgs = self.gen1.predict(inpt)\n        else:\n            imgs = self.gen2.predict(inpt)\n\n        # size of a tile\n        s0 = self.img_shape[0] + 2\n        s1 = self.img_shape[1] + 2\n\n        mode = 'RGB'\n        try:\n            out = Image.new('RGB', (2 + n[0]*s0, 2 + n[1]*s1))\n        except:\n            mode = 'rgb'\n            out = Image.new('rgb', (2 + n[0]*s0, 2 + n[1]*s1))\n\n        for i in range(n[0]):\n            for j in range(n[1]):\n                out.paste(Image.fromarray(imgs[n[1]*i + j,:,:,:], mode=mode),\n                          (2 + s0*i, 2 + s1*j))\n\n        out.save('output/' + name + '/cyclegan_%04i.png' % epoch)\n\n    def build_generator(self):\n\n        model = Sequential()\n\n        # We use dimension (6, 6, 256) because we want to obtain 96x96-shaped images,\n        # and 96 = 6 * (2 ** k), where k is the depth of the network in terms of\n        # convolutionnal layers: 4.\n        # We use 256 neurons as in the Chairs implementation, in the InfoGAN paper\n\n        model.add(Conv2D(128, kernel_size=4, strides=2, input_shape=self.img_shape))\n        model.add(Activation(\"relu\"))\n        model.add(BatchNormalization(momentum=0.8))\n\n        model.add(Conv2D(128, kernel_size=4, strides=2, input_shape=self.img_shape))\n        model.add(Activation(\"relu\"))\n        model.add(BatchNormalization(momentum=0.8))\n\n        model.add(UpSampling2D())\n        model.add(Conv2D(128, kernel_size=3, padding=\"same\"))\n        model.add(Activation(\"relu\"))\n        model.add(BatchNormalization(momentum=0.8))\n\n        model.add(UpSampling2D())\n        model.add(Conv2D(64, kernel_size=3, padding=\"same\"))\n        model.add(Activation(\"relu\"))\n        model.add(BatchNormalization(momentum=0.8))\n\n        model.add(Conv2D(self.channels, kernel_size=3, padding='same'))\n        model.add(Activation(\"tanh\"))\n\n        gen_input = Input(shape=self.img_shape)\n        img = model(gen_input)\n\n        print(\"Generator:\")\n        model.summary()\n        plot_model(model, to_file='model_plots/generator_plot.png', show_shapes=True, show_layer_names=True)\n\n        return Model(gen_input, img)\n\n    def build_discriminator(self):\n        \"\"\"Create the discriminator model as described in the paper.\"\"\"\n\n        model = Sequential()\n\n        model.add(Conv2D(filters=self.ndf, kernel_size=4, padding='same',\n                         strides=2, input_shape=self.img_shape))\n        model.add(LeakyReLU(self.alpha))\n\n        model.add(Conv2D(filters=self.ndf, kernel_size=4, padding='same',\n                         strides=2))\n        model.add(LeakyReLU(self.alpha))\n        model.add(BatchNormalization())\n\n        model.add(Conv2D(filters=self.ndf * 8, kernel_size=4, padding='same',\n                         strides=2))\n        model.add(LeakyReLU(self.alpha))\n        model.add(BatchNormalization())\n\n        model.add(Conv2D(filters=1, kernel_size=4, activation='sigmoid'))\n        model.add(Flatten())\n\n        print('\\n%%% Discriminator %%%')\n        model.summary()\n\n        return model\n\n\nif __name__ == \"__main__\":\n    cy = CYCLEGAN(**CONFIG)\n    cy.train(10, 64, 2)\n","sub_path":"cyclegan/cyclegan.py","file_name":"cyclegan.py","file_ext":"py","file_size_in_byte":8043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"12626366","text":"#\n# [986] Largest Time for Given Digits\n#\n# https://leetcode.com/problems/largest-time-for-given-digits/description/\n#\n# algorithms\n# Easy (30.20%)\n# Total Accepted:    2.9K\n# Total Submissions: 9.5K\n# Testcase Example:  '[1,2,3,4]'\n#\n# Given an array of 4 digits, return the largest 24 hour time that can be\n# made.\n#\n# The smallest 24 hour time is 00:00, and the largest is 23:59.  Starting from\n# 00:00, a time is larger if more time has elapsed since midnight.\n#\n# Return the answer as a string of length 5.  If no valid time can be made,\n# return an empty string.\n#\n#\n#\n#\n# Example 1:\n#\n#\n# Input: [1,2,3,4]\n# Output: \"23:41\"\n#\n#\n#\n# Example 2:\n#\n#\n# Input: [5,5,5,5]\n# Output: \"\"\n#\n#\n#\n#\n# Note:\n#\n#\n# A.length == 4\n# 0 <= A[i] <= 9\n#\n#\n#\n#\n\n\nclass Solution:\n    def isValid(self, s):\n        if s[3] >= '6':\n            return False\n        return s[0] < '2' or s[0] == '2' and s[1] <= '3'\n\n    def largestTimeFromDigits(self, A):\n        \"\"\"\n        :type A: List[int]\n        :rtype: str\n        \"\"\"\n        if not A or len(A) < 4:\n            return \"\"\n        res = \"\"\n        # first list all possible combination\n        for a in range(4):\n            for b in range(4):\n                if b != a:\n                    for c in range(4):\n                        if c != b and c != a:\n                            for d in range(4):\n                                if d != c and d != b and d != a:\n                                    s = str(A[a])+str(A[b])+':' + \\\n                                        str(A[c]) + str(A[d])\n                                    # check whether it is valid\n                                    if self.isValid(s):\n                                        if res == \"\" or s > res:\n                                            # then find max one\n                                            res = s\n        return res\n","sub_path":"Easy/949.largest-time-for-given-digits.python3.py","file_name":"949.largest-time-for-given-digits.python3.py","file_ext":"py","file_size_in_byte":1860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"478329257","text":"#!/usr/bin/env python3\nimport os\nfrom PIL import Image\n#the script and the images dir are in same dir\nk = os.listdir('./images')\nfor i in k:\n    o = os.path.splitext(i)[0]\n    try:\n        with Image.open('./images/'+i).convert('RGB') as im:\n            im.thumbnail((128,128))\n            im.rotate(270).save('/opt/icons/'+o,'jpeg')\n    except OSError:\n        print('OSError')\n","sub_path":"automating-real-world-tasks-with-py/week1.py","file_name":"week1.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"270029581","text":"n = int(input()) # 超過即開始獎勵之時數\nx = int(input()) # 正常單每單報酬\ny = int(input()) # 獎勵單每單報酬\nd1 = int(input()) # 時段1-4訂單數\nd2 = int(input()) # 時段5-8訂單數\nd3 = int(input()) # 時段9-12訂單數\nc = int(input()) # 每小時機會成本\n\ntotalOrders = d1 * 4 + d2 * 4 + d3 * 4\nrevenueList = [] # 存放完成每筆訂單拿到的報酬\nhourRevenueList = [] # 每小時的報酬\ntotalProfit, maxProfitHours = 0, 0\n\nfor i in range(1, totalOrders+1):\n  if i <= n:\n    revenueList.append(x)\n  else:\n    revenueList.append(y)\n\nfor i in range(3):\n  if i == 0:\n    for j in range(0, d1 * 4, d1):\n      hourRevenueList.append(sum(revenueList[j:j+d1]))\n  elif i == 1:\n    for j in range(d1 * 4, (d1 + d2) * 4, d2):\n      hourRevenueList.append(sum(revenueList[j:j+d2]))\n  else:\n    for j in range((d1 + d2) * 4, (d1 + d2 + d3) * 4, d3):\n      hourRevenueList.append(sum(revenueList[j:j+d3]))\n\nfor i in range(len(hourRevenueList)):\n  hourProfit = hourRevenueList[i] - c\n  totalProfit += hourProfit\n  if (i == 0):\n    maxProfit = totalProfit\n    maxProfitHours = i + 1\n  if (totalProfit > maxProfit):\n    maxProfit = totalProfit\n    maxProfitHours = i + 1\n\nprint(maxProfitHours, maxProfit)","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"440788295","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, division, print_function, absolute_import\nimport os\nimport datetime\n\nimport testdata\n\nfrom . import BaseTestCase, EnvironTestCase\nimport prom\nfrom prom.model import Orm\nfrom prom.config import Schema, Connection, DsnConnection, Index\nfrom prom.config import Field, ObjectField, JsonField\nfrom prom.compat import *\n\n\nclass SchemaTest(BaseTestCase):\n    def test___init__(self):\n        \"\"\"\n        I had set the class .fields and .indexes attributes to {} instead of None, so you\n        could only ever create one instance of Schema, this test makes sure that's been fixed\n        \"\"\"\n        s = Schema(\"foo\")\n        self.assertTrue(isinstance(s.fields, dict))\n        self.assertTrue(isinstance(s.indexes, dict))\n\n        s2 = Schema(\"bar\")\n        self.assertTrue(isinstance(s.fields, dict))\n        self.assertTrue(isinstance(s.indexes, dict))\n\n        s = Schema(\n            \"foo\",\n            bar=Field(int),\n            che=Field(str, True),\n            barche=Index(\"bar\", \"che\")\n        )\n        self.assertTrue(\"bar\" in s.fields)\n        self.assertTrue(\"che\" in s.fields)\n        self.assertTrue(\"barche\" in s.indexes)\n\n    def test___getattr__(self):\n        s = Schema(\"foo\")\n\n        with self.assertRaises(AttributeError):\n            s.foo\n\n        s.set_field(\"foo\", Field(int, True))\n        self.assertTrue(isinstance(s.foo, Field))\n\n    def test_set_field(self):\n        s = Schema(\"foo\")\n\n        with self.assertRaises(ValueError):\n            s.set_field(\"\", int)\n\n        with self.assertRaises(ValueError):\n            s.set_field(\"foo\", \"bogus\")\n\n        s.set_field(\"foo\", Field(int))\n        with self.assertRaises(ValueError):\n            s.set_field(\"foo\", int)\n\n        s = Schema(\"foo\")\n        s.set_field(\"foo\", Field(int, unique=True))\n        self.assertTrue(\"foo\" in s.fields)\n        self.assertTrue(\"foo\" in s.indexes)\n\n        s = Schema(\"foo\")\n        s.set_field(\"foo\", Field(int, ignore_case=True))\n        self.assertTrue(s.foo.options[\"ignore_case\"])\n\n    def test_set_index(self):\n        s = Schema(\"foo\")\n        s.set_field(\"bar\", Field(int, True))\n        s.set_field(\"che\", Field(str))\n\n        with self.assertRaises(ValueError):\n            s.set_index(\"foo\", Index())\n\n        with self.assertRaises(ValueError):\n            s.set_index(\"\", Index(\"bar\", \"che\"))\n\n        s.set_index(\"bar_che\", Index(\"che\", \"bar\"))\n        with self.assertRaises(ValueError):\n            s.set_index(\"bar_che\", Index(\"che\", \"bar\"))\n\n        s.set_index(\"testing\", Index(\"che\", unique=True))\n        self.assertTrue(s.indexes[\"testing\"].unique)\n\n    def test_primary_key(self):\n        s = self.get_schema()\n        self.assertEqual(s._id, s.pk)\n\n\nclass DsnConnectionTest(BaseTestCase):\n\n    def test_environ(self):\n        os.environ['PROM_DSN'] = \"prom.interface.postgres.PostgreSQL://localhost:5000/database#i0\"\n        os.environ['PROM_DSN_1'] = \"prom.interface.postgres.PostgreSQL://localhost:5000/database#i1\"\n        os.environ['PROM_DSN_2'] = \"prom.interface.postgres.PostgreSQL://localhost:5000/database#i2\"\n        os.environ['PROM_DSN_4'] = \"prom.interface.postgres.PostgreSQL://localhost:5000/database#i4\"\n        prom.configure_environ()\n        self.assertTrue('i0' in prom.get_interfaces())\n        self.assertTrue('i1' in prom.get_interfaces())\n        self.assertTrue('i2' in prom.get_interfaces())\n        self.assertTrue('i3' not in prom.get_interfaces())\n        self.assertTrue('i4' not in prom.get_interfaces())\n\n        prom.interface.interfaces.pop('i0', None)\n        prom.interface.interfaces.pop('i1', None)\n        prom.interface.interfaces.pop('i2', None)\n        prom.interface.interfaces.pop('i3', None)\n        prom.interface.interfaces.pop('i4', None)\n\n    def test_dsn(self):\n        tests = [\n            (\n                \"prom.interface.postgres.PostgreSQL://username:password@localhost:5000/database?option=1&var=2#fragment\",\n                {\n                    'username': \"username\",\n                    'interface_name': \"prom.interface.postgres.PostgreSQL\",\n                    'database': \"database\",\n                    'host': \"localhost\",\n                    'port': 5000,\n                    'password': \"password\",\n                    'name': 'fragment',\n                    'options': {\n                        'var': \"2\",\n                        'option': \"1\"\n                    }\n                }\n            ),\n            (\n                \"prom.interface.postgres.PostgreSQL://localhost:5/database2\",\n                {\n                    'interface_name': \"prom.interface.postgres.PostgreSQL\",\n                    'database': \"database2\",\n                    'host': \"localhost\",\n                    'port': 5,\n                }\n            ),\n            (\n                \"prom.interface.postgres.PostgreSQL://localhost/db3\",\n                {\n                    'interface_name': \"prom.interface.postgres.PostgreSQL\",\n                    'database': \"db3\",\n                    'host': \"localhost\",\n                }\n            ),\n            (\n                \"prom.interface.postgres.PostgreSQL://localhost/db3?var1=1&var2=2#name\",\n                {\n                    'interface_name': \"prom.interface.postgres.PostgreSQL\",\n                    'database': \"db3\",\n                    'host': \"localhost\",\n                    'name': \"name\",\n                    'options': {\n                        'var1': \"1\",\n                        'var2': \"2\"\n                    }\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite://../this/is/the/path\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': '../this/is/the/path'\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite://./this/is/the/path\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': './this/is/the/path'\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///this/is/the/path\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': '/this/is/the/path'\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite://:memory:#fragment_name\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \":memory:\",\n                    'name': 'fragment_name'\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite://:memory:?option=1&var=2#fragment_name\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \":memory:\",\n                    'name': 'fragment_name',\n                    'options': {\n                        'var': \"2\",\n                        'option': \"1\"\n                    }\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite://:memory:\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \":memory:\",\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///db4\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \"/db4\",\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///relative/path/to/db/4.sqlite\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \"/relative/path/to/db/4.sqlite\",\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///abs/path/to/db/4.sqlite\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \"/abs/path/to/db/4.sqlite\",\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///abs/path/to/db/4.sqlite?var1=1&var2=2\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \"/abs/path/to/db/4.sqlite\",\n                    'options': {\n                        'var1': \"1\",\n                        'var2': \"2\"\n                    }\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///abs/path/to/db/4.sqlite?var1=1&var2=2#name\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \"/abs/path/to/db/4.sqlite\",\n                    'name': \"name\",\n                }\n            ),\n            (\n                \"prom.interface.sqlite.SQLite:///abs/path/to/db/4.sqlite?var1=1&var2=2#name\",\n                {\n                    'interface_name': \"prom.interface.sqlite.SQLite\",\n                    'host': None,\n                    'database': \"/abs/path/to/db/4.sqlite\",\n                    'name': \"name\",\n                    'options': {\n                        'var1': \"1\",\n                        'var2': \"2\"\n                    }\n                }\n            ),\n        ]\n\n        for t in tests:\n            c = DsnConnection(t[0])\n            for attr, val in t[1].items():\n                self.assertEqual(val, getattr(c, attr))\n\n\nclass ConnectionTest(BaseTestCase):\n    def test_interface_is_unique_each_time(self):\n        c = Connection(\n            interface_name=\"prom.interface.sqlite.SQLite\",\n            database=\":memory:\",\n        )\n\n        iids = set()\n        inters = set()\n        for x in range(10):\n            inter = c.interface\n            iid = id(inter)\n            self.assertFalse(iid in iids)\n            iids.add(iid)\n            inters.add(inter)\n\n    def test___init__(self):\n\n        c = Connection(\n            interface_name=\"prom.interface.sqlite.SQLite\",\n            database=\"dbname\",\n            port=5000,\n            some_random_thing=\"foo\"\n        )\n\n        self.assertEqual(5000, c.port)\n        self.assertEqual(\"dbname\", c.database)\n        self.assertEqual({\"some_random_thing\": \"foo\"}, c.options)\n\n\nclass ObjectFieldTest(EnvironTestCase):\n    field_class = ObjectField\n\n    def get_orm(self, default=None):\n        class EnvironObjectOrm(Orm):\n            interface = self.create_interface()\n            body = self.field_class(True, default=default)\n\n        return EnvironObjectOrm()\n\n    def get_sqlite_orm(self):\n        class IMethodPickleOrmSQLite(Orm):\n            interface = self.create_sqlite_interface()\n            body = self.field_class(True)\n\n        return IMethodPickleOrmSQLite()\n\n    def get_postgres_orm(self):\n        class IMethodPickleOrmPostgres(Orm):\n            interface = self.create_postgres_interface()\n            body = self.field_class(True)\n\n        return IMethodPickleOrmPostgres()\n\n    def test_default(self):\n        o = self.get_orm(default=dict)\n        o.body[\"foo\"] = 1\n        self.assertEqual(1, o.body[\"foo\"])\n\n    def test_imethods_pickle(self):\n        o = self.get_orm()\n        o.body = {\"foo\": 1}\n        o.save()\n\n        o2 = type(o).query.get_pk(o.pk)\n        self.assertEqual(o.body, o2.body)\n\n    def test_modify(self):\n        o = self.get_orm()\n        o.body = {\"bar\": 1}\n        o.save()\n\n        o.body[\"che\"] = 2\n        o.save()\n\n        o2 = type(o).query.get_pk(o.pk)\n        self.assertEqual(o.body, o2.body)\n\n    def test_iget_iset_override(self):\n        o = self.get_orm()\n        ocls = type(o)\n\n        ocls_iget = ocls.body.iget\n        ocls_iset = ocls.body.iset\n\n        @ocls.body.igetter\n        def body(cls, field_val):\n            self.assertTrue(isinstance(field_val, dict))\n            return field_val\n\n        @ocls.body.isetter\n        def body(cls, field_val, *args, **kwargs):\n            self.assertTrue(isinstance(field_val, dict))\n            return field_val\n\n        o.body = {\"bar\": 1, \"igetter\": 0, \"isetter\": 0}\n        o.save()\n        self.assertTrue(isinstance(o.body, dict))\n\n        o2 = type(o).query.get_pk(o.pk)\n        self.assertTrue(isinstance(o2.body, dict))\n\n        ocls.body.iget = ocls_iget\n        ocls.body.iset = ocls_iset\n\n\nclass JsonFieldTest(ObjectFieldTest):\n    field_class = JsonField\n\n\nclass FieldTest(EnvironTestCase):\n#     def test_dict_type(self):\n#         class DOrm(Orm):\n#             foo = Field(dict)\n# \n#         o = DOrm()\n#         o.foo = {\"bar\": 1, \"che\": 2}\n#         o.save()\n\n    def test_datetime_jsonable(self):\n        class FDatetimeOrm(Orm):\n            foo = Field(datetime.datetime)\n\n        o = FDatetimeOrm()\n        o.foo = datetime.datetime.min\n        r = o.jsonable()\n        self.assertTrue(\"foo\" in r)\n\n    def test_default(self):\n        class FDefaultOrm(Orm):\n            foo = Field(int, default=0)\n            bar = Field(int)\n\n        o = FDefaultOrm()\n        foo = o.schema.foo\n        self.assertEqual(0, foo.fdefault(o, None))\n        self.assertEqual(0, o.foo)\n\n        bar = o.schema.bar\n        self.assertEqual(None, bar.fdefault(o, None))\n        self.assertEqual(None, o.bar)\n\n    def test_fcrud(self):\n\n        class FCrudOrm(Orm):\n            foo = Field(int)\n\n            @foo.fsetter\n            def foo(self, v):\n                return 0 if v is None else v\n\n            @foo.fgetter\n            def foo(self, v):\n                ret = None if v is None else v + 1\n                return ret\n\n            @foo.fdeleter\n            def foo(self, val):\n                return None\n\n        o = FCrudOrm()\n\n        self.assertEqual(None, o.foo)\n        o.foo = 0\n        self.assertEqual(1, o.foo)\n        self.assertEqual(1, o.foo)\n\n        pk = o.save()\n        self.assertEqual(2, o.foo)\n        self.assertEqual(2, o.foo)\n\n        del o.foo\n        self.assertEqual(None, o.foo)\n        pk = o.save()\n        self.assertEqual(1, o.foo)\n\n        o.foo = 10\n        self.assertEqual(11, o.foo)\n        self.assertEqual(11, o.foo)\n\n        o.foo = None\n        self.assertEqual(1, o.foo)\n\n    def test_icrud(self):\n        class ICrudOrm(Orm):\n            foo = Field(int)\n\n            @foo.isetter\n            def foo(self, v, is_update, is_modified):\n                if is_modified:\n                    v = v + 1\n                elif is_update:\n                    v = v - 1\n                else:\n                    v = 0\n                return v\n\n            @foo.igetter\n            def foo(self, v):\n                if v > 1:\n                    v = v * 100\n                return v\n\n        o = ICrudOrm()\n        self.assertEqual(None, o.foo)\n\n        o.save()\n        self.assertEqual(0, o.foo)\n\n        o.foo = 5\n        self.assertEqual(5, o.foo)\n\n        o.save()\n        self.assertEqual(600, o.foo)\n\n        o2 = o.query.get_pk(o.pk)\n        self.assertEqual(600, o2.foo)\n\n\n    def test_fdeleter(self):\n        class FDOrm(Orm):\n            foo = Field(int)\n\n            @foo.fdeleter\n            def foo(self, val):\n                return None\n\n        o = FDOrm()\n        o.foo = 1\n        del o.foo\n        self.assertEqual(None, o.foo)\n\n    def test_property(self):\n        class FieldPropertyOrm(Orm):\n            foo = Field(int)\n\n            @foo.fgetter\n            def foo(self, val):\n                return val\n\n            @foo.fsetter\n            def foo(self, val):\n                return int(val) + 10 if (val is not None) else val\n\n        o = FieldPropertyOrm()\n\n        o.foo = 1\n        self.assertEqual(11, o.foo)\n\n        o.foo = 2\n        self.assertEqual(12, o.foo)\n\n        o.foo = None\n        self.assertEqual(None, o.foo)\n\n    def test_ref(self):\n        testdata.create_module(\"ref\", \"\\n\".join([\n                \"import prom\",\n                \"class Foo(prom.Orm):\",\n                \"    che = prom.Field(str)\",\n                \"\",\n                \"class Bar(prom.Orm):\",\n                \"    foo_id = prom.Field(Foo)\",\n                \"\"\n            ])\n        )\n\n        from ref import Foo, Bar\n\n        self.assertTrue(isinstance(Bar.schema.fields['foo_id'].schema, Schema))\n        self.assertTrue(issubclass(Bar.schema.fields['foo_id'].type, long))\n\n    def test_string_ref(self):\n        testdata.create_modules({\n            \"stringref.foo\": \"\\n\".join([\n                \"import prom\",\n                \"class Foo(prom.Orm):\",\n                \"    interface = None\",\n                \"    bar_id = prom.Field('stringref.bar.Bar')\",\n                \"\"\n            ]),\n            \"stringref.bar\": \"\\n\".join([\n                \"import prom\",\n                \"class Bar(prom.Orm):\",\n                \"    interface = None\",\n                \"    foo_id = prom.Field('stringref.foo.Foo')\",\n                \"\"\n            ])\n        })\n\n        from stringref.foo import Foo\n        from stringref.bar import Bar\n\n        self.assertTrue(isinstance(Foo.schema.fields['bar_id'].schema, Schema))\n        self.assertTrue(issubclass(Foo.schema.fields['bar_id'].type, long))\n        self.assertTrue(isinstance(Bar.schema.fields['foo_id'].schema, Schema))\n        self.assertTrue(issubclass(Bar.schema.fields['foo_id'].type, long))\n\n    def test___init__(self):\n        f = Field(str, True)\n        self.assertTrue(f.required)\n        self.assertTrue(issubclass(f.type, str))\n\n        with self.assertRaises(TypeError):\n            f = Field()\n\n        f = Field(int, max_length=100)\n        self.assertTrue(issubclass(f.type, int))\n        self.assertEqual(f.options['max_length'], 100)\n\n\n","sub_path":"tests/config_test.py","file_name":"config_test.py","file_ext":"py","file_size_in_byte":17974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"367954152","text":"\"\"\"Defautl test module.\"\"\"\nimport os\n\nimport testinfra.utils.ansible_runner\n\ntestinfra_hosts = testinfra.utils.ansible_runner.AnsibleRunner(\n    os.environ['MOLECULE_INVENTORY_FILE']).get_hosts('all')\n\n\ndef test_service(host):\n    \"\"\"Test service.\"\"\"\n    service = host.service(\"grafana-server\")\n    assert service.is_enabled\n    assert service.is_running\n\n\ndef test_socket(host):\n    \"\"\"Test socket connection.\"\"\"\n    socket = host.socket(\"tcp://0.0.0.0:3000\")\n    assert socket.is_listening\n","sub_path":"molecule/default/tests/test_default.py","file_name":"test_default.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"343200017","text":"import zlib\n\nfrom utils import PNGWrongHeaderError\nfrom utils import PNGNotImplementedError\n\n\n__author__ = 'ivo'\n__name__ = \"png_reader\"\n\n\ndef __read_png(filename):\n    \"\"\"\n    Precte vsechny byty souboru\n    :param filename: jmeno souboru\n    :return: byty souboru\n    \"\"\"\n    with open(filename, \"rb\") as f:\n        while True:\n            byte = f.read(1)\n            if byte:\n                yield byte\n            else:\n                break\n\n\ndef __check_header(data):\n    \"\"\"\n    Zkontroluje PNG header.\n    Vyhodi PNGWrongHeaderError pokud je header spatny.\n    :param data: hlavicka\n    \"\"\"\n    if not (data[0] == b'\\x89' and data[1] == b'P' and data[2] == b'N' and data[3] == b'G' and data[4] == b'\\r' and\n                    data[5] == b'\\n' and data[6] == b'\\x1a' and data[7] == b'\\n'):\n        raise PNGWrongHeaderError\n\n\ndef __read_chunks(data):\n    \"\"\"\n    Precte vsechny chunky PNG\n    :param data: bytove data\n    :return: list chunku (chunk = tuple(delka, typ, data, crc))\n    \"\"\"\n    p = 8  # prvnich 8B je hlavicka\n    chunks = list()\n    while p < len(data):\n        length = int.from_bytes(data[p] + data[p + 1] + data[p + 2] + data[p + 3], byteorder='big')\n        p += 4\n        type = data[p] + data[p + 1] + data[p + 2] + data[p + 3]\n        p += 4\n\n        chunkdata = b''\n        for i in range(p, p + length):\n            chunkdata += data[i]\n            p += 1\n\n        crc = int.from_bytes(data[p] + data[p + 1] + data[p + 2] + data[p + 3], byteorder='big')\n        p += 4\n        chunks.append((length, type, chunkdata, crc))\n\n    return chunks\n\n\ndef __check_settings(data):\n    \"\"\"\n    Zkontroluje ze PNG je ve formatau specifikace.\n    Vyhodi PNGNotImplementedError pokud neni.\n    :param data: data\n    \"\"\"\n    if not (data[0:1] == b'\\x08' and data[1:2] == b'\\x02' and data[2:3] == b'\\x00'and data[3:4] == b'\\x00' and data[4:5] == b'\\x00'):\n        raise PNGNotImplementedError\n\n\ndef __check_and_parse_first_chunk(chunk):\n    \"\"\"\n    Zkottroluje hlavickovy chunk.\n    :param chunk: hlavickovy chunk\n    :return: sirku, vysku\n    \"\"\"\n    data = chunk[2]  # hlavickovy chunk je tuple, 3 polozka jsou data\n\n    width = int.from_bytes(data[0:4], byteorder=\"big\")\n    height = int.from_bytes(data[4:8], byteorder=\"big\")\n\n    data = data[8:13]\n\n    __check_settings(data)\n\n    return width, height\n\n\ndef __decompress(chunks):\n    \"\"\"\n    Pomoci zlib decompresne data chunku a spoji do jednoho\n    :param chunks: list chunku\n    :return: RGB dekompresovane hodnoty\n    \"\"\"\n    data = b''\n    for i in range(1, len(chunks) - 1):\n        if chunks[i][1] == b'IDAT':\n            data += chunks[i][2]  # treti polozka jsou data\n\n    return zlib.decompress(data)\n\n\ndef __paeth(a, b, c):\n    \"\"\"\n    Provede paeth filtraci nad 3 hodnotami\n    \"\"\"\n    p = a + b - c\n    pa = abs(p - a)\n    pb = abs(p - b)\n    pc = abs(p - c)\n\n    if pa <= pb and pa <= pc:\n        pr = a\n    elif pb <= pc:\n        pr = b\n    else:\n        pr = c\n\n    return pr\n\n\ndef __rgb_data(tmp, f, pa=None, pb=None, pc=None):\n    \"\"\"\n    Prevede 3 byty na rgb hodnotu podle filtrace\n    :param tmp: data rgb\n    :param f: png filtr\n    :param pa: a ve filtru\n    :param pb: b ve filtru\n    :param pc: c ve filtru\n    :return: cervena, zelena, modra jako int tuple\n    \"\"\"\n    r = int.from_bytes(tmp[:1], byteorder=\"big\")\n    g = int.from_bytes(tmp[1:2], byteorder=\"big\")\n    b = int.from_bytes(tmp[2:3], byteorder=\"big\")\n\n    if f == 0:\n        return r, g, b\n\n    if f == 1:\n        r += pa[0]\n        g += pa[1]\n        b += pa[2]\n\n    if f == 2:\n        r += pb[0]\n        g += pb[1]\n        b += pb[2]\n\n    if f == 3:\n        r += ((pa[0] + pb[0]) / 2)\n        g += ((pa[1] + pb[1]) / 2)\n        b += ((pa[2] + pb[2]) / 2)\n\n    if f == 4:\n        r += __paeth(pa[0], pb[0], pc[0])\n        g += __paeth(pa[1], pb[1], pc[1])\n        b += __paeth(pa[2], pb[2], pc[2])\n\n    return r % 256, g % 256, b % 256\n\n\ndef create_rgb_matrix(image_data, width, height):\n    \"\"\"\n    Vytvori RGB matici z raw bytu.\n    :param image_data: rgb data obrazku\n    :param width: sirka\n    :param height: vyska\n    :return: RGB matice, pocet barev\n    \"\"\"\n    ret = [(0, 0, 0) for x in range(0, height * width)]\n    colours = {}\n    p = 0\n    for x in range(height):\n        f = int.from_bytes(image_data[p: p + 1], byteorder=\"big\")\n        p += 1\n\n        for y in range(width):\n            tmp = image_data[p:p + 3]\n\n            p += 3\n            a = ret[x * width + y - 1] if y > 0 else (0, 0, 0)\n            b = ret[(x - 1) * width + y] if x > 0 else (0, 0, 0)\n            c = ret[(x - 1) * width + y - 1] if x > 0 and y > 0 else (0, 0, 0)\n            rgb = __rgb_data(tmp, f, a, b, c)\n\n            if colours.get(rgb) is None:\n                colours[rgb] = 1\n            else:\n                colours[rgb] += 1\n\n            ret[x * width + y] = rgb\n\n    return ret, len(colours.keys())\n\n\ndef get_image(filename):\n    \"\"\"\n    Precte obrazek a vraci ho jako RGB matici.\n    :param filename: jmeno souboru\n    :return: rgb, pocet barev, sirka, vyska\n    \"\"\"\n    data = list(__read_png(filename))\n\n    __check_header(data)\n\n    chunks = __read_chunks(data)\n    width, height = __check_and_parse_first_chunk(chunks[0])\n    image_data = __decompress(chunks)\n\n    rgb, colours = create_rgb_matrix(image_data, width, height)\n\n    return rgb, colours, width, height","sub_path":"utils/png_reader.py","file_name":"png_reader.py","file_ext":"py","file_size_in_byte":5337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"14782192","text":"#!/usr/bin/python3\n# Setup script to install this package.\n# M.Blakeney, Mar 2018.\n\nfrom pathlib import Path\nfrom setuptools import setup\n\nname = 'timesched'\nmodule = name.replace('-', '_')\nhere = Path(__file__).resolve().parent\n\nsetup(\n    name=name,\n    version='1.5',\n    description='Improved simple time scheduler based on standard sched',\n    long_description=here.joinpath('README.md').read_text(),\n    long_description_content_type='text/markdown',\n    url='https://github.com/bulletmark/{}'.format(name),\n    author='Mark Blakeney',\n    author_email='mark@irsaere.net',\n    keywords='sched scheduler timer periodic cron crontab',\n    license='GPLv3',\n    py_modules=[module],\n    python_requires='>=3.4',\n    classifiers=[\n        'Programming Language :: Python :: 3',\n    ],\n    data_files=[\n        ('share/{}'.format(name), ['README.md']),\n    ],\n)\n","sub_path":"pypi_install_script/timesched-1.5.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"384489668","text":"import numpy as np\nimport networkx as nx\nimport torch\nimport random\nimport glob\nimport re\nfrom scipy.io import mmread\nimport os\nimport sys\ncurrent_dir = os.path.dirname(os.path.abspath(\"__file__\"))\nsys.path.append( str(current_dir) + '/../../../' )\nfrom setting_param import ratio_test\nfrom setting_param import ratio_valid\nfrom setting_param import adj_shape\nfrom setting_param import max_nnz_am\n\nfrom setting_param import all_node_num\nfrom setting_param import n_expanded\nfrom setting_param import L\nfrom setting_param import Model_link_prediction_appeared_InputDir as InputDir\n\n\ndef load_paths_from_dir(dir_path):\n    # dir 以下のファイル名のリストを取得\n    path_list = glob.glob(dir_path + \"/*\")\n    # ソート (ゼロ埋めされていない数字の文字列のソート)\n    path_list = np.array(sorted(path_list, key=lambda s: int(re.findall(r'\\d+', s)[-1])))\n    return path_list\n\ndef dev_test_split(all_idx, n_samples, ratio_test):\n    n_test = int(n_samples * ratio_test)\n    return all_idx[:-n_test], all_idx[-n_test:]\n\ndef train_valid_split(dev_idx, n_samples, ratio_valid):\n    n_valid = int(n_samples * ratio_valid)\n    return dev_idx[:-n_valid], dev_idx[-n_valid:]\n\ndef coo_scipy2coo_numpy(coo_scipy, max_nnz):\n    # scipy_cooをvalue, indicesに分解してnumpy配列に変換\n    # バッチ内のサイズを揃えるためにmax_nnzでゼロパディング\n    coo_numpy = np.zeros((3, max_nnz))\n    coo_numpy[:, :len(coo_scipy.data)] = np.vstack((coo_scipy.data, coo_scipy.row, coo_scipy.col))\n    return coo_numpy\n\ndef in_out_generate(coo_numpy, n_node):\n    coo_numpy_in = coo_numpy.copy()\n    coo_numpy_out = np.zeros_like(coo_numpy)\n    coo_numpy_out[0] = coo_numpy[0]\n    coo_numpy_out[1] = coo_numpy[2] % n_node\n    coo_numpy_out[2] = (coo_numpy[2] // n_node) * n_node + coo_numpy[1]\n    return np.stack((coo_numpy_in, coo_numpy_out))\n\ndef get_A_in_per_batch(A, batch):\n    A_in = A[batch][0]  # (3, max_nnz)\n    nnz = int(A_in[0].sum().item())\n    A_in = A_in[:, :nnz]  # (3, nnz)\n    return A_in\n\ndef get_adj_per_t(A_in, t):\n    col = A_in[2]\n    idx = (t * all_node_num) <= col\n    idx = idx * (col < ((t + 1) * all_node_num))\n    A_t = A_in[:, idx]  # (3, nnz_per_t)\n    A_t[2] = A_t[2] % all_node_num  # adjacency matrix per t\n    return A_t\n\ndef get_cur_adj(A_t):\n    cur_adj = {}\n    cur_adj['vals'] = A_t[0]  # (nnz_per_t, )\n    cur_adj['idx'] = A_t[1:].t().long()  # (nnz_per_t, 2)\n    return cur_adj\n\ndef make_sparse_tensor(adj,tensor_type,torch_size):\n    if len(torch_size) == 2:\n        tensor_size = torch.Size(torch_size)\n    elif len(torch_size) == 1:\n        tensor_size = torch.Size(torch_size*2)\n\n    if tensor_type == 'float':\n        test = torch.sparse.FloatTensor(adj['idx'].t(),\n                                      adj['vals'].type(torch.float),\n                                      tensor_size)\n        return torch.sparse.FloatTensor(adj['idx'].t(),\n                                      adj['vals'].type(torch.float),\n                                      tensor_size)\n    elif tensor_type == 'long':\n        return torch.sparse.LongTensor(adj['idx'].t(),\n                                      adj['vals'].type(torch.long),\n                                      tensor_size)\n    else:\n        raise NotImplementedError('only make floats or long sparse tensors')\n\ndef sparse_prepare_tensor(tensor,torch_size):\n    tensor = make_sparse_tensor(tensor,\n                                tensor_type = 'float',\n                                torch_size = torch_size)\n    return tensor\n\ndef get_A_last(A):\n    \"\"\"\n    時系列の最後の隣接行列を取得\n    \"\"\"\n    batch_adj_list = []\n    for batch in range(A.shape[0]):\n        A_in = get_A_in_per_batch(A, batch) # (3, nnz)\n        A_t = get_adj_per_t(A_in, L-1) # (3, nnz_per_t)\n        cur_adj = get_cur_adj(A_t)\n        cur_adj = sparse_prepare_tensor(cur_adj, torch_size=[all_node_num])\n        batch_adj_list.append(cur_adj)\n    A_last = torch.stack(batch_adj_list, 0)\n    return A_last\n\nclass BADataset():\n    def __init__(self, path, L, is_train, is_valid, is_test):\n        # 入力ファイルのPATHのリストを取得\n        attribute_paths = load_paths_from_dir(path + '/input')\n        adjacency_paths = load_paths_from_dir(InputDir + '/input/adjacency')\n        label_paths = load_paths_from_dir(path + '/label')\n        mask_paths = load_paths_from_dir(path + '/mask')\n\n        # split data\n        n_samples = len(label_paths)\n        all_idx = list(range(n_samples))\n        dev_idx, test_idx = dev_test_split(all_idx, n_samples, ratio_test)\n        train_idx, valid_idx = dev_test_split(dev_idx, n_samples, ratio_valid)\n\n        if is_train:\n            target_idx = train_idx\n        elif is_valid:\n            target_idx = valid_idx\n        elif is_test:\n            target_idx = test_idx\n        else:\n            target_idx = all_idx\n\n        self.idx_list = target_idx\n        self.attribute = [np.load(attribute_paths[idx]) for idx in target_idx]\n        self.label = [mmread(label_paths[idx]).toarray() for idx in target_idx]\n        self.mask = [mmread(mask_paths[idx]).toarray() for idx in target_idx]\n\n        \"\"\"\n        # adjacency: [n_sample * (2, 3, max_nnz_am)] → (n_sample, all_node_num, all_node_num) に変換\n        self.adjacency = [in_out_generate(coo_scipy2coo_numpy(mmread(adjacency_paths[idx]), max_nnz_am), adj_shape[0])\n                          for idx in target_idx]\n        self.adjacency = get_A_last(torch.Tensor(self.adjacency))\n        \"\"\"\n        self.L = L\n\n        self.indic_test_ones = []\n        self.indic = []\n        self.indic_mask = []\n        for n in range(len(self.label)):\n            \"\"\"\n            indic = self.adjacency[n]._indices().numpy().transpose(1, 0) # (nnz, 2) (有向グラフ：[0, 1]と[1, 0]が存在)\n            indic_frozenset = set([frozenset({i, j}) for [i, j] in indic])\n            graph = nx.Graph()\n            graph.add_edges_from(indic.tolist())\n            indic = np.array(sorted(graph.edges)) # (nnz/2, 2) (無向グラフ：[0, 1]と[1, 0]を区別しない)\n\n            # test_ones用をサンプリングする\n            indic_test_ones = torch.Tensor(self.label[n]).to_sparse()._indices().numpy().transpose(1, 0)  # (nnz, 2) (有向グラフ：[0, 1]と[1, 0]が存在)\n            graph = nx.Graph()\n            graph.add_edges_from(indic_test_ones.tolist())\n            indic_test_ones = np.array(sorted(graph.edges))  # (nnz/2, 2) (無向グラフ：[0, 1]と[1, 0]を区別しない)\n            indic_test_ones = np.array([sorted(edge) for edge in indic_test_ones])  # (existing_node, new_node)の順序\n            indic_test_ones_frozenset = set([frozenset({i, j}) for [i, j] in indic_test_ones])\n\n            # test_zeros用をサンプリングする\n            indic_test_zeros = set()\n            while len(indic_test_zeros) < len(indic_test_ones):\n                i = int(indic_test_ones[random.sample(list(range(indic_test_ones.shape[0])), 1), 1])\n                j = int(indic[random.sample(list(range(indic.shape[0])), 1), 0])\n                while i == j:\n                    j = int(indic[random.sample(list(range(indic.shape[0])), 1), 0])\n                if not frozenset([i, j]) in indic_test_ones_frozenset:\n                    indic_test_zeros.add(frozenset([i, j]))\n            indic_test_zeros = np.array(sorted([sorted(edge) for edge in indic_test_zeros]))\n            \"\"\"\n            # test_onesをサンプリングする\n            indic_test_ones = torch.Tensor(self.label[n]).to_sparse()._indices().numpy().transpose(1, 0)  # (nnz, 2) (有向グラフ：[0, 1]と[1, 0]が存在)\n            graph = nx.Graph()\n            graph.add_edges_from(indic_test_ones.tolist())\n            indic_test_ones = np.array(sorted(graph.edges))  # (nnz/2, 2) (無向グラフ：[0, 1]と[1, 0]を区別しない)\n            indic_test_ones = np.array([sorted(edge) for edge in indic_test_ones])  # (existing_node, new_node)の順序\n            self.indic_test_ones.append(indic_test_ones)\n\n            # mask をスパース化する\n            indic_mask = torch.Tensor(self.mask[n]).to_sparse()._indices().numpy().transpose(1, 0)  # (nnz, 2) (有向グラフ：[0, 1]と[1, 0]が存在)\n            graph = nx.Graph()\n            graph.add_edges_from(indic_mask.tolist())\n            indic_mask = np.array(sorted(graph.edges))  # (nnz/2, 2) (無向グラフ：[0, 1]と[1, 0]を区別しない)\n            indic_mask = np.array([sorted(edge, reverse=True) for edge in indic_mask])  # (new_node, existing_node)の順序\n            self.indic_mask.append(indic_mask)\n\n            # 予測対象の座標\n            tmp = []\n            for i in range(all_node_num, all_node_num + n_expanded):\n                for j in range(all_node_num + n_expanded):\n                    tmp.append([i, j])\n            self.indic.append(np.array(tmp))\n\n    def __getitem__(self, index):\n        sample_idx = self.idx_list[index] + self.L\n        annotation = self.attribute[index]\n        label = self.indic_test_ones[index]\n        mask = self.indic_mask[index]\n        indic = self.indic[index]\n        return sample_idx, annotation, label, mask, indic\n\n    def __len__(self):\n        return len(self.idx_list)","sub_path":"Model/repeat1_link_prediction_new_AGATE/COSSIMMLP/utils/data/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":9255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"316226498","text":"import sys\nimport json\nfrom bs4 import BeautifulSoup\nfrom collections import OrderedDict\nfrom pyspark import SparkContext\n\n# --\n# Functions\n\ndef dictify_(x):\n    if not x:\n        return\n        \n    tmp = OrderedDict([])\n    \n    if x.attrs:\n        tmp['__attrs__'] = x.attrs\n    \n    for child in x.children:\n        if child.name:\n            curr = tmp.get(child.name)\n            if curr:\n                if isinstance(curr, list):\n                    tmp[child.name] = curr + [dictify_(child)]\n                else:\n                    tmp[child.name] = [curr, dictify_(child)]\n            else:\n                tmp[child.name] = dictify_(child)\n        else:\n            # If one child isn't a tag, grab text and stop\n            return x.text\n        \n    return OrderedDict(tmp)\n\ndef dictify(x):\n    tmp = BeautifulSoup(x, 'lxml').find('us-patent-grant')\n    tmp = dictify_(tmp)\n    return json.dumps(tmp)\n\n\n# --\n# Run\n\nusername = 'bjohnson'\nsc = SparkContext(appName='%s-uspto' % username)\n\ninpath = '/data/uspto/grants/full-text-dataxml/' + sys.argv[1]\noutpath = '/user/bjohnson/uspto/grants/full-text-datajson-' + sys.argv[1][:-1]\n\nsc.textFile(inpath)\\\n    .map(dictify)\\\n    .filter(lambda x: x != None)\\\n    .saveAsTextFile(\n        outpath, \n        compressionCodecClass=\"org.apache.hadoop.io.compress.GzipCodec\"\n    )\n\n","sub_path":"etl/dictify.py","file_name":"dictify.py","file_ext":"py","file_size_in_byte":1337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"83475248","text":"import seaborn as sns\r\nimport pandas as pd  \r\nimport lightgbm as lgb\r\nimport operator\r\nfrom sklearn.model_selection import StratifiedKFold\r\nimport matplotlib.pyplot as plt\r\n \r\n\r\ndef Modeling1(X_train,y_train):\r\n        \r\n    \r\n    model_lgb = lgb.LGBMClassifier(\r\n                n_jobs=4,\r\n                n_estimators=100000,\r\n                objective='multiclass', \r\n                metric='multi_logloss', \r\n                boost_from_average='false',\r\n                num_class=11,## we added\r\n                learning_rate=0.01,\r\n                leaves=64,\r\n                max_depth=-1,\r\n                tree_learner = \"serial\",\r\n                feature_fraction = 0.7,\r\n                bagging_freq = 5,\r\n                bagging_fraction = 0.7,\r\n                min_data_in_leaf=100,\r\n                silent=-1,\r\n                verbose=-1,\r\n                max_bin = 255,\r\n                bagging_seed = 11,\r\n                )\r\n    kf = StratifiedKFold(n_splits=5,shuffle=True,random_state=10)\r\n    \r\n    \r\n    \r\n        \r\n    \r\n    \r\n    \r\n    models =[]\r\n    for i, (train_idx,valid_idx) in enumerate(kf.split(X_train,y_train)):\r\n        print('...... training {}th fold \\n'.format(i+1))\r\n        tr_X = X_train.loc[train_idx]\r\n        tr_y = y_train.loc[train_idx]\r\n        \r\n        va_X = X_train.loc[valid_idx]\r\n        va_y = y_train.loc[valid_idx]\r\n        \r\n        model = model_lgb\r\n        model.fit(tr_X, tr_y, eval_set=[(tr_X, tr_y), (va_X, va_y)], eval_metric = 'multi_logloss', verbose=500, early_stopping_rounds = 300)\r\n        models.append(model)\r\n        \r\n     \r\n     \r\n    fts = X_train.columns.values \r\n    len(fts)\r\n    \r\n    fts_imp = dict(zip(fts,models[1].feature_importances_))\r\n    fts_imp = sorted(fts_imp.items(), key=operator.itemgetter(1),reverse=True)\r\n    fts_imp[:18]    \r\n    feature_imp = pd.DataFrame({'Value':models[1].feature_importances_,'Feature':X_train.columns})\r\n        \r\n        \r\n    plt.figure(figsize=(40, 20))\r\n    sns.set(font_scale = 5)\r\n    sns.barplot(x=\"Value\", y=\"Feature\", data=feature_imp.sort_values(by=\"Value\", ascending=False)[:18])\r\n    \r\n    plt.title('LightGBM Features (avg over folds)')\r\n    plt.tight_layout()\r\n    plt.savefig('lgbm_importances-01.png')\r\n    plt.show()\r\n\r\n   \r\n    \r\n    return \r\n\r\n\r\n\r\n\r\n\r\n","sub_path":"Modeling1.py","file_name":"Modeling1.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"557404423","text":"#!/usr/bin/env python\nimport pygame, time, sys, argparse\nfrom random import randint\n\nclass CountdownClock(object):\n    DISPLAY_WIDTH = 1024\n    DISPLAY_HEIGHT = 768\n\n    BLACK = (0,0,0)\n    WHITE = (255,255,255)\n    RED = (255,0,0)\n    \n    def __init__(self, start_time):\n        self.finished = False\n        self.start_time = start_time\n        \n        pygame.init()\n        pygame.mixer.init()\n        self.times_up_file = \"TimesUp.ogg\"\n        self.times_up_sound = pygame.mixer.Sound(self.times_up_file)\n        \n        self.game_display = pygame.display.set_mode((CountdownClock.DISPLAY_WIDTH, CountdownClock.DISPLAY_HEIGHT))\n        pygame.display.set_caption('Countdown Clock')\n        self.game_display.fill(CountdownClock.WHITE)\n        pygame.display.update()\n\n    def text_objects(self, text, font):\n        self.text_surface = font.render(text, True, CountdownClock.BLACK)\n        return self.text_surface, self.text_surface.get_rect()\n\n    def red_text_objects(self, text, font):\n        self.text_surface = font.render(text, True, CountdownClock.RED)\n        return self.text_surface, self.text_surface.get_rect()\n\n    def message_display(self, text):\n        self.large_text = pygame.font.Font(None, 240)\n        self.text_surf, self.text_rect = self.text_objects(text, self.large_text)\n        self.text_rect.center = ((CountdownClock.DISPLAY_WIDTH/2),(CountdownClock.DISPLAY_HEIGHT/2))\n        self.game_display.blit(self.text_surf, self.text_rect)\n\n    def urgent_message_display(self, text):\n        self.large_text = pygame.font.Font(None,240)\n        self.text_surf, self.text_rect = self.red_text_objects(text, self.large_text)\n        self.text_rect.center = ((CountdownClock.DISPLAY_WIDTH/2),(CountdownClock.DISPLAY_HEIGHT/2))\n        self.game_display.blit(self.text_surf, self.text_rect)    \n\n    def times_up(self):\n        pygame.quit()\n        sys.exit()\n\n    def main_loop(self):\n        while not self.finished:\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.finished = True\n                if event.type == pygame.KEYUP:\n                    if event.key == pygame.K_SPACE:\n                        for t in range(self.start_time,-1,-1):\n                            self.game_display.fill(CountdownClock.WHITE)\n                            minutes = t / 60\n                            seconds = t % 60\n                            sf = \"{:02d}:{:02d}\".format(*divmod(t, 60))\n                            if t < 10:\n                                self.urgent_message_display(sf)\n                            else:\n                                self.message_display(sf)\n                            pygame.event.get()\n                            time.sleep(1)\n                            pygame.display.update()\n                            \n                        self.times_up_sound.play()\n                        time.sleep(4)\n                        self.times_up()\n                        \n        pygame.quit()\n        quit()\n\nnew_clock = CountdownClock(240)\nnew_clock.main_loop()\n\n##if __name__ == \"__main__\":\n##    parser = argparse.ArgumentParser(description=\"CountdownClock.py v1.0 A large-font graphical countdown clock. (c) J.J. Gerschler\")\n##    parser.add_argument(\"-s\", \"--seconds\", help=\"Number of seconds to count down.\")\n##    args = parser.parse_args()\n##    \n##    print(\"\")\n##    print(\"When screen loads, press the spacebar to start the countdown!\")\n##    print(\"\")\n##    new_clock = CountdownClock(int(args.seconds))\n##    new_clock.main_loop()\n\n","sub_path":"Countdown Clock/CountdownClock.py","file_name":"CountdownClock.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"77982742","text":"\n\nimport os\n\n\ndef factorial(n):\n    if n == 0:\n        return 1\n    else:\n        return factorial(n-1) * n\n\ndef binary_search(data, target, low, high):\n    \"\"\"\n        Return true if target is found in indicated portion of a Python list\n        The search only considers the portion from data[low] to data[high] inclusive\n    \"\"\"\n    \n    if low > high:\n        return False\n    else:\n        mid = (low + high) // 2\n        print(data[mid])\n        if target == data[mid]:\n            return True\n        elif target < data[mid]:\n            return binary_search(data, target, low, mid-1)\n        else:\n            return binary_search(data, target, mid+1, high)\n\ndef disk_usage(path):\n    \"\"\" Return the number of bytes used by a file/folder and any descendents \"\"\"\n    total = os.path.getsize(path)\n    if os.path.isdir(path):\n        for filename in os.listdir(path):\n            childpath = os.path.join(path, filename)\n            total += disk_usage(childpath)\n    \n    print('{0:<7}'.format(total), path)\n    return total\n\n\ndef linear_sum(S, n):\n    \"\"\" Return the sum of the first n numbers of sequence S. \"\"\"\n    if n == 0:\n        return 0\n    else:\n        return linear_sum(S, n-1) + S[n-1]\n\n\ndef reverse(S, start, stop):\n    \"\"\" Reverse elements in implicit slice[start:stop] \"\"\"\n    if start < stop-1:\n        S[start], S[stop-1] = S[stop-1], S[start]\n        print(S)\n        reverse(S, start+1, stop-1)\n\ndef power(x, n):\n    if n == 0:\n        return 1\n    return x * power(x, n-1)\n\n# R4-1 递归查找序列 S 的最大值\ndef recurse_max(S, n):\n    if n == 1:\n        return S[0]\n    m = recurse_max(S, n-1)\n    if m > S[n-1]:\n        return m\n    else:\n        return S[n-1]\n\n# R4-10 只使用加法和整数除法，求n以2为底的对数的整数部分\ndef log2(n):\n    if n < 2:\n        return 0\n    return 1 + log2(n / 2)\n\nif __name__ == '__main__':\n    # print(factorial(5))\n\n    # data = [2,4,5,7,8,9,12,14,17,19,22,25,27,28,33,37]\n    # result = binary_search(data, 23, 0, len(data)-1)\n    # print(result)\n\n    # disk_usage('D:\\courses')\n    # print(reverse(data, 0, len(data)))\n\n    # s = [1,5,4, 10,7,8,2]\n    # m = recurse_max(s, 6)\n    m = log2(1024)\n    print(m)\n    ","sub_path":"recurse.py","file_name":"recurse.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"335436876","text":"#!/usr/bin/env python3\n\nimport sys\nimport argparse\n\nfrom app import App\n\n\"\"\"\nREADME: This works! Although the API response isn't great. It doesn't seem to\nsay when an annotation was added, just that it was \"refreshed\". Will be holding\noff on any api-related updates until hlts_v2 is up and running.\n\nNOTE: Only works with hlts v.1.1.0 beta\n\nTo run with Books:\n- Close Books\n- Run: python3 run.py --setup\n  This will build folder structure and config files.\n- Set configuration in ~/.hltsync/config.json\n- Run: python3 run.py applebooks\n\"\"\"\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\n    \"reader\",\n    choices=[\"dummy\", \"applebooks\", \"kindle\"],\n    nargs=\"?\",\n    help=\"Which reader to sync.\",\n)\nparser.add_argument(\"-s\", \"--setup\", action=\"store_true\", help=\"Run initial setup.\")\n\nargs = parser.parse_args()\n\n\nif __name__ == \"__main__\":\n\n    app = App(args)\n\n    if args.setup:\n        sys.exit()\n\n    app.run()\n    sys.exit()\n","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"426945731","text":"#! /usr/bin/python3\n\nimport os\nimport sys\nimport smtplib\nimport json\nfrom email.mime.text import MIMEText\nfrom email.header import Header\nimport subprocess\nimport shlex\n\ndef issuper_oper(logline):\n    #\n    if logline.find(\"OK DOWNLOAD:\") != -1:\n       if logline.find(\"ftpsuper_\") != -1:\n           return \"ftpsuper_\"\n    \n    return \"\"\n\ndef readcfg(cfg_path):\n    with open(cfg_path, \"r\" , encoding=\"utf-8\") as f:\n        cfg = json.load(f)\n    \n    return cfg\n\ndef getcfg_lastmodifytime(cfg_path):\n    cfg_fd    = os.open(cfg_path, os.O_RDONLY)\n    file_info = os.fstat(cfg_fd)\n    return file_info.st_mtime\n\nmail_infos = {\n               'time':'unknown',\n               'operator':'unknown',\n               'access_ip':'unknown',\n               'file':'unknown',\n               'size':'unknown'\n            }\n\nmail_msg_template = \"\"\"\n<html>\n <body>\n  <table align=\"center\" border='1' cellpadding='1' cellspacing='0' width='90%'>\n   <tbody>\n     <tr><td align=\"center\" bgcolor='#FF0000' colspan=\"2\"><strong>Alarming</strong></td></tr>\n     <tr><td width=\"10%\" align=\"left\">time</td><td align=\"center\">{time}</td></tr>\n     <tr><td width=\"10%\" align=\"left\">operator</td><td align=\"center\">{operator}</td></tr>\n     <tr><td width=\"10%\" align=\"left\">file</td><td align=\"center\">{file}</td></tr>\n     <tr><td width=\"10%\" align=\"left\">size</td><td align=\"center\">{size}</td></tr>\n     <tr><td width=\"10%\" align=\"left\">access_ip</td><td align=\"center\">{access_ip}</td></tr>\n   </tbody>\n  </table>\n </body>\n</html>\n\"\"\"\n\nSubject_template=\"{0} pull <<{1}>> alarm!\"\n\n# if hostname is not ftptransfer,neet change the hard-coding blow\nHOST_NAME=\"ftptransfer\"\nMB_UNIT=(1024*1024)\nSMTP_SERVER='10.1.2.3'\nCFG_LAST_MODIFYTIME=0\nNEWER_CFG_LAST_MODIFYTIME=0\n\n\nif __name__ == '__main__':\n    script_dir,filename    = os.path.split(os.path.realpath(sys.argv[0])) \n    pathtocfg              = script_dir + \"/\" + \"cfg.json\"\n    cfg                    = readcfg(pathtocfg)\n    CFG_LAST_MODIFYTIME    = getcfg_lastmodifytime(pathtocfg)\n\n    # avoding duplicatly read older history when restart\n    command                = \"tail -F --lines=0 /var/log/vsftpd.log\"\n    args                   = shlex.split(command)\n    child_p                = subprocess.Popen(args, shell=False,stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n\n    while child_p.poll() is None:\n        line = child_p.stdout.readline()\n\n        try:\n            line = line.decode()\n            line = line.strip()\n        except:\n            print(\"readline exception,may occur disorder code! ftp client should use utf8 charset to access...\")\n            continue\n\n        #print('Subprogram output: [{}]'.format(line))\n\n        superlog_flag  = issuper_oper(line)\n        if superlog_flag :\n            NEWER_CFG_LAST_MODIFYTIME = getcfg_lastmodifytime(pathtocfg)\n\n            if NEWER_CFG_LAST_MODIFYTIME > CFG_LAST_MODIFYTIME :\n                cfg                  = readcfg(pathtocfg)\n                CFG_LAST_MODIFYTIME  = NEWER_CFG_LAST_MODIFYTIME\n                print(\"reload config again:{}\".format(cfg))\n\n            split_pos                = line.find(HOST_NAME)\n            mail_infos['time']       = line[0:split_pos]\n\n            split_pos                = line.find(superlog_flag)\n            next_split_pos           = line.find(\"]\", split_pos)\n            ftp_operator             = line[split_pos + len(superlog_flag):next_split_pos].strip()\n            operator_details         = cfg['ftp_supers'][ftp_operator]\n\n            if operator_details is None:\n                print(\"cannot get operator's detail info,please check!\")\n                continue\n\n\n            mail_infos['operator']  = operator_details['name']\n            split_pos               = line.find(\"Client\",next_split_pos)\n            line                    = line[split_pos + len(\"Client\"):]\n            downloadinfos           = line.split(\",\")\n            mail_infos['access_ip'] = downloadinfos[0].strip().replace('\"','')\n            mail_infos['file']      = downloadinfos[1].strip().replace('\"','')\n\n            if not mail_infos['file'].startswith(operator_details['home']):\n                mail_infos['file']  = operator_details['home'] + mail_infos['file']\n\n            downloadinfos[2]        = downloadinfos[2].strip()\n            split_pos               = downloadinfos[2].find(\"bytes\")\n            float_size              = float(downloadinfos[2][0:split_pos - 1])/MB_UNIT\n\n            mail_infos['size']      = '%s , %.2f MBytes'%(downloadinfos[2],float_size)\n            #print('keylog: {}'.format(str(mail_infos)))\n            mail_content            = mail_msg_template.format(**mail_infos)\n            #print('mail content: [{}]'.format(mail_content))\n\n            sender                 = cfg['sender']\n            receiver               = \";\".join(cfg['receiver'])\n\n            # list copy,must has all rept,include Cc&Bc\n            receivers              = cfg['receiver'].copy()\n\n            try:\n                message            = MIMEText(mail_content, 'html','utf-8')\n\n                message['From']    = sender\n                message['To']      = receiver\n\n                receiver_Cc        = cfg['receiver_Cc'].copy()\n                receiver_Cc.extend(operator_details['receiver_Cc'])\n\n                if not operator_details['mail'] in receiver_Cc :\n                    receiver_Cc.append(operator_details['mail'])\n\n                receiver_Cc       = list(set(receiver_Cc))\n                receivers.extend(receiver_Cc)\n                # remove the duplicated ele\n                #print(\"original receivers:\" + str(receivers))\n                receivers         = list(set(receivers))\n\n                message['Cc']     = \";\".join(receiver_Cc)\n\n\n                Subject_header_str = Subject_template.format(operator_details['name'], mail_infos['file'])\n                #print('mail subject:{}'.format(Subject_header_str))\n\n                message['Subject'] = Header(Subject_header_str,'utf-8')\n\n                #print('before mail: {}'.format(message.as_string()))\n\n                smtpObj            = smtplib.SMTP(SMTP_SERVER)\n                smtpObj.sendmail(sender, receivers, message.as_string())\n                smtpObj.close()\n\n                print(\"send notice mail successfully...\")\n            except smtplib.SMTPException as err:\n                print(\"Error:{}\".format(err))\n        else:\n            continue\n\n\n\n\n","sub_path":"autosendmail/sendmail.py","file_name":"sendmail.py","file_ext":"py","file_size_in_byte":6400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"404131282","text":"# warzywa = ['marchew', 'kalafior', 'pietruszka']\n# for warzywo in warzywa:\n#     print(warzywo)\n\nliczby = [-3, -2, 1, -4, 6, 8, 9]\ndodatnie = 0\nujemne = 0\n\nfor liczba in liczby:\n    if liczba < 0:\n        ujemne += 1\n    else:\n        dodatnie += 1\n\nprint(f'Ilosc liczby dodatnich = {dodatnie}, a ujemnych = {ujemne}')\n\n","sub_path":"2.3.py","file_name":"2.3.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"620801832","text":"import numpy as np\r\nimport pandas as pd\r\nfrom PIL import Image\r\n\r\n# visualize images from imgArray\r\n# imgArray : contains image array with value 0.0 (black) ~ 1.0 (white)\r\n# saveName : name of the image file\r\n# show     : whether to show the image\r\ndef visualizeFromImgArray(imgArray, imgLabels, imgIndex, show, optionalSaveName):\r\n\r\n    width = 64\r\n    height = 64\r\n\r\n    # for each image\r\n    if show == True:\r\n        print('showing image ' + str(imgIndex) + '...')\r\n    else:\r\n        print('image ' + str(imgIndex) + '...')\r\n\r\n    img = imgArray[imgIndex]\r\n\r\n    # convert into [height][width][3] = [64][64][3] image\r\n    converted = []\r\n    for i in range(height):\r\n            \r\n        row = [] # each row of the image\r\n        for j in range(width):\r\n\r\n            pixel = [] # each pixel of the image\r\n            for k in range(3): pixel.append(int(float(img[i * 64 + j]) * 255))\r\n            row.append(pixel)\r\n\r\n        converted.append(row)\r\n\r\n    # visualize image\r\n    convertedImg = Image.fromarray(np.array(converted).astype('uint8'), 'RGB')\r\n    if show == True: convertedImg.show()\r\n\r\n    # save image\r\n    if optionalSaveName == None:\r\n        saveName = 'signTest_' + str(imgIndex) + '_' + str(imgLabels[imgIndex][0]) + '.png'\r\n    else:\r\n        saveName = optionalSaveName + '.png'\r\n        \r\n    convertedImg.save(saveName)\r\n    \r\nif __name__ == '__main__':\r\n\r\n    # save grayscaled every 50th test images\r\n    imgArray = np.round_(np.array(pd.read_csv('test.csv', index_col=0)) / 255.0, 3)\r\n    imgLabels = np.array(pd.read_csv('testLabels.csv', index_col=0))\r\n    imgs = len(imgArray)\r\n\r\n    for i in range(imgs // 10):\r\n        visualizeFromImgArray(imgArray, imgLabels, i*10, False, None)\r\n","sub_path":"AI/CAR_test_202102/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"245089877","text":"#\n# Trabalho Final\n# \n# Aluno      : Mário Júnior de Sales Pimentel - 11218796\n# Disciplina : Analise e Projeto de Algoritmos - 2017.1\n# Professor  : GILBERTO FARIAS DE SOUSA FILHO \n#\n#\n# uso : \n#       utilizando pipe de arquivo:\n#           > python3 app.py 100 < nome_do_arquivo.txt\n#           \n#           OBS 100 = numero de iteracoes do grasp\n#           \nfrom City import City\nfrom TSP import TSP\n\nimport random\nimport time\nimport itertools\nimport urllib\nimport csv\nimport functools\nimport pprint \nimport sys\nimport timeit\n\ndef main(argv):\n\t\t\n\tnumber_exec = int(argv[1])\n\tfilename = \"\"\n\n\tcities = []\n\n\twhile True:\n\t\ttry:\n\t\t\tline = input().strip()\n\n\t\t\tif \"NAME\" in line:\n\t\t\t\tfilename = line\n\n\t\t\telif line == \"NODE_COORD_SECTION\":\n\t\t\t   \n\t\t\t\twhile True:\n\t\t\t\t\ttry:\n\t\t\t\t\t\t\n\t\t\t\t\t\tline = input().split()\n\t\t\t\t\t\tcity = City(line[0], line[1], line[2])\n\t\t\t\t\t\tcities.append(city)\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\t#print (\"1 \" + str(e) + \" city :\" + str(city.id))\n\t\t\t\t\t\tbreak;\n\n\t\texcept Exception as e:\n\t\t\t#print (\"2 \" + str(e) + \" city :\" + str(city.id))\n\t\t\tbreak;\n\t\n\ttsp = TSP()\n\tM = tsp.get_distance_matrix(cities)\n\t\n\t#tsp.pretty_print(M)\n\t#exit()\n\n\t#print (\"\\nCusto NN : \" + str(custo_nn))\n\t#tsp.print_tour_simple(nn_tour)\n\n\tprint (\"Executing \" + str(number_exec) + \" times on : \" + str(filename) )\n\n\tstart_time = timeit.default_timer()\n\n\tminimo = float(\"inf\")\n\tfor i in range(number_exec):\n\t\t#alpha = random.uniform(0, 1)\n\t\talpha = 0.5\n\t\tgrasp_tour = tsp.GRASP_TSP(cities, M, alpha)\n\t\tcusto_final = tsp.get_cost(grasp_tour, M)\n\n\t\tif custo_final < minimo:\n\t\t\tprint (\"\\nAlpha : \" + str(alpha) + \" Custo GRASP: \" + str(custo_final))\n\t\t\ttsp.plot_cities(grasp_tour)\n\t\t\tminimo = custo_final\n\t\n\telapsed = timeit.default_timer() - start_time\n\n\tprint (\"Elapsed time : \" + str(elapsed) + \" s\")\n\tprint (\"---------------------------------------------\\n\\n\")\n\nif __name__ == \"__main__\":\n  \tmain(sys.argv)\n\n\n\n","sub_path":"Heuristica e Meta-Heuristica para o PCV/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"495117058","text":"import requests\r\nimport pandas as pd\r\nimport time\r\nimport datetime\r\n\r\nfrom bs4 import BeautifulSoup\r\n\r\ndef i2str(i):\r\n    i2=str(i)\r\n    if len(i2)<2:\r\n        i2=\"0\"+i2\r\n    return(i2)\r\n\r\n\r\ndef readconfig():\r\n    f=open(\"./messer.ini\")\r\n    config = {}\r\n\r\n    for fs in f:\r\n        fs=fs.strip()\r\n        print(fs)\r\n        confline = fs.split(\"=\")\r\n        config[confline[0]] = confline[1]\r\n\r\n    f.close()\r\n    print(config)\r\n    return(config)\r\n\r\nconfline=readconfig()\r\n\r\n\r\nif \"path\" in confline:\r\n    path=confline[\"path\"]\r\nif \"prefix\" in confline:\r\n    prefix=confline[\"prefix\"]\r\n\r\n\r\nprint(path+prefix)\r\n\r\n\r\n\r\ndt=datetime.date.today()\r\nt1=dt\r\ndt=datetime.timedelta(days=1)\r\nt2=t1-dt\r\n\r\nprint(t1)\r\nprint(t2)\r\ny1=i2str(t1.year)\r\nm1=i2str(t1.month)\r\nd1=i2str(t1.day)\r\n\r\n\r\ny2=i2str(t2.year)\r\nm2=i2str(t2.month)\r\nd2=i2str(t2.day)\r\n\r\n\r\nprint(y1,m1,d1)\r\nprint(y2,m2,d2)\r\n\r\n\r\n\r\nprint(dt)\r\n\r\n\r\nurl1=\"http://telemetry.messer.hu:8080/cgi-bin/rview.exe?Log=..%5C..%5C..%5Cppsrv%5CRichter1.txt&Html=Richter1log&MaxRows=\"\r\nurl2=\"10000&Year1=\"+y2+\"&Mon1=\"+m2+\"&Day1=\"+d2+\"&Hour1=0&Year2=\"+y1+\"&Mon2=\"+m1+\"&Day2=\"+d1+\"&Hour2=0\"\r\n\r\n\r\nurl=url1+url2\r\n\r\n\r\nfname=path+prefix+y2+m2+d2+\".csv\"\r\n\r\n\r\nprint(url)\r\nresult=requests.get(url)\r\nprint(result.content)\r\nsoup = BeautifulSoup(result.content, 'html.parser')\r\nprint(\"----------------\")\r\n#print(soup.prettify)\r\n#print(list(soup.children))\r\nallrow=soup.find_all(\"tr\")[:]\r\nfo  = open(fname, \"w\",encoding='utf-8')\r\nfor  row in allrow:\r\n    #print(row)\r\n    soup2=BeautifulSoup(result.content, 'html.parser')\r\n    allcell=row.find_all(\"td\")[:]\r\n    sumcell=\"\"\r\n    #print(\"--------------\")\r\n    #print(allcell)\r\n    for cell in allcell:\r\n        celltxt=cell.text\r\n        sumcell=sumcell+celltxt+\";\"\r\n\r\n    outstr=sumcell.replace(\".\",\",\").replace(\";\",\" \",1).replace(\",\",\".\",2).replace(\"   \",\"\").replace(\"  \",\"\")\r\n    print(outstr)\r\n\r\n    fo.write(outstr+\"\\n\")\r\n\r\n# Close opend file\r\nfo.close()\r\n\r\n","sub_path":"messer_orig.py","file_name":"messer_orig.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"620878304","text":"import os\r\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0,-1\"\r\nimport numpy as np\r\nimport tensorflow as tf\r\n\r\n#from tensorflow.python.client import device_lib\r\n#print(device_lib.list_local_devices())\r\n\r\nimport shutil, sys\r\nfrom datetime import datetime\r\nimport h5py\r\n\r\nfrom cnn_sleep_config import Config\r\nfrom cnn_sleep import CNN_Sleep\r\nfrom datagenerator_from_list_v2 import DataGenerator\r\nfrom equaldatagenerator_from_list_v2 import EqualDataGenerator\r\nfrom datagenerator_nchannel import DataGeneratorNChannel\r\nfrom equaldatagenerator_nchannel import EqualDataGeneratorNChannel\r\n\r\nfrom sklearn.metrics import f1_score\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.metrics import cohen_kappa_score\r\n\r\nfrom scipy.io import loadmat,savemat\r\n\r\n\r\n# Misc Parameters\r\ntf.app.flags.DEFINE_boolean(\"allow_soft_placement\", True, \"Allow device soft device placement\")\r\ntf.app.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\r\n\r\n# My Parameters\r\ntf.app.flags.DEFINE_string(\"eeg_train_data\", \"../train_data.mat\", \"Point to directory of input data\")\r\ntf.app.flags.DEFINE_string(\"eeg_test_data\", \"../test_data.mat\", \"Point to directory of input data\")\r\ntf.app.flags.DEFINE_string(\"eog_train_data\", \"../train_data.mat\", \"Point to directory of input data\")\r\ntf.app.flags.DEFINE_string(\"eog_test_data\", \"../test_data.mat\", \"Point to directory of input data\")\r\ntf.app.flags.DEFINE_string(\"emg_train_data\", \"../train_data.mat\", \"Point to directory of input data\")\r\ntf.app.flags.DEFINE_string(\"emg_test_data\", \"../test_data.mat\", \"Point to directory of input data\")\r\ntf.app.flags.DEFINE_string(\"out_dir\", \"./output/\", \"Point to output directory\")\r\ntf.app.flags.DEFINE_string(\"checkpoint_dir\", \"./checkpoint/\", \"Point to checkpoint directory\")\r\n\r\ntf.app.flags.DEFINE_float(\"dropout_keep_prob\", 0.8, \"Dropout keep probability (default: 0.8)\")\r\ntf.app.flags.DEFINE_float(\"dropout_keep_prob_conv\", 0.8, \"Convolutional dropout keep probability (default: 0.8)\")\r\n\r\ntf.app.flags.DEFINE_string(\"eeg_pretrainedfb_path\", \"./output/filterbank.mat\", \"Point to the pretrainedfb mat file\")\r\ntf.app.flags.DEFINE_string(\"eog_pretrainedfb_path\", \"./output/filterbank.mat\", \"Point to the pretrainedfb mat file\")\r\ntf.app.flags.DEFINE_string(\"emg_pretrainedfb_path\", \"./output/filterbank.mat\", \"Point to the pretrainedfb mat file\")\r\n\r\n\r\nFLAGS = tf.app.flags.FLAGS\r\nprint(\"\\nParameters:\")\r\nfor attr, value in sorted(FLAGS.__flags.items()): # python3\r\n    print(\"{}={}\".format(attr.upper(), value))\r\nprint(\"\")\r\n\r\nout_path = os.path.abspath(os.path.join(os.path.curdir,FLAGS.out_dir))\r\ncheckpoint_path = os.path.abspath(os.path.join(out_path,FLAGS.checkpoint_dir))\r\n\r\nconfig = Config()\r\nconfig.dropout_keep_prob = FLAGS.dropout_keep_prob\r\nconfig.dropout_keep_prob_conv = FLAGS.dropout_keep_prob_conv\r\n\r\neeg_active = ((FLAGS.eeg_train_data != \"\") & (FLAGS.eeg_test_data != \"\"))\r\neog_active = ((FLAGS.eog_train_data != \"\") & (FLAGS.eog_test_data != \"\"))\r\nemg_active = ((FLAGS.emg_train_data != \"\") & (FLAGS.emg_test_data != \"\"))\r\n\r\nnum_channel = 0\r\n\r\nif (eeg_active):\r\n    print(\"eeg active\")\r\n    num_channel += 1\r\n\r\n    # Initalize the data generator seperately for the training, validation, and test sets\r\n    eeg_train_gen = EqualDataGenerator(os.path.abspath(FLAGS.eeg_train_data), data_shape=[config.n_time, config.n_dim], shuffle = False)\r\n    eeg_test_gen = DataGenerator(os.path.abspath(FLAGS.eeg_test_data), data_shape=[config.n_time, config.n_dim], shuffle = False, test_mode=True)\r\n    # load pretrained filterbanks and do filtering first\r\n    eeg_filter = loadmat(FLAGS.eeg_pretrainedfb_path)\r\n    Wfb = eeg_filter['Wfb']\r\n    eeg_train_gen.filter_with_filterbank(Wfb)\r\n    eeg_test_gen.filter_with_filterbank(Wfb)\r\n    del Wfb, eeg_filter\r\n\r\n    # normalization here\r\n    X = eeg_train_gen.X\r\n    X = np.reshape(X,(eeg_train_gen.data_size*eeg_train_gen.data_shape[0], eeg_train_gen.data_shape[1]))\r\n    meanX = X.mean(axis=0)\r\n    stdX = X.std(axis=0)\r\n\r\n    X = eeg_test_gen.X\r\n    X = np.reshape(X,(eeg_test_gen.data_size*eeg_test_gen.data_shape[0], eeg_test_gen.data_shape[1]))\r\n    X = (X - meanX) / stdX\r\n    eeg_test_gen.X = np.reshape(X, (eeg_test_gen.data_size, eeg_test_gen.data_shape[0], eeg_test_gen.data_shape[1]))\r\n    del X, eeg_train_gen\r\n\r\n\r\nif (eog_active):\r\n    print(\"eog active\")\r\n    num_channel += 1\r\n\r\n    # Initalize the data generator seperately for the training, validation, and test sets\r\n    eog_train_gen = EqualDataGenerator(os.path.abspath(FLAGS.eog_train_data), data_shape=[config.n_time, config.n_dim], shuffle = False)\r\n    eog_test_gen = DataGenerator(os.path.abspath(FLAGS.eog_test_data), data_shape=[config.n_time, config.n_dim], shuffle = False, test_mode=True)\r\n    # load pretrained filterbanks and do filtering first\r\n    eog_filter = loadmat(FLAGS.eog_pretrainedfb_path)\r\n    Wfb = eog_filter['Wfb']\r\n    eog_train_gen.filter_with_filterbank(Wfb)\r\n    eog_test_gen.filter_with_filterbank(Wfb)\r\n    del Wfb, eog_filter\r\n\r\n    # normalization here\r\n    X = eog_train_gen.X\r\n    X = np.reshape(X,(eog_train_gen.data_size*eog_train_gen.data_shape[0], eog_train_gen.data_shape[1]))\r\n    meanX = X.mean(axis=0)\r\n    stdX = X.std(axis=0)\r\n\r\n    X = eog_test_gen.X\r\n    X = np.reshape(X,(eog_test_gen.data_size*eog_test_gen.data_shape[0], eog_test_gen.data_shape[1]))\r\n    X = (X - meanX) / stdX\r\n    eog_test_gen.X = np.reshape(X, (eog_test_gen.data_size, eog_test_gen.data_shape[0], eog_test_gen.data_shape[1]))\r\n    del X, eog_train_gen\r\n\r\nif (emg_active):\r\n    print(\"emg active\")\r\n    num_channel += 1\r\n\r\n    # Initalize the data generator seperately for the training, validation, and test sets\r\n    emg_train_gen = EqualDataGenerator(os.path.abspath(FLAGS.emg_train_data), data_shape=[config.n_time, config.n_dim], shuffle = False)\r\n    emg_test_gen = DataGenerator(os.path.abspath(FLAGS.emg_test_data), data_shape=[config.n_time, config.n_dim], shuffle = False, test_mode=True)\r\n    # load pretrained filterbanks and do filtering first\r\n    emg_filter = loadmat(FLAGS.emg_pretrainedfb_path)\r\n    Wfb = emg_filter['Wfb']\r\n    emg_train_gen.filter_with_filterbank(Wfb)\r\n    emg_test_gen.filter_with_filterbank(Wfb)\r\n    del Wfb, emg_filter\r\n\r\n    # normalization here\r\n    X = emg_train_gen.X\r\n    X = np.reshape(X,(emg_train_gen.data_size*emg_train_gen.data_shape[0], emg_train_gen.data_shape[1]))\r\n    meanX = X.mean(axis=0)\r\n    stdX = X.std(axis=0)\r\n\r\n    X = emg_test_gen.X\r\n    X = np.reshape(X,(emg_test_gen.data_size*emg_test_gen.data_shape[0], emg_test_gen.data_shape[1]))\r\n    X = (X - meanX) / stdX\r\n    emg_test_gen.X = np.reshape(X, (emg_test_gen.data_size, emg_test_gen.data_shape[0], emg_test_gen.data_shape[1]))\r\n    del X, emg_train_gen\r\n\r\nconfig.n_dim = eeg_test_gen.data_shape[1] # update frequency dimension\r\nconfig.n_channel = num_channel # update number of channels\r\n\r\ntest_generator = DataGeneratorNChannel(data_shape=[config.n_time, config.n_dim, num_channel], shuffle = False, test_mode=True)\r\n\r\n# expanding channel dimension and concatenation\r\nif(num_channel == 1):\r\n    test_generator.X = np.expand_dims(eeg_test_gen.X,axis=3)\r\nelif(num_channel == 2):\r\n    test_generator.X = np.concatenate((np.expand_dims(eeg_test_gen.X,axis=3),\r\n                                        np.expand_dims(eog_test_gen.X,axis=3)), axis=-1)\r\nelse: # num_channel == 3\r\n    test_generator.X = np.concatenate((np.expand_dims(eeg_test_gen.X,axis=3),\r\n                                        np.expand_dims(eog_test_gen.X,axis=3),\r\n                                        np.expand_dims(emg_test_gen.X,axis=3)), axis=-1)\r\ntest_generator.y = eeg_test_gen.y\r\ntest_generator.label = eeg_test_gen.label\r\ntest_generator.boundary_index = eeg_test_gen.boundary_index\r\n\r\n# clear individual data\r\ndel eeg_test_gen\r\nif (eog_active):\r\n    del eog_test_gen\r\nif (emg_active):\r\n    del emg_test_gen\r\n\r\n# generate index here\r\ntest_generator.indexing()\r\n\r\ntest_batches_per_epoch = np.floor(len(test_generator.data_index) / config.batch_size).astype(np.int16)\r\n\r\n\r\nwith tf.Graph().as_default():\r\n    session_conf = tf.ConfigProto(\r\n      allow_soft_placement=FLAGS.allow_soft_placement,\r\n      log_device_placement=FLAGS.log_device_placement)\r\n    sess = tf.Session(config=session_conf)\r\n    with sess.as_default():\r\n        cnn = CNN_Sleep(config=config)\r\n\r\n        # Define Training procedure\r\n        global_step = tf.Variable(0, name=\"global_step\", trainable=False)\r\n        optimizer = tf.train.AdamOptimizer(config.learning_rate)\r\n        grads_and_vars = optimizer.compute_gradients(cnn.loss)\r\n        train_op = optimizer.apply_gradients(grads_and_vars, global_step=global_step)\r\n\r\n        def dev_step(x_batch, y_batch1, y_batch2, y_batch3):\r\n            feed_dict = {\r\n                cnn.X: x_batch,\r\n                cnn.Y1: y_batch1,\r\n                cnn.Y2: y_batch2,\r\n                cnn.Y3: y_batch3,\r\n                cnn.dropout_keep_prob: 1.0,\r\n                cnn.dropout_keep_prob_conv: 1.0\r\n            }\r\n            _, loss, yhat1, score1, acc1, \\\r\n                    yhat2, score2, acc2, \\\r\n                    yhat3, score3, acc3 = sess.run(\r\n                [global_step, cnn.loss, cnn.pred_Y1, cnn.score1, cnn.accuracy1,\r\n                 cnn.pred_Y2, cnn.score2, cnn.accuracy2,\r\n                 cnn.pred_Y3, cnn.score3, cnn.accuracy3],\r\n                feed_dict)\r\n            return acc1, acc2, acc3, yhat1, yhat2, yhat3, score1, score2, score3\r\n\r\n        saver = tf.train.Saver(tf.all_variables())\r\n\r\n        # Load saved model to continue training or initialize all variables\r\n        best_dir = os.path.join(checkpoint_path, \"best_model_acc\")\r\n        saver.restore(sess, best_dir)\r\n        print(\"Model loaded\")\r\n\r\n        test_yhat1 = np.zeros_like(test_generator.data_index)\r\n        score1 = np.zeros([len(test_generator.data_index),config.n_class])\r\n        test_yhat2 = np.zeros_like(test_generator.data_index)\r\n        score2 = np.zeros([len(test_generator.data_index),config.n_class])\r\n        test_yhat3 = np.zeros_like(test_generator.data_index)\r\n        score3 = np.zeros([len(test_generator.data_index),config.n_class])\r\n        test_step = 1\r\n        while test_step < test_batches_per_epoch:\r\n            x_batch, y_batch1, label_batch1_,\\\r\n                y_batch2, label_batch2_,\\\r\n                y_batch3, label_batch3_ = test_generator.next_batch(config.batch_size)\r\n            _, _, _, test_yhat1_, test_yhat2_, test_yhat3_,\\\r\n                        score1_, score2_, score3_ = dev_step(x_batch, y_batch1, y_batch2, y_batch3)\r\n            test_yhat1[(test_step-1)*config.batch_size : test_step*config.batch_size] = test_yhat1_\r\n            score1[(test_step-1)*config.batch_size : test_step*config.batch_size,:] = score1_\r\n            test_yhat2[(test_step-1)*config.batch_size : test_step*config.batch_size] = test_yhat2_\r\n            score2[(test_step-1)*config.batch_size : test_step*config.batch_size,:] = score2_\r\n            test_yhat3[(test_step-1)*config.batch_size : test_step*config.batch_size] = test_yhat3_\r\n            score3[(test_step-1)*config.batch_size : test_step*config.batch_size,:] = score3_\r\n            test_step += 1\r\n        if(test_generator.pointer < len(test_generator.data_index)):\r\n            actual_len, x_batch, y_batch1, label_batch1_,\\\r\n                y_batch2, label_batch2_,\\\r\n                y_batch3, label_batch3_ = test_generator.rest_batch(config.batch_size)\r\n            _, _, _, test_yhat1_, test_yhat2_, test_yhat3_,\\\r\n                        score1_, score2_, score3_ = dev_step(x_batch, y_batch1, y_batch2, y_batch3)\r\n            test_yhat1[(test_step-1)*config.batch_size : len(test_generator.data_index)] = test_yhat1_\r\n            score1[(test_step-1)*config.batch_size : len(test_generator.data_index),:] = score1_\r\n            test_yhat2[(test_step-1)*config.batch_size : len(test_generator.data_index)] = test_yhat2_\r\n            score2[(test_step-1)*config.batch_size : len(test_generator.data_index),:] = score2_\r\n            test_yhat3[(test_step-1)*config.batch_size : len(test_generator.data_index)] = test_yhat3_\r\n            score3[(test_step-1)*config.batch_size : len(test_generator.data_index),:] = score3_\r\n        test_yhat1 = test_yhat1 + 1\r\n        test_yhat2 = test_yhat2 + 1\r\n        test_yhat3 = test_yhat3 + 1\r\n        test_fscore1 = f1_score(test_generator.label[:-1], test_yhat1[1:], average='macro')\r\n        test_acc1 = accuracy_score(test_generator.label[:-1], test_yhat1[1:])\r\n        test_kappa1 = cohen_kappa_score(test_generator.label[:-1], test_yhat1[1:])\r\n        test_fscore2 = f1_score(test_generator.label, test_yhat2, average='macro')\r\n        test_acc2 = accuracy_score(test_generator.label, test_yhat2)\r\n        test_kappa2 = cohen_kappa_score(test_generator.label, test_yhat2)\r\n        test_fscore3 = f1_score(test_generator.label[1:], test_yhat3[:-1], average='macro')\r\n        test_acc3 = accuracy_score(test_generator.label[1:], test_yhat3[:-1])\r\n        test_kappa3 = cohen_kappa_score(test_generator.label[1:], test_yhat3[:-1])\r\n        savemat(os.path.join(out_path, \"test_ret_model_acc.mat\"), dict(acc1=test_acc1,\r\n                                                                       fscore1=test_fscore1,\r\n                                                                       kappa1=test_kappa1,\r\n                                                                       acc2=test_acc2,\r\n                                                                       fscore2=test_fscore2,\r\n                                                                       kappa2=test_kappa2,\r\n                                                                       acc3=test_acc3,\r\n                                                                       fscore3=test_fscore3,\r\n                                                                       kappa3=test_kappa3,\r\n                                                                       yhat1=test_yhat1,\r\n                                                                       yhat2=test_yhat2,\r\n                                                                       yhat3=test_yhat3,\r\n                                                                       score1 = score1,\r\n                                                                       score2 = score2,\r\n                                                                       score3 = score3))\r\n        test_generator.reset_pointer()\r\n","sub_path":"SleepEDF/tensorflow_net/deep_cnn_baseline_1to3/test_cnn_sleep_gpu0.py","file_name":"test_cnn_sleep_gpu0.py","file_ext":"py","file_size_in_byte":14417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"573535028","text":"import os\nimport unittest\nimport sys as _sys\nif _sys.version_info[0] == 3:\n    from tempfile import TemporaryDirectory\nelse:\n    from tempfile import mkdtemp\n    import warnings as _warnings\n\n    class TemporaryDirectory(object):\n        \"\"\"Create and return a temporary directory.  This has the same\n        behavior as mkdtemp but can be used as a context manager.  For\n        example:\n\n            with TemporaryDirectory() as tmpdir:\n                ...\n\n        Upon exiting the context, the directory and everything contained\n        in it are removed.\n        \"\"\"\n\n        def __init__(self, suffix=\"\", prefix=\"tmp\", dir=None):\n            self._closed = False\n            self.name = None # Handle mkdtemp raising an exception\n            self.name = mkdtemp(suffix, prefix, dir)\n\n        def __repr__(self):\n            return \"<{} {!r}>\".format(self.__class__.__name__, self.name)\n\n        def __enter__(self):\n            return self.name\n\n        def cleanup(self, _warn=False):\n            if self.name and not self._closed:\n                try:\n                    self._rmtree(self.name)\n                except (TypeError, AttributeError) as ex:\n                    # Issue #10188: Emit a warning on stderr\n                    # if the directory could not be cleaned\n                    # up due to missing globals\n                    if \"None\" not in str(ex):\n                        raise\n                    return\n                self._closed = True\n                if _warn:\n                    self._warn(\"Implicitly cleaning up {!r}\".format(self),\n                            ResourceWarning)\n\n        def __exit__(self, exc, value, tb):\n            self.cleanup()\n\n        def __del__(self):\n            # Issue a ResourceWarning if implicit cleanup needed\n            self.cleanup(_warn=True)\n\n        # XXX (ncoghlan): The following code attempts to make\n        # this class tolerant of the module nulling out process\n        # that happens during CPython interpreter shutdown\n        # Alas, it doesn't actually manage it. See issue #10188\n        _listdir = staticmethod(os.listdir)\n        _path_join = staticmethod(os.path.join)\n        _isdir = staticmethod(os.path.isdir)\n        _islink = staticmethod(os.path.islink)\n        _remove = staticmethod(os.remove)\n        _rmdir = staticmethod(os.rmdir)\n        _warn = _warnings.warn\n\n        def _rmtree(self, path):\n            # Essentially a stripped down version of shutil.rmtree.  We can't\n            # use globals because they may be None'ed out at shutdown.\n            for name in self._listdir(path):\n                fullname = self._path_join(path, name)\n                try:\n                    isdir = self._isdir(fullname) and not self._islink(fullname)\n                except OSError:\n                    isdir = False\n                if isdir:\n                    self._rmtree(fullname)\n                else:\n                    try:\n                        self._remove(fullname)\n                    except OSError:\n                        pass\n            try:\n                self._rmdir(path)\n            except OSError:\n                pass\nimport numpy as np\nfrom PIL import Image, ImageDraw\n\nimport webp\n\n\nclass TestWebP(unittest.TestCase):\n    def test_WebPConfig(self):\n        config = webp.WebPConfig.new(webp.WebPPreset.DRAWING, 50)\n        del config\n\n    def test_WebPPicture(self):\n        pic = webp.WebPPicture.new(32, 32)\n        del pic\n\n        img = Image.new('RGB', (32, 16))\n        pic = webp.WebPPicture.from_pil(img)\n        del pic\n\n    def test_image(self):\n        img = Image.new('RGB', (32, 16))\n        draw = ImageDraw.Draw(img)\n        draw.rectangle([0, 0, 7, 15], fill=(255, 0, 0))\n\n        pic = webp.WebPPicture.from_pil(img)\n        config = webp.WebPConfig.new(lossless=True)\n        buf = pic.encode(config).buffer()\n\n        with TemporaryDirectory() as tmpdir:\n            file_name = os.path.join(tmpdir, 'image.webp')\n            with open(file_name, 'wb') as f:\n                f.write(buf)\n\n            with open(file_name, 'rb') as f:\n                webp_data = webp.WebPData.from_buffer(f.read())\n                arr = webp_data.decode(color_mode=webp.WebPColorMode.RGB)\n\n                expected = np.asarray(img, dtype=np.uint8)\n                np.testing.assert_array_equal(arr, expected)\n\n    def test_anim(self):\n        imgs = []\n        width = 256\n        height = 64\n        for i in range(4):\n            img = Image.new('RGBA', (width, height))\n            draw = ImageDraw.Draw(img)\n            x = i * (width/4)\n            draw.rectangle([x, 0, x + (width/4-1), height-1], fill=(255, 0, 0))\n            imgs.append(img)\n\n        webp_pics = [webp.WebPPicture.from_pil(img) for img in imgs]\n\n        enc_opts = webp.WebPAnimEncoderOptions.new()\n        enc = webp.WebPAnimEncoder.new(width, height, enc_opts)\n        t = 0\n        config = webp.WebPConfig.new(lossless=True)\n        for webp_pic in webp_pics:\n            enc.encode_frame(webp_pic, t, config)\n            t += 250\n        anim_data = enc.assemble(t)\n\n        with TemporaryDirectory() as tmpdir:\n            file_name = os.path.join(tmpdir, 'anim.webp')\n\n            with open(file_name, 'wb') as f:\n                f.write(anim_data.buffer())\n\n            with open(file_name, 'rb') as f:\n                webp_data = webp.WebPData.from_buffer(f.read())\n                dec_opts = webp.WebPAnimDecoderOptions.new()\n                dec = webp.WebPAnimDecoder.new(webp_data, dec_opts)\n                self.assertEqual(dec.anim_info.frame_count, 4)\n                for i, (arr, t) in enumerate(dec.frames()):\n                    expected = np.asarray(imgs[i], dtype=np.uint8)\n                    np.testing.assert_array_equal(arr, expected)\n\n    def test_default_enc_opts(self):\n        enc = webp.WebPAnimEncoder.new(64, 64)\n        self.assertFalse(enc.enc_opts.minimize_size)\n        self.assertFalse(enc.enc_opts.allow_mixed)\n\n    def test_anim_simple(self):\n        imgs = []\n        width = 256\n        height = 64\n        for i in range(4):\n            img = Image.new('RGBA', (width, height))\n            draw = ImageDraw.Draw(img)\n            draw.rectangle([0, 0, width-1, height-1], fill=(0, 0, 255))\n            x = i * (width/4)\n            draw.rectangle([x, 0, x + (width/4-1), height-1], fill=(255, 0, 0))\n            imgs.append(img)\n\n        with TemporaryDirectory() as tmpdir:\n            file_name = os.path.join(tmpdir, 'anim.webp')\n\n            webp.save_images(imgs, file_name, fps=4, lossless=True)\n            dec_imgs = webp.load_images(file_name, 'RGBA')\n\n            self.assertEqual(len(dec_imgs), 4)\n            for dec_img, img in zip(dec_imgs, imgs):\n                actual = np.asarray(dec_img, dtype=np.uint8)\n                expected = np.asarray(img, dtype=np.uint8)\n                np.testing.assert_array_equal(actual, expected)\n\n    # WebP combines adjacent duplicate frames and adjusts timestamps\n    # accordingly, resulting in unevenly spaced frames. By specifying the fps\n    # while loading we can return evenly spaced frames.\n    def test_anim_simple_resample(self):\n        width = 256\n        height = 64\n        img1 = Image.new('RGB', (width, height))\n        draw = ImageDraw.Draw(img1)\n        draw.rectangle([0, 0, width-1, height-1], fill=(0, 0, 255))\n        draw.rectangle([0, 0, (width/4-1), height-1], fill=(255, 0, 0))\n        img2 = Image.new('RGB', (width, height))\n        draw = ImageDraw.Draw(img2)\n        draw.rectangle([0, 0, width-1, height-1], fill=(0, 0, 255))\n        draw.rectangle([0, 0, (width/4-1), height-1], fill=(0, 255, 0))\n\n        imgs = [img1, img1, img2, img2]\n\n        with TemporaryDirectory() as tmpdir:\n            file_name = os.path.join(tmpdir, 'anim.webp')\n\n            webp.save_images(imgs, file_name, fps=4, lossless=True)\n            dec_imgs = webp.load_images(file_name, 'RGBA', fps=4)\n\n            self.assertEqual(len(dec_imgs), 4)\n\n    def test_image_simple(self):\n        width = 256\n        height = 64\n        img = Image.new('RGB', (width, height))\n        draw = ImageDraw.Draw(img)\n        draw.rectangle([0, 0, width-1, height-1], fill=(0, 0, 255))\n        draw.rectangle([0, 0, (width/4-1), height-1], fill=(255, 0, 0))\n\n        with TemporaryDirectory() as tmpdir:\n            file_name = os.path.join(tmpdir, 'image.webp')\n\n            webp.save_image(img, file_name, lossless=True)\n            dec_img = webp.load_image(file_name, 'RGB')\n\n            actual = np.asarray(dec_img, dtype=np.uint8)\n            expected = np.asarray(img, dtype=np.uint8)\n            np.testing.assert_array_equal(actual, expected)\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"tests/test_webp.py","file_name":"test_webp.py","file_ext":"py","file_size_in_byte":8695,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"263112419","text":"#coding: utf-8\n\nimport urllib\nimport urllib2\nimport sys\n\nurl = \"https://slack.com/api/chat.postMessage\"\n\nparams = {'token':'xoxp-15751162311-15745376292-35448987459-53e17d59e7',   # トークン\n          'channel':'C0FT2FNUE', # チャンネルID\n          'text': ''    # 送信するテキスト\n}\n\nparams['text'] = '僕が' + sys.argv[2] +  'ファイルは' + sys.argv[1] + 'だ！！'\nparams = urllib.urlencode(params)\n\nreq = urllib2.Request(url)\n# ヘッダ追加\nreq.add_header('Content-Type', 'application/x-www-form-urlencoded')\n# パラメータ追加\nreq.add_data(params)\n\nres = urllib2.urlopen(req)\n\n# レスポンス取得\nbody = res.read()\n","sub_path":"slack_pythons/slack_test_post.py","file_name":"slack_test_post.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"507180740","text":"#-------------------------------------------------------------------------------\n# Name:        module1\n# Purpose:\n#\n# Author:      lvanw\n#\n# Created:     23-01-2019\n# Copyright:   (c) lvanw 2019\n# Licence:     <your licence>\n#-------------------------------------------------------------------------------\n\nimport requests\nimport json\nimport csv\nimport geojson\nimport geopandas\nfrom shapely.geometry import shape\nimport pandas as pd\nfrom geopandas import GeoDataFrame\nfrom shapely.geometry import Polygon, Point\nfrom pandas.io.json import json_normalize\n\n\nbaseUrl = 'https://api.schiphol.nl/wayfinding'\nheaderData   = {'app_id': 'a81ebb80', 'app_key': '28ebc893c62b69b9b8c75fa6ce972f63', 'ResourceVersion': 'v3'}\nfromLocation = 'mt Lincolnweg,amsterdam'\nflightName   = 'KL2717'\n\nresponse = requests.get(\n\t'%s/plan/from/%s/profile/car/flight/%s' % (\n        baseUrl,\n    \tfromLocation,\n\t\tflightName\n\t),\n\theaders = headerData\n).json()\n\nformat = '* #%s : %s - %s,%s'\nfor num, stop in enumerate(response['stops']):\n\tprint(\n\t\tformat % (\n\t\t\tnum,\n\t\t\tstop['name'],\n\t\t\tstop['latLng']['lat'],\n\t\t\tstop['latLng']['lng']\n\t\t)\n\t)\n\ngate = response['stops'][-1]\nresponse = requests.get(\n\t'%s/position/%s,%s,%s/distance/500/coffee' % (\n\t\tbaseUrl,\n\t\tgate['latLng']['lat'],\n\t\tgate['latLng']['lng'],\n\t\tgate['floor']\n\t),\n\theaders = headerData\n).json()\n\ncategory = '30'\n\nresponse = requests.get(\n\t'%s/pois?category=%s' % (\n\t\tbaseUrl,\n\t\tcategory\n\t),\n\theaders = headerData\n).json()\n\npois = response['pois']\n\nprint (pois[0])\n\ndf =json_normalize(pois)\n\ndf['geometry'] = df.apply(lambda row: Point(row['position'][0],row['position'][1] )   , axis=1)\n\ndf = df.drop(['position'], axis=1)\nprint (df)\ncrs = {'init': 'epsg:4326'}\ngdf = geopandas.GeoDataFrame(df, crs=crs, geometry='geometry')\ngdf.to_file('Category30.shp', driver='ESRI Shapefile')","sub_path":"MappingFiles/category30.py","file_name":"category30.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"19192364","text":"#This code is the original work of Xavier López developed as an Udacity assignment on February 2019\n\nimport time\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n              'new york': 'new_york_city.csv',\n              'washington': 'washington.csv' }\n\n#it has been chosen to asgin monday as 0 and not in one for convinence of the datetime package function .week()\nWEEKDAY = {'monday': 0,\n           'tuesday':1,\n           'wednesday':2,\n           'thursday':3,\n           'friday':4,\n           'satuday':5,\n           'sunday':6\n          }\n\nNUM_TO_WEEKDAY = {\n            0:'Monday',\n            1:'Tuesday',\n            2:'Wednesday',\n            3:'Thursday',\n            4:'Friday',\n            5:'Satuday',\n            6:'Sunday'\n          }\n\nMONTH = {'january': 1,\n         'february':2,\n         'march':3,\n         'april':4,\n         'may':5,\n         'june':6}\n\nNUM_TO_MONTH = {\n         1:'January',\n         2:'February',\n         3:'March',\n         4:'April',\n         5:'May',\n         6:'June'}\n\ndef get_filters():\n    \"\"\"\n    Asks user to specify a city, month, and day to analyze.\n\n    Returns:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    \"\"\"\n    #initialize month and day (if time_filter = 'none' month and day are kept equal to zero)\n    month = 0\n    day = 0\n\n    print('Hello! Let\\'s explore some US bikeshare data!')\n\n    #GET CITY\n    city = input(\"\\n Would you like to see data from Chicago, New York or Washington? \\n\").lower()\n    while city not in CITY_DATA:\n        city = input('Wrong input! \\nWrite a city from the following list: Chicago, New York, Washington. \\n').lower()\n\n    #GET TYPE OF TIME FILTER\n    time_filter = input('\\n Would you like to filter data by day, month, or none at all?\\n').lower()\n    viable_input_time = ['day','month','none']\n\n    while time_filter not in viable_input_time:\n        time_filter = input('\\nWrong input! \\nWrite a filter type from the following list: day, month, none. \\n').lower()\n    #GET DAY\n    if time_filter == 'day':\n        day = input('\\n Which day? Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday. \\n').lower()\n        viable_input_day = ['monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday','sunday']\n        while day not in viable_input_day:\n            day = input('\\n Wrong input! Write a day from the following list Monday, Tuesday, Wednesday, Thursday, Friday, Saturday, Sunday\\n').lower()\n    else:\n         #GET MONTH\n        if time_filter == 'month':\n            month = input('\\n Which month? January, February, March, April, May or June ?\\n').lower()\n            viable_input_month = ['january', 'february', 'march','april','may','june']\n            while month not in viable_input_month:\n                month = input('\\n Wrong input! \\nWrite a month from the following list: January, February, March, April, May, June\\n').lower()\n\n    return city, time_filter, month, day\n\n\ndef load_data_print(start_time):\n    print(\"This took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\ndef load_data(city, time_filter, month, day):\n    \"\"\"\n    Loads data for the specified city and filters by month and day if applicable.\n\n    Args:\n        (str) city - name of the city to analyze\n        (str) month - name of the month to filter by, or \"all\" to apply no month filter\n        (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n    Returns:\n        df - Pandas DataFrame containing city data filtered by month and day\n    \"\"\"\n    print(\"\\nLoading data and preprocessing...\")\n    start_time = time.time()\n    df = pd.read_csv(CITY_DATA[city])\n\n    #Create columns for the dataframe for convinience in calculations\n    df['start_year']  = [int(value[0:4])   for value in df[\"Start Time\"]]\n    df['start_month'] = [int(value[5:7])   for value in df[\"Start Time\"]]\n    df['start_day']   = [int(value[8:10])  for value in df[\"Start Time\"]]\n    df['start_hour']  = [int(value[11:13]) for value in df[\"Start Time\"]]\n    df['start_min']   = [int(value[14:16]) for value in df[\"Start Time\"]]\n    df['start_sec']   = [int(value[17:20]) for value in df[\"Start Time\"]]\n\n    df['end_year']  = [int(value[0:4])   for value in df[\"End Time\"]]\n    df['end_month'] = [int(value[5:7])   for value in df[\"End Time\"]]\n    df['end_day']   = [int(value[8:10])  for value in df[\"End Time\"]]\n    df['end_hour']  = [int(value[11:13]) for value in df[\"End Time\"]]\n    df['end_min']   = [int(value[14:16]) for value in df[\"End Time\"]]\n    df['end_sec']   = [int(value[17:20]) for value in df[\"End Time\"]]\n\n    #Create a new column for the dataframe containing the day of the week\n    day_of_week = []\n    for index, row in df.iterrows():\n        day_of_week.append( dt.date(row[\"start_year\"], row[\"start_month\"], row[\"start_day\"]).weekday())\n    df['day_of_week'] = day_of_week\n\n    #Apply the required filters and return the dataframe\n    if time_filter == 'none':\n        load_data_print(start_time)\n        return df\n    elif time_filter == 'month':\n        df = df[(df['start_month']==MONTH[month])]\n        load_data_print(start_time)\n        return df\n    elif time_filter == 'day':\n        df = df[(df['day_of_week']==WEEKDAY[day])]\n        load_data_print(start_time)\n        return df\n    else:\n        print(\"\\nERROR on viable_unput_time value\")\n        return 1\n\n\n\ndef time_stats(df):\n    \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n\n    print('\\nCalculating The Most Frequent Times of Travel...\\n')\n    start_time = time.time()\n\n    # Display the most common month\n    month_mode = NUM_TO_MONTH[df['start_month'].mode()[0]]\n    print(\"The most common month has been \" + month_mode + \".\")\n\n    # Display the most common day of week\n    weekday_mode = NUM_TO_WEEKDAY[df['day_of_week'].mode()[0]]\n    print(\"The most common day of the week has been \" + weekday_mode + \".\")\n\n    # Display the most common start hour\n    start_hour_mode = [df['start_hour'].mode()[0]][0]\n    print(\"The most common hour has been at \" + str(start_hour_mode) + \" hours.\")\n\n    # Display the most common day\n    sd_df = df[['start_year', 'start_month','start_day']]\n    sd_df = sd_df.groupby(['start_day', 'start_month', 'start_year']).size().reset_index(name='counts')\n    sd_df = sd_df.sort_values(by = ['counts','start_year','start_month','start_day'], ascending = [False, False, False, False ])\n\n    cmonth, cday, cyear, ncounts = sd_df['start_month'].values[0], sd_df['start_day'].values[0], sd_df['start_year'].values[0], sd_df['counts'].values[0]\n    print(\"The most common day has been on \" + str(cday) + \" \" + NUM_TO_MONTH[cmonth] + \" \" + str(cyear) + \", having a total of \" + str(ncounts)+ \" bike rents.\")\n\n    #Time controlling\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef station_stats(df):\n    \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n    print('\\nCalculating The Most Popular Stations and Trip...\\n')\n    start_time = time.time()\n\n    # Display most commonly used start station\n    cstart_station = df['Start Station'].mode()[0]\n    print(\"The most common start station has been \" + cstart_station + \".\")\n\n    # Display most commonly used end station\n    cend_station = df['End Station'].mode()[0]\n    print(\"The most common end station has been \" + cend_station + \".\")\n\n    # Display most frequent combination of start station and end station trip\n    ss_df = df[['Start Station', 'End Station']]\n    ss_df = ss_df.groupby(['Start Station', 'End Station']).size().reset_index(name='counts')\n    ss_df = ss_df.sort_values(by = ['counts'], ascending = [False])\n\n    ccomb_start_station, ccbom_end_station = ss_df['Start Station'].values[0], ss_df['End Station'].values[0]\n    print(\"The most frequent combination of start station and end station trip bas been:\")\n    print(\"     -Start Station: \" + ccomb_start_station)\n    print(\"     -End Station:   \" + ccbom_end_station)\n    print(\"     Having a total of \" + str(ss_df['counts'].values[0]) + ' bike rents.')\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef trip_duration_stats(df):\n    \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n    print('\\nCalculating Trip Duration...\\n')\n    start_time = time.time()\n\n    # compute a column on the data frame having the travel time of each bike rent\n    df['travel_time'] = df['end_sec'] - df['start_sec'] + 60*(df['end_min']-df['start_min']) + 3600*(df['end_hour']-df['start_hour'])\n\n    # Display total travel time\n    total_time = df['travel_time'].sum()\n    print(\"The total travel time has been \" + str(dt.timedelta(seconds=int(total_time))) + \".\")\n\n    # Display mean travel time\n    avg_time = df['travel_time'].mean()\n    print(\"The average travel time has been \" + str(dt.timedelta(seconds=int(avg_time))) + \".\")\n\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef user_stats(df, city):\n    \"\"\"Displays statistics on bikeshare users.\"\"\"\n\n    print('\\nCalculating User Stats...\\n')\n    start_time = time.time()\n\n    #Display counts of user types\n    ut_df = df[['User Type']]\n    ut_df = ut_df.groupby(['User Type']).size().reset_index(name='counts')\n    ut_df = ut_df.sort_values(by = ['counts'], ascending = [False])\n\n    for row in range(ut_df.values.shape[0]):\n        counts = ut_df.values[row][1]\n        user_type = ut_df.values[row][0]\n        print(\"There has been \" + str(counts) + \" bike rents done by \" + user_type + \" users.\" )\n\n    print(\"\")\n\n    if city == \"washington\":\n        return 0\n\n\n    # Display counts of gender\n\n    g_df = df[['Gender']]\n    g_df = g_df.groupby(['Gender']).size().reset_index(name='counts')\n    g_df = g_df.sort_values(by = ['counts'], ascending = [False])\n\n    for row in range(g_df.values.shape[0]):\n        counts = g_df.values[row][1]\n        gender = g_df.values[row][0]\n        print(\"There has been \" + str(counts) + \" bike rents done by \" + gender + \" users.\" )\n\n\n    # Display earliest, most recent, and most common year of birth\n    print(\"\\nThe earliest year of birth as been \" + str(int(df['Birth Year'].min())))\n    print(\"The most recent year of birth as been \" + str(int(df['Birth Year'].max())))\n    print(\"The most common year of birth has been \" + str(int(df['Birth Year'].mode())))\n\n\n    # Print end message\n    print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n    print('-'*40)\n\n\ndef display_data(df):\n    q = input(\" Would you like to display some raw data?\\n\").lower()\n    if q != \"yes\":\n        return 0\n\n    #Deleting custom-created rows from the dataframe, which have been 14\n    original_df = df[df.columns[:-14]]\n\n    #initialize rowcount\n    rcount = 0\n\n    while(q == \"yes\"):\n        print(original_df.iloc[rcount : rcount + 5])\n        q = input(\"\\n Would you like to display 5 more rows?\\n\").lower()\n        rcount += 5\n\n\ndef main():\n    while True:\n        city,time_filter, month, day = get_filters()\n\n#         print('\\n' + ''*40 + '\\n')\n\n        df = load_data(city, time_filter, month, day)\n        time_stats(df)\n        station_stats(df)\n        trip_duration_stats(df)\n        user_stats(df, city)\n        display_data(df)\n\n        restart = input('\\nWould you like to restart? Enter yes or no.\\n').lower()\n        if restart.lower() != 'yes':\n            break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":11572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"343320898","text":"from flask import (\n    Blueprint,\n)\n\nfrom controllers.validators import (\n    HelloWorldSchema,\n)\nfrom controllers.utils import (\n    succeed,\n    binding_schemma,\n)\nfrom models import (\n    get_session,\n    HelloLog,\n)\n\nhello_bp = Blueprint(\"hello\", __name__, url_prefix=\"/v1/hello\")\n\n\n@hello_bp.route(\"/\")\n@binding_schemma(HelloWorldSchema)\ndef hello(data):\n    \"\"\"hello world\"\"\"\n    with get_session() as s:\n        s.add(HelloLog(name=data[\"name\"]))\n        s.commit()\n\n        return succeed()\n","sub_path":"controllers/hello.py","file_name":"hello.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"151573229","text":"# Processing Imports\nimport requests\n\n# Data Manipulation Imports\nimport pandas as pd\nimport numpy as np\n\n# CV Imports\nimport cv2\nimport cvlib as cv\n\n# Data Imports\nfrom components import traffic_maps\n\n# Streamlit Imports\nimport streamlit as st\nfrom streamlit_folium import folium_static\n\ndef app():\n    # Traffic Camera Data\n    API_URL = 'https://api.data.gov.sg/v1/transport/traffic-images'\n\n    camera_data = traffic_maps.get_camera_data(API_URL)\n    metadata = traffic_maps.get_metadata(camera_data[0])\n    camera_data_df = traffic_maps.get_camera_data_df(camera_data, metadata)\n\n    # Exhaust Emission Data\n    try:\n        exhaust_emission_df = pd.read_csv(\"./assets/exhaust_emissions.csv\")\n    except:\n        exhaust_emission_df = pd.read_csv(\"../assets/exhaust_emissions.csv\")\n\n    st.title(\"Prototype\")\n    # Explanation\n    st.header('Geospatial Breakdown of Traffic Conditions')\n    st.subheader('Prototype Explanation')\n    st.write(\"Our prototype aims to allow the ICC to make informed traffic decisions based on the current exhaust emissions on the road.\")\n    st.write(\"It utilises OpenCV's Computer Vision model to calculate the number of vehicles at each camera location based on categories and aggregates the vehicle's exhaust emissions data to provide the expected emissions at each camera location.\")\n    st.write(\"Note: the model may take some time to process the images from all 87 cameras.\")\n\n    cv_progress_bar = st.progress(0)\n    camera_progress_rate = 1/len(camera_data_df.index)\n    \n    # Heatmap\n    @st.cache(ttl = 300)\n    def run_CV(camera_data_df, exhaust_emission_df):\n        ## Image Processing function from traffic_maps component\n        # traffic_maps.get_vehicle_data(camera_data_df, exhaust_emission_df)\n        '''\n            Returns orginal df with number of vehicles at each camera.\n            Iterates through camera_data_df, appending num_vehicles based on vehicles on the road calculated through image link in df row.\n            Params:\n                camera_data_df: pd dataframe as input from get_camera_data_df() function.\n                exhaust_emission_df: exhaust emission data.\n        '''\n        CO2_emission_rate = exhaust_emission_df['CO₂'].values\n        NOx_emission_rate = exhaust_emission_df['NOx'].values\n        CO_emission_rate = exhaust_emission_df['CO'].values\n        P_emission_rate = exhaust_emission_df['P'].values\n\n\n        for index, row in camera_data_df.iterrows():\n            image = requests.get(row['image'])\n            file = open(\"sample_image.png\", \"wb\")\n            file.write(image.content)\n\n            im = cv2.imread('sample_image.png')\n            bbox, label, conf = cv.detect_common_objects(im)\n\n            all_vehicles = np.array([label.count('bus'), label.count('car'), label.count('truck'), label.count('motorcycle')])\n            test = label.count('bus')\n\n            camera_data_df.loc[index, 'vehicle_qty'] = all_vehicles.sum()\n            camera_data_df.loc[index, 'total_CO₂'] = np.around(np.multiply(CO2_emission_rate, all_vehicles).sum(), 2)\n            camera_data_df.loc[index, 'total_NOx'] = np.around(np.multiply(NOx_emission_rate, all_vehicles).sum(), 2)\n            camera_data_df.loc[index, 'total_CO'] = np.around(np.multiply(CO_emission_rate, all_vehicles).sum(), 2)\n            camera_data_df.loc[index, 'total_P'] = np.around(np.multiply(P_emission_rate, all_vehicles).sum(), 2)\n   \n            cv_progress_bar.progress(index * camera_progress_rate)\n\n        return camera_data_df\n\n    run_CV(camera_data_df, exhaust_emission_df)\n\n    st.subheader('Traffic Geospatial Heatmap')\n    st.write(\"The geospatial heatmap aims to give the ICC an overview of the number of vehicles on the road through a heat gradient. The 'hotter' an area is, the more vehicles there are at that location.\")\n\n    vehicle_qty_coord = traffic_maps.get_vehicle_qty_coord(camera_data_df)\n    heatmap = traffic_maps.create_heatmap(vehicle_qty_coord)\n\n    folium_static(heatmap) # Displays heatmap on Streamlit\n\n\n    # Camera Locations Map\n    st.subheader('Exhaust Emission Markers')\n    st.write(\"After identifying a location of interest through the heatmap, the user can then zoom in on the said location in the map below and identify the exact amount of vehicles picked up at this location and the total derived exhaust emissions.\")\n\n    cameraId_map = traffic_maps.create_cameraId_map(camera_data_df)\n    folium_static(cameraId_map) \n\n","sub_path":"apps/prototype_app.py","file_name":"prototype_app.py","file_ext":"py","file_size_in_byte":4437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"464207110","text":"# --------------------------------------------------------------------------- #\n# D. Rodriguez, 2020-05-21\n# --------------------------------------------------------------------------- #\n\nprint('\\nEnter two numbers and I\\'ll add them.')\n\nfirst_number = input('First number: ')\nsecond_number = input('Second number: ')\n\n\ntry:\n    result = int(first_number) + int(second_number)\nexcept ValueError:\n    print('\\nPlease, enter a number!')\nelse:\n    print(f'\\nThe sum of {first_number} and {second_number} is {result}.')\n\n","sub_path":"chapter_10/tiy_10_6_addition.py","file_name":"tiy_10_6_addition.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"460847570","text":"class Solution:\n\t# @param A : tuple of integers\n\t# @return an integer\n\tdef trap(self, A):\n\t    n=len(A)\n\t    if n<=2:\n\t        return 0\n\t    else:\n\t        R_max=[0]*n\n\t        L_max=[]\n\t        L_max.append(A[0])\n\t        for i in range(1,n):\n\t            L_max.append(max(L_max[i-1],A[i]))\n\t        \n\t        R_max[n-1]=A[n-1]\n\t        last_val=A[n-1]\n\t        i=n-2\n\t        while(i>=0):\n\t            last_val=max(last_val,A[i])\n\t            R_max[i]=last_val\n\t            i-=1\n\t        trap_sum=0\n\t        for i in range(1,n-1):\n\t            if min(L_max[i],R_max[i])>A[i]:\n\t                trap_sum+=min(L_max[i],R_max[i])-A[i]\n\t        return trap_sum\ndef main():\n    A=list(map(int,input().split(' ')))\n    B=Solution()\n    n=B.trap(A)\n    print(n)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"rain_trap.py","file_name":"rain_trap.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"625246675","text":"# Copyright (c) 2015, The MITRE Corporation. All rights reserved.\n# See LICENSE.txt for complete terms.\n\n# internal\nimport stix\nimport stix.bindings.ttp as ttp_binding\nfrom stix.common import vocabs, Statement\nfrom stix.common.kill_chains import KillChainPhasesReference\nfrom stix.common.related import RelatedPackageRefs\n\n# relative\nfrom .behavior import Behavior\nfrom .resource import Resource\nfrom .victim_targeting import VictimTargeting\n\n\nclass TTP(stix.BaseCoreComponent):\n    \"\"\"Implementation of the STIX TTP.\n\n    Args:\n        id_ (optional): An identifier. If ``None``, a value will be generated\n            via ``mixbox.idgen.create_id()``. If set, this will unset the\n            ``idref`` property.\n        idref (optional): An identifier reference. If set this will unset the\n            ``id_`` property.\n        timestamp (optional): A timestamp value. Can be an instance of\n            ``datetime.datetime`` or ``str``.\n        description: A description of the purpose or intent of this object.\n        short_description: A short description of the intent\n            or purpose of this object.\n        title: The title of this object.\n\n    \"\"\"\n    _binding = ttp_binding\n    _binding_class = _binding.TTPType\n    _namespace = \"http://stix.mitre.org/TTP-1\"\n    _version = \"1.2\"\n    _ALL_VERSIONS = (\"1.0\", \"1.0.1\", \"1.1\", \"1.1.1\", \"1.2\")\n    _ID_PREFIX = \"ttp\"\n\n    def __init__(self, id_=None, idref=None, timestamp=None, title=None,\n                 description=None, short_description=None):\n\n        super(TTP, self).__init__(\n            id_=id_,\n            idref=idref,\n            timestamp=timestamp,\n            title=title,\n            description=description,\n            short_description=short_description\n        )\n\n        self.behavior = None\n        self.related_ttps = None\n        self.intended_effects = None\n        self.resources = None\n        self.victim_targeting = None\n        self.exploit_targets = ExploitTargets()\n        self.related_packages = None\n        self.kill_chain_phases = None\n\n    @property\n    def behavior(self):\n        \"\"\"A :class:`.Behavior` field.\n\n        \"\"\"\n        return self._behavior\n\n    @behavior.setter\n    def behavior(self, value):\n        self._set_var(Behavior, try_cast=False, behavior=value)\n\n    @property\n    def related_ttps(self):\n        \"\"\"A collection of :class:`.RelatedTTP` objects. This behaves like a\n        ``MutableSequence`` Type.\n\n        \"\"\"\n        return self._related_ttps\n\n    @related_ttps.setter\n    def related_ttps(self, value):\n        if isinstance(value, RelatedTTPs):\n            self._related_ttps = value\n        else:\n            self._related_ttps = RelatedTTPs(value)\n\n    def add_related_ttp(self, value):\n        \"\"\"Adds an Related TTP to the :attr:`related_ttps` list\n        property of this :class:`TTP`.\n\n        The `TTP` parameter must be an instance of\n        :class:`.RelatedTTP` or :class:`TTP`.\n\n        If the `TTP` parameter is ``None``, no item wil be added to the\n        ``related_ttps`` list property.\n\n        Calling this method is the same as calling ``append()`` on the\n        ``related_ttps`` property.\n\n        See Also:\n            The :class:`RelatedTTPs` documentation.\n\n        Note:\n            If the `TTP` parameter is not an instance of\n            :class:`.RelatedTTP` an attempt will be\n            made to convert it to one.\n\n        Args:\n            TTP: An instance of :class:`TTP` or\n                :class:`.RelatedTTP`.\n\n        Raises:\n            ValueError: If the `TTP` parameter cannot be converted into\n                an instance of :class:`.RelatedTTP`\n\n        \"\"\"\n        self.related_ttps.append(value)\n\n    @property\n    def exploit_targets(self):\n        \"\"\"A collection of :class:`.ExploitTarget` objects. This behaves like\n        a ``MutableSequence`` type.\n\n        \"\"\"\n        return self._exploit_targets\n\n    @exploit_targets.setter\n    def exploit_targets(self, value):\n        if isinstance(value, ExploitTargets):\n            self._exploit_targets = value\n        else:\n            self._exploit_targets = ExploitTargets(value)\n\n    def add_exploit_target(self, value):\n        \"\"\"Adds a :class:`.ExploitTarget` object to the :attr:`exploit_targets`\n        collection.\n\n        \"\"\"\n        self.exploit_targets.append(value)\n\n    @property\n    def intended_effects(self):\n        \"\"\"A collection of :class:`.Statement` objects. This behaves like a\n        ``MutableSequence`` type.\n\n        If set to a string, an attempt will be made to convert it into a\n        :class:`.Statement` object with its value set to an instance of\n        :class:`.IntendedEffect`.\n\n        \"\"\"\n        return self._intended_effects\n\n    @intended_effects.setter\n    def intended_effects(self, value):\n        self._intended_effects = _IntendedEffects(value)\n\n    def add_intended_effect(self, value):\n        \"\"\"Adds a :class:`.Statement` object to the :attr:`intended_effects`\n        collection.\n\n        If `value` is a string, an attempt will be made to convert it into an\n        instance of :class:`.Statement`.\n\n        \"\"\"\n        self.intended_effects.append(value)\n\n    @property\n    def resources(self):\n        \"\"\"A collection of :class:`.Resource` objects. This behaves like a\n        ``MutableSequence`` type.\n\n        \"\"\"\n        return self._resources\n\n    @resources.setter\n    def resources(self, value):\n        self._set_var(Resource, resources=value)\n\n    @property\n    def victim_targeting(self):\n        \"\"\"A collection of :class:`.VictimTargeting` objects. This behaves like\n        a ``MutableSequence`` type.\n\n        \"\"\"\n        return self._victim_targeting\n\n    @victim_targeting.setter\n    def victim_targeting(self, value):\n        self._set_var(VictimTargeting, try_cast=False, victim_targeting=value)\n\n    @property\n    def kill_chain_phases(self):\n        \"\"\"A collection of :class:`.KillChainPhaseReference` objects. This\n        behaves like a ``MutableSequence`` type.\n\n        \"\"\"\n        return self._kill_chain_phases\n\n    @kill_chain_phases.setter\n    def kill_chain_phases(self, value):\n        self._kill_chain_phases = KillChainPhasesReference(value)\n\n    def add_kill_chain_phase(self, value):\n        \"\"\"Adds a :class:`.KillChainPhaseReference` to the\n        :attr:`kill_chain_phases` collection.\n\n        Args:\n            value: A :class:`.KillChainPhase`, :class:`.KillChainPhaseReference`\n                or a ``str`` representing the phase_id of. Note that you if you\n                are defining a custom Kill Chain, you need to add it to the\n                STIX package separately.\n        \"\"\"\n        self.kill_chain_phases.append(value)\n\n    @property\n    def related_packages(self):\n        \"\"\"**DEPRECATED**: A collection of :class:`.RelatedPackageRef`\n        objects. This behaves like a ``MutableSequence``.\n\n        \"\"\"\n        return self._related_packages\n\n    @related_packages.setter\n    def related_packages(self, value):\n        self._related_packages = RelatedPackageRefs(value)\n\n    def add_related_package(self, value):\n        \"\"\"Adds a :class:`.RelatedPackageRef` object to the\n        :attr:`related_packages` collection.\n\n        Args:\n            value: A :class:`.RelatedPackageRef` or a :class:`.STIXPackage`\n                object.\n\n        \"\"\"\n        self.related_packages.append(value)\n\n    def to_obj(self, return_obj=None, ns_info=None):\n        if not return_obj:\n            return_obj = self._binding_class()\n\n        super(TTP, self).to_obj(return_obj=return_obj, ns_info=ns_info)\n\n        if self.behavior:\n            return_obj.Behavior = self.behavior.to_obj(ns_info=ns_info)\n        if self.related_ttps:\n            return_obj.Related_TTPs = self.related_ttps.to_obj(ns_info=ns_info)\n        if self.exploit_targets:\n            return_obj.Exploit_Targets = self.exploit_targets.to_obj(ns_info=ns_info)\n        if self.intended_effects:\n            return_obj.Intended_Effect = self.intended_effects.to_obj(ns_info=ns_info)\n        if self.resources:\n            return_obj.Resources = self.resources.to_obj(ns_info=ns_info)\n        if self.victim_targeting:\n            return_obj.Victim_Targeting = self.victim_targeting.to_obj(ns_info=ns_info)\n        if self.kill_chain_phases:\n            return_obj.Kill_Chain_Phases = self.kill_chain_phases.to_obj(ns_info=ns_info)\n        if self.related_packages:\n            return_obj.Related_Packages = self.related_packages.to_obj(ns_info=ns_info)\n\n        return return_obj\n\n    @classmethod\n    def from_obj(cls, obj, return_obj=None):\n        if not obj:\n            return None\n\n        if not return_obj:\n            return_obj = cls()\n\n        super(TTP, cls).from_obj(obj, return_obj=return_obj)\n\n        if isinstance(obj, cls._binding_class):\n            return_obj.behavior = Behavior.from_obj(obj.Behavior)\n            return_obj.related_ttps = RelatedTTPs.from_obj(obj.Related_TTPs)\n            return_obj.exploit_targets = ExploitTargets.from_obj(obj.Exploit_Targets)\n            return_obj.resources = Resource.from_obj(obj.Resources)\n            return_obj.victim_targeting = VictimTargeting.from_obj(obj.Victim_Targeting)\n            return_obj.intended_effects = _IntendedEffects.from_obj(obj.Intended_Effect)\n            return_obj.kill_chain_phases = KillChainPhasesReference.from_obj(obj.Kill_Chain_Phases)\n            return_obj.related_packages = RelatedPackageRefs.from_obj(obj.Related_Packages)\n\n        return return_obj\n\n    def to_dict(self):\n        return super(TTP, self).to_dict()\n\n    @classmethod\n    def from_dict(cls, dict_repr, return_obj=None):\n        if not dict_repr:\n            return None\n\n        if not return_obj:\n            return_obj = cls()\n\n        super(TTP, cls).from_dict(dict_repr, return_obj=return_obj)\n\n        get = dict_repr.get\n        return_obj.behavior = Behavior.from_dict(get('behavior'))\n        return_obj.related_ttps = RelatedTTPs.from_dict(get('related_ttps'))\n        return_obj.exploit_targets = ExploitTargets.from_dict(get('exploit_targets'))\n        return_obj.intended_effects = _IntendedEffects.from_dict(get('intended_effects'))\n        return_obj.resources = Resource.from_dict(get('resources'))\n        return_obj.victim_targeting = VictimTargeting.from_dict(get('victim_targeting'))\n        return_obj.related_packages = RelatedPackageRefs.from_dict(get('related_packages'))\n        return_obj.kill_chain_phases = KillChainPhasesReference.from_dict(get('kill_chain_phases'))\n\n        return return_obj\n\n\n# NOT ACTUAL STIX TYPE\nclass _IntendedEffects(stix.TypedList):\n    _contained_type = Statement\n\n    def _fix_value(self, value):\n        intended_effect = vocabs.IntendedEffect(value)\n        return Statement(value=intended_effect)\n\n\n# Avoid circular imports\nfrom .related_ttps import RelatedTTPs\nfrom .exploit_targets import ExploitTargets\n","sub_path":"stix/ttp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":10835,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"185118874","text":"import os\nfrom datetime import datetime\nfrom functools import partial\nfrom math import ceil\n\nimport cv2\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.python.keras import Model, applications, Sequential\nfrom tensorflow.python.keras.callbacks_v1 import TensorBoard\nfrom tensorflow.python.keras.engine.saving import load_model\nfrom tensorflow.python.keras.layers import Input, BatchNormalization, Dense, Flatten, RepeatVector, Reshape, Lambda, \\\n    UpSampling2D, Layer, concatenate, Conv2D\nfrom tensorflow.python.keras.losses import huber_loss\nfrom tensorflow.python.keras.optimizers import Adam\nfrom tqdm import tqdm\n\nfrom chroma_instance.config import MAX_INSTANCES\nfrom chroma_instance.config.FirstTest import FirstTestConfig\nfrom chroma_instance.data.generator import Data\nfrom chroma_instance.model.basic import discriminator_network, RandomWeightedAverage, wasserstein_loss_dummy, \\\n    gradient_penalty_loss, constant_loss_dummy\nfrom chroma_instance.util import write_log, deprocess_float2int, reconstruct_and_save, prepare_logger\n\nGRADIENT_PENALTY_WEIGHT = 10\n\n\nclass WeightGenerator(Layer):\n    def __init__(self, units, batch_size, **kwargs):\n        super(WeightGenerator, self).__init__(**kwargs)\n\n        self.instance_conv = Sequential([\n            Conv2D(units, 3, padding='same', strides=1, activation='relu', name='weight_instance_conv1'),\n            Conv2D(units, 3, padding='same', strides=1, activation='relu', name='weight_instance_conv2'),\n            Conv2D(1, 3, padding='same', strides=1, activation='relu', name='weight_instance_conv3'),\n        ])\n\n        self.simple_bg_conv = Sequential([\n            Conv2D(units, 3, padding='same', strides=1, activation='relu', name='weight_bg_conv1'),\n            Conv2D(units, 3, padding='same', strides=1, activation='relu', name='weight_bg_conv2'),\n            Conv2D(1, 3, padding='same', strides=1, activation='relu', name='weight_bg_conv3'),\n        ])\n\n        self.units = units\n        self.batch_size = batch_size\n\n    def resize_and_pad(self, feauture_maps, info_array, bg_size):\n        y_size = tf.math.maximum(tf.cast(bg_size[0] * (info_array[1] - info_array[0]), tf.int32), 1)\n        x_size = tf.math.maximum(tf.cast(bg_size[1] * (info_array[3] - info_array[2]), tf.int32), 1)\n        feauture_maps = tf.image.resize(feauture_maps, (y_size, x_size))\n        y_front_pad = tf.cast(bg_size[0] * info_array[0], tf.int32)\n        x_front_pad = tf.cast(bg_size[1] * info_array[2], tf.int32)\n        padding = [\n            [y_front_pad, bg_size[0] - y_front_pad - y_size],\n            [x_front_pad, bg_size[1] - x_front_pad - x_size],\n            [0, 0],\n        ]\n        feauture_maps = tf.pad(feauture_maps, padding)\n        return feauture_maps\n\n    def call(self, inputs, **kwargs):\n        instance_feature, bg_feature, bbox, mrcnn_mask = inputs\n        bg_size = bg_feature.get_shape().as_list()[1:3]\n\n        output = []\n\n        combined_instance_feature = pack_instance(instance_feature)\n        combined_instance_weight = self.instance_conv(combined_instance_feature)\n        instance_weights = unpack_instance(combined_instance_weight)\n\n        bg_weights = self.simple_bg_conv(bg_feature)\n\n        for i in range(self.batch_size):\n            resized_sub_weights = []\n            resized_masks = []\n            for j in range(MAX_INSTANCES):\n                box_info = bbox[i, :, j]  # (4, )\n                instance_weight = instance_weights[i, :, :, :, j]  # (h, w, 1)\n                resized_sub_weight = self.resize_and_pad(instance_weight, box_info, bg_size)  # (h_i, w_i, 1)\n\n                instance_mask = mrcnn_mask[i, :, :, j:j + 1]  # (H, W, 1)\n                resized_mask = self.resize_and_pad(instance_mask, [0, 1, 0, 1], bg_size)  # (h_i, w_i, 1)\n\n                resized_sub_weights.append(resized_sub_weight)\n                resized_masks.append(resized_mask)\n\n            bg_weight = bg_weights[i, :, :, :]\n\n            exp_weight = []\n            for f, m in zip(resized_sub_weights, resized_masks):\n                exp_weight.append(tf.exp(f) * m)\n            exp_weight.append(tf.exp(bg_weight))\n\n            exp_weight_sum = sum(exp_weight)\n            softmax_weight = [w / exp_weight_sum for w in exp_weight]\n\n            composed = softmax_weight[-1] * bg_feature[i, :, :, :]\n            for j in range(MAX_INSTANCES):\n                box_info = bbox[i, :, j]\n                instance_sub_feature = instance_feature[i, :, :, :, j]  # (h, w, c)\n                resized_sub_feature = self.resize_and_pad(instance_sub_feature, box_info, bg_size)  # (h_i, w_i, c)\n                composed += resized_sub_feature * softmax_weight[j]\n\n            output.append(composed)\n\n        stacked_output = tf.stack(output)\n        return stacked_output\n\n    def compute_output_shape(self, input_shape):\n        return input_shape[1]\n\n    def get_config(self):\n        config = super(WeightGenerator, self).get_config()\n        config.update({\n            # \"instance_weights\": self.instance_conv.get_weights(),\n            # \"bg_weights\": self.simple_bg_conv.get_weights(),\n            \"units\": self.units,\n            \"batch_size\": self.batch_size,\n        })\n        return config\n\n    @classmethod\n    def from_config(cls, config):\n        obj = cls(config[\"units\"], config[\"batch_size\"])\n        # obj.instance_conv.set_weights(config[\"instance_weights\"])\n        # obj.simple_bg_conv.set_weights(config[\"bg_weights\"])\n        return obj\n\n\ndef pack_instance(x):\n    return tf.squeeze(\n        tf.reshape(\n            tf.transpose(\n                x,\n                perm=[0, 4, 1, 2, 3]),\n            (-1, 1, *x.get_shape().as_list()[1:-1])),\n        axis=1)\n\n\ndef unpack_instance(x):\n    return tf.transpose(\n        tf.reshape(\n            tf.expand_dims(\n                x,\n                axis=1),\n            (-1, MAX_INSTANCES, *x.get_shape().as_list()[1:])),\n        perm=[0, 2, 3, 4, 1])\n\n\ndef instance_network(shape):\n    input_img = Input(shape=(*shape, 1, MAX_INSTANCES), name='fg_input_img')\n\n    input_packed = Lambda(lambda x: pack_instance(x), name='fg_pack_input')(input_img)\n    input_img_3 = Lambda(lambda x: tf.tile(x, [1, 1, 1, 3]), name='fg_input_tile')(input_packed)\n\n    # VGG16 without top layers\n    VGG_model = applications.vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n    model_3 = Model(VGG_model.input, VGG_model.layers[-6].output, name='fg_model_3')(input_img_3)\n\n    # Global features\n    conv2d_6 = Conv2D(512, (3, 3), padding='same', strides=(2, 2), activation='relu', name='fg_conv2d_6')(model_3)\n    batch_normalization_1 = BatchNormalization(name='fg_batch_normalization_1')(conv2d_6)\n    conv2d_7 = Conv2D(512, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_7')(\n        batch_normalization_1)\n    batch_normalization_2 = BatchNormalization(name='fg_batch_normalization_2')(conv2d_7)\n\n    conv2d_8 = Conv2D(512, (3, 3), padding='same', strides=(2, 2), activation='relu', name='fg_conv2d_8')(\n        batch_normalization_2)\n    batch_normalization_3 = BatchNormalization(name='fg_batch_normalization_3')(conv2d_8)\n    conv2d_9 = Conv2D(512, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_9')(\n        batch_normalization_3)\n    batch_normalization_4 = BatchNormalization(name='fg_batch_normalization_4')(conv2d_9)\n\n    # Global feature pass back to colorization + classification\n    flatten_1 = Flatten(name='fg_flatten_1')(batch_normalization_4)\n    dense_1 = Dense(1024, activation='relu', name='fg_dense_1')(flatten_1)\n    dense_2 = Dense(512, activation='relu', name='fg_dense_2')(dense_1)\n    dense_3 = Dense(256, activation='relu', name='fg_dense_3')(dense_2)\n    repeat_vector_1 = RepeatVector(28 * 28, name='fg_repeat_vector_1')(dense_3)\n    reshape_1 = Reshape((28, 28, 256), name='fg_reshape_1')(repeat_vector_1)\n\n    # Mid-level features\n    conv2d_10 = Conv2D(512, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_10')(model_3)\n    batch_normalization_5 = BatchNormalization(name='fg_batch_normalization_5')(conv2d_10)\n    conv2d_11 = Conv2D(256, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_11')(\n        batch_normalization_5)\n    batch_normalization_6 = BatchNormalization(name='fg_batch_normalization_6')(conv2d_11)\n\n    # Fusion of (VGG16 -> Mid-level) + (VGG16 -> Global) + Colorization\n    concatenate_2 = concatenate([batch_normalization_6, reshape_1], name='fg_concatenate_2')\n\n    conv2d_12 = Conv2D(256, (1, 1), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_12')(\n        concatenate_2)\n    conv2d_13 = Conv2D(128, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_13')(conv2d_12)\n    up_sampling2d_1 = UpSampling2D(size=(2, 2), name='fg_up_sampling2d_1', interpolation='bilinear')(conv2d_13)\n    # conv2dt_1 = Conv2DTranspose(64, (4, 4), padding='same', strides=(2, 2), name='fg_conv2dt_1')(conv2d_13)\n\n    conv2d_14 = Conv2D(64, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_14')(\n        up_sampling2d_1)\n    conv2d_15 = Conv2D(64, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_15')(conv2d_14)\n    up_sampling2d_2 = UpSampling2D(size=(2, 2), name='fg_up_sampling2d_2', interpolation='bilinear')(conv2d_15)\n    # conv2dt_2 = Conv2DTranspose(32, (4, 4), padding='same', strides=(2, 2), name='fg_conv2dt_2')(conv2d_15)\n\n    conv2d_16 = Conv2D(32, (3, 3), padding='same', strides=(1, 1), activation='relu', name='fg_conv2d_16')(\n        up_sampling2d_2)\n    conv2d_17 = Conv2D(2, (3, 3), padding='same', strides=(1, 1), activation='sigmoid', name='fg_conv2d_17')(conv2d_16)\n    up_sampling2d_3 = UpSampling2D(size=(2, 2), name='fg_up_sampling2d_3')(conv2d_17)\n\n    model_3_unpack = Lambda(lambda x: unpack_instance(x), name='fg_model_3_unpack')(model_3)\n    conv2d_11_unpack = Lambda(lambda x: unpack_instance(x), name='fg_conv2d_11_unpack')(conv2d_11)\n    conv2d_13_unpack = Lambda(lambda x: unpack_instance(x), name='fg_conv2d_13_unpack')(conv2d_13)\n    conv2d_15_unpack = Lambda(lambda x: unpack_instance(x), name='fg_conv2d_15_unpack')(conv2d_15)\n    conv2d_17_unpack = Lambda(lambda x: unpack_instance(x), name='fg_conv2d_17_unpack')(conv2d_17)\n    up_sampling2d_3_unpack = Lambda(lambda x: unpack_instance(x), name='up_sampling2d_3_unpack')(up_sampling2d_3)\n    generated = Model(inputs=input_img,\n                      outputs=[model_3_unpack, conv2d_11_unpack, conv2d_13_unpack, conv2d_15_unpack, conv2d_17_unpack,\n                               up_sampling2d_3_unpack])\n\n    return generated\n\n\ndef fusion_network(shape, batch_size):\n    input_img = Input(shape=(*shape, 1), name='input_img')\n    input_img_3 = Lambda(lambda x: tf.tile(x, [1, 1, 1, 3]), name='input_tile')(input_img)\n\n    bbox = Input(shape=(4, MAX_INSTANCES), name='bbox')\n    mask = Input(shape=(*shape, MAX_INSTANCES), name='mask')\n\n    # VGG16 without top layers\n    VGG_model = applications.vgg16.VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3))\n    vgg_model_3_pre = Model(VGG_model.input, VGG_model.layers[-6].output, name='model_3')(input_img_3)\n    fg_model_3 = Input(shape=(*vgg_model_3_pre.get_shape().as_list()[1:], MAX_INSTANCES), name='fg_model_3')  # <-\n    vgg_model_3 = WeightGenerator(64, batch_size, name='weight_generator_1')(\n        [fg_model_3, vgg_model_3_pre, bbox, mask])  # <-\n\n    # Global features\n    conv2d_6 = Conv2D(512, (3, 3), padding='same', strides=(2, 2), activation='relu', name='conv2d_6')(vgg_model_3)\n    batch_normalization_1 = BatchNormalization(name='batch_normalization_1')(conv2d_6)\n    conv2d_7 = Conv2D(512, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_7')(\n        batch_normalization_1)\n    batch_normalization_2 = BatchNormalization(name='batch_normalization_2')(conv2d_7)\n\n    conv2d_8 = Conv2D(512, (3, 3), padding='same', strides=(2, 2), activation='relu', name='conv2d_8')(\n        batch_normalization_2)\n    batch_normalization_3 = BatchNormalization(name='batch_normalization_3')(conv2d_8)\n    conv2d_9 = Conv2D(512, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_9')(\n        batch_normalization_3)\n    batch_normalization_4 = BatchNormalization(name='batch_normalization_4')(conv2d_9)\n\n    # Classification\n    flatten_2 = Flatten(name='flatten_2')(batch_normalization_4)\n    dense_4 = Dense(4096, activation='relu', name='dense_4')(flatten_2)\n    dense_5 = Dense(4096, activation='relu', name='dense_5')(dense_4)\n    dense_6 = Dense(1000, activation='softmax', name='dense_6')(dense_5)\n\n    # Global feature pass back to colorization + classification\n    flatten_1 = Flatten(name='flatten_1')(batch_normalization_4)\n    dense_1 = Dense(1024, activation='relu', name='dense_1')(flatten_1)\n    dense_2 = Dense(512, activation='relu', name='dense_2')(dense_1)\n    dense_3 = Dense(256, activation='relu', name='dense_3')(dense_2)\n    repeat_vector_1 = RepeatVector(28 * 28, name='repeat_vector_1')(dense_3)\n    reshape_1 = Reshape((28, 28, 256), name='reshape_1')(repeat_vector_1)\n\n    # Mid-level features\n    conv2d_10 = Conv2D(512, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_10')(vgg_model_3)\n    batch_normalization_5 = BatchNormalization(name='batch_normalization_5')(conv2d_10)\n    conv2d_11_pre = Conv2D(256, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_11')(\n        batch_normalization_5)\n    fg_conv2d_11 = Input(shape=(*conv2d_11_pre.get_shape().as_list()[1:], MAX_INSTANCES), name='fg_conv2d_11')  # <-\n    conv2d_11 = WeightGenerator(32, batch_size, name='weight_generator_2')(\n        [fg_conv2d_11, conv2d_11_pre, bbox, mask])  # <-\n    batch_normalization_6 = BatchNormalization(name='batch_normalization_6')(conv2d_11)\n\n    # Fusion of (VGG16 -> Mid-level) + (VGG16 -> Global) + Colorization\n    concatenate_2 = concatenate([batch_normalization_6, reshape_1], name='concatenate_2')\n\n    conv2d_12 = Conv2D(256, (1, 1), padding='same', strides=(1, 1), activation='relu', name='conv2d_12')(concatenate_2)\n    conv2d_13_pre = Conv2D(128, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_13')(conv2d_12)\n    fg_conv2d_13 = Input(shape=(*conv2d_13_pre.get_shape().as_list()[1:], MAX_INSTANCES), name='fg_conv2d_13')  # <-\n    conv2d_13 = WeightGenerator(16, batch_size, name='weight_generator_3')(\n        [fg_conv2d_13, conv2d_13_pre, bbox, mask])  # <-\n    # conv2dt_1 = Conv2DTranspose(64, (4, 4), padding='same', strides=(2, 2), name='conv2dt_1')(conv2d_13)\n    up_sampling2d_1 = UpSampling2D(size=(2, 2), name='up_sampling2d_1', interpolation='bilinear')(conv2d_13)\n\n    conv2d_14 = Conv2D(64, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_14')(up_sampling2d_1)\n    conv2d_15_pre = Conv2D(64, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_15')(conv2d_14)\n    fg_conv2d_15 = Input(shape=(*conv2d_15_pre.get_shape().as_list()[1:], MAX_INSTANCES), name='fg_conv2d_15')  # <-\n    conv2d_15 = WeightGenerator(16, batch_size, name='weight_generator_4')(\n        [fg_conv2d_15, conv2d_15_pre, bbox, mask])  # <-\n    # conv2dt_2 = Conv2DTranspose(32, (4, 4), padding='same', strides=(2, 2), name='conv2dt_2')(conv2d_15)\n    up_sampling2d_2 = UpSampling2D(size=(2, 2), name='up_sampling2d_2', interpolation='bilinear')(conv2d_15)\n\n    conv2d_16 = Conv2D(32, (3, 3), padding='same', strides=(1, 1), activation='relu', name='conv2d_16')(up_sampling2d_2)\n    conv2d_17_pre = Conv2D(2, (3, 3), padding='same', strides=(1, 1), activation='sigmoid', name='conv2d_17')(conv2d_16)\n    fg_conv2d_17 = Input(shape=(*conv2d_17_pre.get_shape().as_list()[1:], MAX_INSTANCES), name='fg_conv2d_17')  # <-\n    conv2d_17 = WeightGenerator(16, batch_size, name='weight_generator_5')(\n        [fg_conv2d_17, conv2d_17_pre, bbox, mask])  # <-\n    # conv2dt_3 = Conv2DTranspose(2, (4, 4), padding='same', strides=(2, 2), name='conv2dt_3')(conv2d_17)\n    up_sampling2d_3 = UpSampling2D(size=(2, 2), name='up_sampling2d_3', interpolation='bilinear')(conv2d_17)\n\n    return Model(\n        inputs=[input_img, fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15, fg_conv2d_17, bbox, mask],\n        outputs=[up_sampling2d_3, dense_6])\n\n\nclass FusionModel:\n    def __init__(self, config, load_weight_path=None, ab_loss='mse'):\n        img_shape = (config.IMAGE_SIZE, config.IMAGE_SIZE)\n\n        # Creating generator and discriminator\n        optimizer = Adam(0.00002, 0.5)\n\n        self.foreground_generator = instance_network(img_shape)\n        self.foreground_generator.compile(\n            loss=[constant_loss_dummy, constant_loss_dummy, constant_loss_dummy, constant_loss_dummy,\n                  constant_loss_dummy, ab_loss], optimizer=optimizer)\n\n        self.fusion_discriminator = discriminator_network(img_shape)\n        self.fusion_discriminator.compile(loss=wasserstein_loss_dummy, optimizer=optimizer)\n        self.fusion_generator = fusion_network(img_shape, config.BATCH_SIZE)\n        self.fusion_generator.compile(loss=[ab_loss, 'kld'], optimizer=optimizer)\n\n        if load_weight_path:\n            chroma_gan = load_model(load_weight_path)\n            chroma_gan_layers = [layer.name for layer in chroma_gan.layers]\n\n            print('Loading chroma GAN parameter to instance network...')\n            instance_layer_names = [layer.name for layer in self.foreground_generator.layers]\n            for i, layer in enumerate(instance_layer_names):\n                if layer == 'fg_model_3':\n                    print('model 3 skip')\n                    continue\n                if len(layer) < 2:\n                    continue\n                if layer[:3] == 'fg_':\n                    try:\n                        j = chroma_gan_layers.index(layer[3:])\n                        self.foreground_generator.layers[i].set_weights(chroma_gan.layers[j].get_weights())\n                        print(f'Successfully set weights for layer {layer}')\n                    except ValueError:\n                        print(f'Layer {layer} not found in chroma gan.')\n                    except Exception as e:\n                        print(e)\n\n            print('Loading chroma GAN parameter to fusion network...')\n            fusion_layer_names = [layer.name for layer in self.fusion_generator.layers]\n            for i, layer in enumerate(fusion_layer_names):\n                if layer == 'model_3':\n                    print('model 3 skip')\n                    continue\n                try:\n                    j = chroma_gan_layers.index(layer)\n                    self.fusion_generator.layers[i].set_weights(chroma_gan.layers[j].get_weights())\n                    print(f'Successfully set weights for layer {layer}')\n                except ValueError:\n                    print(f'Layer {layer} not found in chroma gan.')\n                except Exception as e:\n                    print(e)\n\n        # Fg=instance prediction\n        fg_img_l = Input(shape=(*img_shape, 1, MAX_INSTANCES), name='fg_img_l')\n\n        # self.foreground_generator.trainable = False\n        self.foreground_generator.trainable = False\n        fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15, fg_conv2d_17, up_sampling2d_3 = self.foreground_generator(\n            fg_img_l)\n\n        # Fusion prediction\n        fusion_img_l = Input(shape=(*img_shape, 1), name='fusion_img_l')\n        fusion_img_real_ab = Input(shape=(*img_shape, 2), name='fusion_img_real_ab')\n        fg_bbox = Input(shape=(4, MAX_INSTANCES), name='fg_bbox')\n        fg_mask = Input(shape=(*img_shape, MAX_INSTANCES), name='fg_mask')\n\n        self.fusion_generator.trainable = False\n        fusion_img_pred_ab, fusion_class_vec = self.fusion_generator([fusion_img_l, fg_model_3, fg_conv2d_11,\n                                                                      fg_conv2d_13, fg_conv2d_15, fg_conv2d_17, fg_bbox,\n                                                                      fg_mask])\n\n        dis_pred_ab = self.fusion_discriminator([fusion_img_pred_ab, fusion_img_l])\n        dis_real_ab = self.fusion_discriminator([fusion_img_real_ab, fusion_img_l])\n\n        # Sample the gradient penalty\n        img_ab_interp_samples = RandomWeightedAverage()([fusion_img_pred_ab, fusion_img_real_ab])\n        dis_interp_ab = self.fusion_discriminator([img_ab_interp_samples, fusion_img_l])\n        partial_gp_loss = partial(gradient_penalty_loss,\n                                  averaged_samples=img_ab_interp_samples,\n                                  gradient_penalty_weight=GRADIENT_PENALTY_WEIGHT)\n        partial_gp_loss.__name__ = 'gradient_penalty'\n\n        # Compile D and G as well as combined\n        self.discriminator_model = Model(\n            inputs=[fusion_img_l, fusion_img_real_ab, fg_img_l, fg_bbox, fg_mask],\n            outputs=[dis_real_ab,\n                     dis_pred_ab,\n                     dis_interp_ab], name='discriminator')\n\n        self.discriminator_model.compile(optimizer=optimizer,\n                                         loss=[wasserstein_loss_dummy,\n                                               wasserstein_loss_dummy,\n                                               partial_gp_loss], loss_weights=[-1.0, 1.0, 1.0])\n\n        self.fusion_generator.trainable = True\n        self.fusion_discriminator.trainable = False\n        self.combined = Model(inputs=[fusion_img_l, fg_img_l, fg_bbox, fg_mask],\n                              outputs=[fusion_img_pred_ab, up_sampling2d_3, fusion_class_vec, dis_pred_ab],\n                              name='combined')\n        self.combined.compile(loss=[ab_loss, ab_loss, 'kld', wasserstein_loss_dummy],\n                              loss_weights=[1.0, 0.5, 0.003, -0.1],\n                              optimizer=optimizer)\n\n        # Monitor stuff\n        self.callback = TensorBoard(config.LOG_DIR)\n        self.callback.set_model(self.combined)\n        self.train_names = ['loss', 'mse_loss', 'msei_loss', 'kullback_loss', 'wasserstein_loss']\n        self.disc_names = ['disc_loss', 'disc_valid', 'disc_fake', 'disc_gp']\n\n        self.test_loss_array = []\n        self.g_loss_array = []\n\n    def train(self, data: Data, test_data, log, config, skip_to_after_epoch=None):\n        # Load VGG network\n        VGG_modelF = applications.vgg16.VGG16(weights='imagenet', include_top=True)\n\n        # Real, Fake and Dummy for Discriminator\n        positive_y = np.ones((data.batch_size, 1), dtype=np.float32)\n        negative_y = -positive_y\n        dummy_y = np.zeros((data.batch_size, 1), dtype=np.float32)\n\n        # total number of batches in one epoch\n        total_batch = int(data.size / data.batch_size)\n        print(f'batch_size={data.batch_size} * total_batch={total_batch}')\n\n        save_path = lambda type, epoch: os.path.join(config.MODEL_DIR, f\"fusion_{type}Epoch{epoch}.h5\")\n\n        if skip_to_after_epoch:\n            start_epoch = skip_to_after_epoch + 1\n            print(f\"Loading weights from epoch {skip_to_after_epoch}\")\n            self.combined.load_weights(save_path(\"combined\", skip_to_after_epoch))\n            self.fusion_discriminator.load_weights(save_path(\"discriminator\", skip_to_after_epoch))\n        else:\n            start_epoch = 0\n\n        for epoch in range(start_epoch, config.NUM_EPOCHS):\n            for batch in tqdm(range(total_batch)):\n                train_batch = data.generate_batch()\n                resized_l = train_batch.resized_images.l\n                resized_ab = train_batch.resized_images.ab\n\n                # GT vgg\n                predictVGG = VGG_modelF.predict(np.tile(resized_l, [1, 1, 1, 3]))\n\n                # train generator\n                g_loss = self.combined.train_on_batch(\n                    [resized_l, train_batch.instances.l, train_batch.instances.bbox, train_batch.instances.mask],\n                    [resized_ab, train_batch.instances.ab, predictVGG, positive_y])\n                # train discriminator\n                d_loss = self.discriminator_model.train_on_batch(\n                    [resized_l, resized_ab, train_batch.instances.l, train_batch.instances.bbox,\n                     train_batch.instances.mask],\n                    [positive_y, negative_y, dummy_y])\n\n                # update log files\n                write_log(self.callback, self.train_names, g_loss, (epoch * total_batch + batch + 1))\n                write_log(self.callback, self.disc_names, d_loss, (epoch * total_batch + batch + 1))\n\n                if batch % 10 == 0:\n                    print(\n                        f\"[Epoch {epoch}] [Batch {batch}/{total_batch}] [generator loss: {g_loss[0]:08f}] [discriminator loss: {d_loss[0]:08f}]\")\n\n            print('Saving models...')\n            self.combined.save(save_path(\"combined\", epoch))\n            self.fusion_discriminator.save(save_path(\"discriminator\", epoch))\n            print('Models saved.')\n\n            print('Sampling test images...')\n            # sample images after each epoch\n            self.sample_images(test_data, epoch, config)\n\n    def sample_images(self, test_data: Data, epoch, config):\n        total_batch = int(ceil(test_data.size / test_data.batch_size))\n        for _ in range(total_batch):\n            # load test data\n            test_batch = test_data.generate_batch()\n\n            # predict AB channels\n            fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15, fg_conv2d_17, up_sampling2d_3 = self.foreground_generator.predict(\n                test_batch.instances.l)\n\n            fusion_img_pred_ab, _ = self.fusion_generator.predict(\n                [test_batch.resized_images.l, fg_model_3, fg_conv2d_11, fg_conv2d_13, fg_conv2d_15, fg_conv2d_17,\n                 test_batch.instances.bbox, test_batch.instances.mask])\n\n            # print results\n            for i in range(test_data.batch_size):\n                original_full_img = test_batch.images.full[i]\n                height, width, _ = original_full_img.shape\n                pred_ab = cv2.resize(deprocess_float2int(fusion_img_pred_ab[i]), (width, height))\n                reconstruct_and_save(test_batch.images.l[i], pred_ab, f'epoch{epoch}_{test_batch.file_names[i]}',\n                                     config)\n\n\nif __name__ == '__main__':\n    config_name = 'fusion_2obj_huber'\n    if config_name == 'fusion_2obj':\n        config = FirstTestConfig('fusion_2obj', ROOT_DIR='../../../')\n        train_data = Data(config.TRAIN_DIR, config)\n        test_data = Data(config.TEST_DIR, config)\n        with prepare_logger(train_data.batch_size, config) as logger:\n            logger.write(str(datetime.now()) + \"\\n\")\n\n            # Scenario 1: fresh start\n            # print(\"Initializing Model...\")\n            # colorizationModel = FusionModel(config, load_weight_path='../../../weights/chroma_gan/imagenet.h5')\n            # print(\"Model Initialized!\")\n            # print(\"Start training\")\n            # colorizationModel.train(train_data, test_data, logger, config=config)\n\n            # Scenario 2: pick up where we left\n            print(\"Initializing Model...\")\n            colorizationModel = FusionModel(config)\n            print(\"Model Initialized!\")\n            print(\"Start training\")\n            colorizationModel.train(train_data, test_data, logger, config=config, skip_to_after_epoch=1)\n    elif config_name == 'fusion_2obj_huber':\n        config = FirstTestConfig('fusion_2obj_huber', ROOT_DIR='../../../')\n        train_data = Data(config.TRAIN_DIR, config)\n        test_data = Data(config.TEST_DIR, config)\n        with prepare_logger(train_data.batch_size, config) as logger:\n            logger.write(str(datetime.now()) + \"\\n\")\n\n            # Scenario 1: fresh start\n            print(\"Initializing Model...\")\n            colorizationModel = FusionModel(config, load_weight_path='../../../weights/chroma_gan/imagenet.h5',\n                                            ab_loss=lambda y_true, y_pred: tf.reduce_mean(huber_loss(y_true, y_pred, delta=0.5)))\n            print(\"Model Initialized!\")\n            print(\"Start training\")\n            colorizationModel.train(train_data, test_data, logger, config=config)\n\n            # Scenario 2: pick up where we left\n            # print(\"Initializing Model...\")\n            # colorizationModel = FusionModel(config)\n            # print(\"Model Initialized!\")\n            # print(\"Start training\")\n            # colorizationModel.train(train_data, test_data, logger, config=config, skip_to_after_epoch=1)\n","sub_path":"chroma_instance/source/chroma_instance/model/fusion_with_instance.py","file_name":"fusion_with_instance.py","file_ext":"py","file_size_in_byte":28443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"630351630","text":"# -*- coding: utf-8 -*-\n'''\nReturn salt data via pushover (http://www.pushover.net)\n\n.. versionadded:: Boron\n\nThe following fields can be set in the minion conf file::\n\n    pushover.user (required)\n    pushover.token (required)\n    pushover.title (optional)\n    pushover.device (optional)\n    pushover.priority (optional)\n    pushover.expire (optional)\n    pushover.retry (optional)\n    pushover.profile (optional)\n\nAlternative configuration values can be used by prefacing the configuration.\nAny values not found in the alternative configuration will be pulled from\nthe default location::\n\n    alternative.pushover.user\n    alternative.pushover.token\n    alternative.pushover.title\n    alternative.pushover.device\n    alternative.pushover.priority\n    alternative.pushover.expire\n    alternative.pushover.retry\n\nPushOver settings may also be configured as::\n\n    pushover:\n        user: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        token: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        title: Salt Returner\n        device: phone\n        priority: -1\n        expire: 3600\n        retry: 5\n\n    alternative.pushover:\n        user: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        token: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        title: Salt Returner\n        device: phone\n        priority: 1\n        expire: 4800\n        retry: 2\n\n    pushover_profile:\n        token: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n\n    pushover:\n        user: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        profile: pushover_profile\n\n    alternative.pushover:\n        user: xxxxxxxxxxxxxxxxxxxxxxxxxxxxxx\n        profile: pushover_profile\n\n  To use the PushOver returner, append '--return pushover' to the salt command. ex:\n\n  .. code-block:: bash\n\n    salt '*' test.ping --return pushover\n\n  To use the alternative configuration, append '--return_config alternative' to the salt command. ex:\n\n    salt '*' test.ping --return pushover --return_config alternative\n'''\nfrom __future__ import absolute_import\n\n# Import Python libs\nimport pprint\nimport logging\n\n# Import 3rd-party libs\nimport requests\nfrom requests.exceptions import ConnectionError\n# pylint: disable=import-error,no-name-in-module,redefined-builtin\nfrom salt.ext.six.moves.urllib.parse import urljoin as _urljoin  # pylint: disable=import-error,no-name-in-module\nimport salt.ext.six.moves.http_client\n# pylint: enable=import-error,no-name-in-module,redefined-builtin\n\n# Import Salt Libs\nimport salt.returners\n\nlog = logging.getLogger(__name__)\n\n__virtualname__ = 'pushover'\n\n\ndef _get_options(ret=None):\n    '''\n    Get the pushover options from salt.\n    '''\n\n    defaults = {'priority': '0'}\n\n    attrs = {'pushover_profile': 'profile',\n             'user': 'user',\n             'device': 'device',\n             'token': 'token',\n             'priority': 'priority',\n             'title': 'title',\n             'api_version': 'api_version',\n             'expire': 'expire',\n             'retry': 'retry',\n             'sound': 'sound',\n             }\n\n    profile_attr = 'pushover_profile'\n\n    profile_attrs = {'user': 'user',\n                     'device': 'device',\n                     'token': 'token',\n                     'priority': 'priority',\n                     'title': 'title',\n                     'api_version': 'api_version',\n                     'expire': 'expire',\n                     'retry': 'retry',\n                     'sound': 'sound',\n                     }\n\n    _options = salt.returners.get_returner_options(__virtualname__,\n                                                   ret,\n                                                   attrs,\n                                                   profile_attr=profile_attr,\n                                                   profile_attrs=profile_attrs,\n                                                   __salt__=__salt__,\n                                                   __opts__=__opts__,\n                                                   defaults=defaults)\n    return _options\n\n\ndef __virtual__():\n    '''\n    Return virtual name of the module.\n\n    :return: The virtual name of the module.\n    '''\n    return __virtualname__\n\n\ndef _query(function,\n           token=None,\n           api_version='1',\n           method='POST',\n           data=None,\n           query_params=None):\n    '''\n    PushOver object method function to construct and execute on the API URL.\n\n    :param token:       The PushOver api key.\n    :param api_version: The PushOver API version to use, defaults to version 1.\n    :param function:    The PushOver api function to perform.\n    :param method:      The HTTP method, e.g. GET or POST.\n    :param data:        The data to be sent for POST method.\n    :return:            The json response from the API call or False.\n    '''\n    headers = {}\n\n    if query_params is None:\n        query_params = {}\n\n    if data is None:\n        data = {}\n\n    ret = {'message': '',\n           'res': True}\n\n    pushover_functions = {\n        'message': {\n            'request': 'messages.json',\n            'response': 'status',\n        },\n        'validate_user': {\n            'request': 'users/validate.json',\n            'response': 'status',\n        },\n        'validate_sound': {\n            'request': 'sounds.json',\n            'response': 'status',\n        },\n    }\n\n    if not token:\n        try:\n            options = __salt__['config.option']('pushover')\n            if not token:\n                token = options.get('token')\n        except (NameError, KeyError, AttributeError):\n            log.error('No PushOver token found.')\n            ret['message'] = 'No PushOver token found.'\n            ret['res'] = False\n            return ret\n\n    api_url = 'https://api.pushover.net'\n    base_url = _urljoin(api_url, api_version + '/')\n    path = pushover_functions.get(function).get('request')\n    url = _urljoin(base_url, path, False)\n\n    try:\n        result = requests.request(\n            method=method,\n            url=url,\n            headers=headers,\n            params=query_params,\n            data=data,\n            verify=True,\n        )\n    except ConnectionError as e:\n        ret['message'] = e\n        ret['res'] = False\n        return ret\n\n    if result.status_code == salt.ext.six.moves.http_client.OK:\n        result = result.json()\n        response = pushover_functions.get(function).get('response')\n        if response in result and result[response] == 0:\n            ret['res'] = False\n        ret['message'] = result\n        return ret\n    else:\n        try:\n            result = result.json()\n            if response in result and result[response] == 0:\n                ret['res'] = False\n            ret['message'] = result\n            return ret\n        except ValueError:\n            ret['res'] = False\n            ret['message'] = result\n            return ret\n\n\ndef _validate_sound(sound,\n                    token):\n    '''\n    Validate that the sound sent to Pushover exists.\n    :param sound:       The sound that we want to verify\n    :param token:       The PushOver token.\n    '''\n    parameters = dict()\n    parameters['token'] = token\n\n    response = _query(function='validate_sound',\n                      method='GET',\n                      query_params=parameters)\n\n    if response['res']:\n        if 'message' in response:\n            _message = response.get('message', '')\n            if 'status' in _message:\n                if _message.get('status', '') == 1:\n                    sounds = _message.get('sounds', '')\n                    if sound in sounds:\n                        return True\n                    else:\n                        log.info('Warning: {0} not a valid sound.'.format(sound))\n                        return False\n                else:\n                    log.info('Error: {0}'.format(''.join(_message.get('errors', ''))))\n    return False\n\n\ndef _validate_user(user,\n                   device,\n                   token):\n    '''\n    Validate that a Pushover user or group exists.\n    :param user:        The user or group name, either will work.\n    :param device:      The device for the user.\n    :param token:       The PushOver token.\n    '''\n    parameters = dict()\n    parameters['user'] = user\n    parameters['token'] = token\n    parameters['device'] = device\n\n    response = _query(function='validate_user',\n                      method='POST',\n                      data=parameters)\n\n    if response['res']:\n        if 'message' in response:\n            _message = response.get('message', '')\n            if 'status' in _message:\n                if _message.get('status', '') == 1:\n                    return True\n                else:\n                    log.info('Error: {0}'.format(''.join(_message.get('errors', ''))))\n    return False\n\n\ndef _post_message(user,\n                  device,\n                  message,\n                  title,\n                  priority,\n                  expire,\n                  retry,\n                  sound,\n                  api_version=1,\n                  token=None):\n    '''\n    Send a message to a Pushover user or group.\n    :param user:        The user or group to send to, must be key of user or group not email address.\n    :param message:     The message to send to the PushOver user or group.\n    :param title:       Specify who the message is from.\n    :param priority     The priority of the message, defaults to 0.\n    :param api_version: The PushOver API version, if not specified in the configuration.\n    :param notify:      Whether to notify the room, default: False.\n    :param token:       The PushOver token, if not specified in the configuration.\n    :return:            Boolean if message was sent successfully.\n    '''\n\n    if not _validate_user(user, device, token):\n        return\n\n    parameters = dict()\n    parameters['user'] = user\n    parameters['device'] = device\n    parameters['token'] = token\n    parameters['title'] = title\n    parameters['priority'] = priority\n    parameters['expire'] = expire\n    parameters['retry'] = retry\n    parameters['message'] = message\n\n    if sound and _validate_sound(sound, token):\n        parameters['sound'] = sound\n\n    result = _query(function='message',\n                    method='POST',\n                    data=parameters)\n\n    return result\n\n\ndef returner(ret):\n    '''\n    Send an PushOver message with the data\n    '''\n\n    _options = _get_options(ret)\n\n    user = _options.get('user')\n    device = _options.get('device')\n    token = _options.get('token')\n    title = _options.get('title')\n    priority = _options.get('priority')\n    expire = _options.get('expire')\n    retry = _options.get('retry')\n    sound = _options.get('sound')\n\n    if not user:\n        log.error('pushover.user not defined in salt config.')\n        return\n\n    if not token:\n        log.error('pushover.token not defined in salt config.')\n        return\n\n    if priority and priority == 2:\n        if not expire and not retry:\n            log.error('Priority 2 requires pushover.expire and pushover.retry options.')\n            return\n\n    message = ('id: {0}\\r\\n'\n               'function: {1}\\r\\n'\n               'function args: {2}\\r\\n'\n               'jid: {3}\\r\\n'\n               'return: {4}\\r\\n').format(\n                    ret.get('id'),\n                    ret.get('fun'),\n                    ret.get('fun_args'),\n                    ret.get('jid'),\n                    pprint.pformat(ret.get('return')))\n\n    result = _post_message(user=user,\n                           device=device,\n                           message=message,\n                           title=title,\n                           priority=priority,\n                           expire=expire,\n                           retry=retry,\n                           sound=sound,\n                           token=token)\n    if not result['res']:\n        log.info('Error: {0}'.format(result['message']))\n    return\n","sub_path":"salt/returners/pushover_returner.py","file_name":"pushover_returner.py","file_ext":"py","file_size_in_byte":11847,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"170040053","text":"# On importe les fonctions mathématiques\nfrom math import log, e, atan, pi, sin\n\n# On définit la fonction phi\ndef phi(x):\n    # Dans le cas x < -2\n    if x < -2:\n        return -2 * log(x, e) + 1\n\n    # Dans le cas x > 5\n    elif x > 5:\n        return atan(x) - pi\n\n    # Dans le cas -2 <= x <= 5\n    else:\n        return 4 * sin(x)\n","sub_path":"ds/ds2Arthur/phi.py","file_name":"phi.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"187073854","text":"from pprint import pprint\n\"\"\"\nThis program takes the output of the show arp command and turns it into\na table of ip address and the corresponding mac address that goesn with it.\n\"\"\"\n\nshow_arp = \"\"\"\nProtocol  Address          Age (min)  Hardware Addr   Type   Interface\nInternet  10.10.20.48             -   0050.56bb.e99c  ARPA   GigabitEthernet1\nInternet  10.10.20.200           14   0050.56bb.8be2  ARPA   GigabitEthernet1\nInternet  10.10.20.254            0   0896.ad9e.444c  ARPA   GigabitEthernet1\n\n\"\"\"\n\narp_table = dict()                                 #Crate an empty dictionary to hold the values\nfor Line in show_arp.splitlines():\n    if Line.lower().find(\"internet\", 0) == 0:           #For every line in show_arp, if you find internet at location 0\n        arp_table[Line[10:25].strip()] = Line[38:52]   #The arp_table dictionary will have a key of slice [10:25] and a value of slice [38:52]\nprint(\"\\n\\n-------ARP Table ---------------\")\npprint(arp_table)\n    ","sub_path":"week07_arp_table.py","file_name":"week07_arp_table.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"390949949","text":"\"\"\"\nA copy of nmrmath that will be used for speed testing and optimization\n\"\"\"\n\nimport numpy as np\n\nfrom scipy.linalg import eigh\nfrom scipy.sparse import kron, csc_matrix, lil_matrix\n\n# print and decorator strategy will be tried first:\nfrom functools import wraps\nimport time\n\n\ndef timefn(fn):\n    \"\"\"\n    Need to figure out decorators!\n    @wraps is needed 'to expose the function name and docstring to the caller\n    of the decorated function (otherwise, we would see the function name\n    and docstring for the decorator, not the function it decorates\"\"\"\n    @wraps(fn)\n    def measure_time(*args, **kwargs):\n        t1 = time.time()\n        result = fn(*args, **kwargs)\n        t2 = time.time()\n        print(\"@timefn:\" + fn.__name__ + \" took \" + str(t2 - t1)\n              + \" seconds\")\n        return result\n    return measure_time\n\n\ndef nlist(length):\n    \"\"\"\n    creates a list (length) long of empty lists.\n    This is probably redundant with a built-in python/numpy/scipy function,\n    so consider replacing in future.\n    Input:\n        :param length: number of empty lists in list\n    Returns:\n        a list of [] x length\n\n    \"\"\"\n    # noinspection PyUnusedLocal\n    return [[] for l in range(length)]\n\n\ndef popcount(n=0):\n    \"\"\"\n    Computes the popcount (binary Hamming weight) of integer n\n    input:\n        :param n: an integer\n    returns:\n        popcount of integer (binary Hamming weight)\n\n    \"\"\"\n    return bin(n).count('1')\n\n\n# noinspection PyShadowingNames\ndef is_allowed(m=0, n=0):\n    \"\"\"\n    determines if a transition between two spin states is allowed or forbidden.\n    The transition is allowed if one and only one spin (i.e. bit) changes\n    input: integers whose binary codes for a spin state\n        :param n:\n        :param m:\n    output: 1 = allowed, 0 = forbidden\n\n    \"\"\"\n    return popcount(m ^ n) == 1\n\n\n# noinspection PyPep8Naming\n@timefn\ndef transition_matrix(n):\n    \"\"\"\n    Creates a matrix of allowed transitions.\n    The integers 0-n, in their binary form, code for a spin state (alpha/beta).\n    The (i,j) cells in the matrix indicate whether a transition from spin state\n    i to spin state j is allowed or forbidden.\n    See the is_allowed function for more information.\n\n    input:\n        :param n: size of the n,n matrix (i.e. number of possible spin states)\n\n    :returns: a transition matrix that can be used to compute the intensity of\n    allowed transitions.\n\n    \"\"\"\n    T = csc_matrix((n, n))  # sparse matrix created\n    for i in range(n):\n        for j in range(n):\n            if is_allowed(i, j):\n                T[i, j] = 1\n    return T\n\n\n# noinspection PyShadowingNames\n@timefn\ndef hamiltonian(freqlist, couplings):\n    \"\"\"\n    Computes the spin Hamiltonian for spin-1/2 nuclei.\n    inputs for n nuclei:\n        :param freqlist: a list of frequencies in Hz of length n\n        :param couplings: a sparse n x n matrix of coupling constants in Hz\n    Returns: a sparse Hamiltonian matrix\n    \"\"\"\n    nspins = len(freqlist)\n    print('Defining unit matrices')\n    # Define Pauli matrices\n    # change below back to csr if no improvement\n    sigma_x = csc_matrix(np.matrix([[0, 1/2], [1/2, 0]]))\n    sigma_y = csc_matrix(np.matrix([[0, -1j/2], [1j/2, 0]]))\n    sigma_z = csc_matrix(np.matrix([[1/2, 0], [0, -1/2]]))\n    unit = csc_matrix(np.matrix([[1, 0], [0, 1]]))\n    print('Unit matrices defined')\n\n    print('Generating lists of Lx/y/z matrices')\n    # The following empty lists will be used to store the\n    # //insert description here\n\n    Lx = nlist(nspins)\n    Ly = nlist(nspins)\n    Lz = nlist(nspins)\n\n    for n in range(nspins):\n        Lx_current = 1; Ly_current = 1; Lz_current = 1\n        for k in range(nspins):\n            # Need to use scipy kron, not np.kron with sparse matrices\n            if k == n:  # Diagonal element\n\n                Lx_current = kron(Lx_current, sigma_x)\n                Ly_current = kron(Ly_current, sigma_y)\n                Lz_current = kron(Lz_current, sigma_z)\n            else:\n                Lx_current = kron(Lx_current, unit)\n                Ly_current = kron(Ly_current, unit)\n                Lz_current = kron(Lz_current, unit)\n\n        Lx[n] = Lx_current\n        Ly[n] = Ly_current\n        Lz[n] = Lz_current\n\n    print('Lx/y/z matrices compiled')\n    print('Calculating Hamiltonian')\n    # Hamiltonian operator\n    H = csc_matrix((2**nspins, 2**nspins))\n\n    # Zeeman interactions:\n    for n in range(nspins):\n        H = H + freqlist[n] * Lz[n]\n    print('Diagonal elements computed')\n    # Scalar couplings\n\n    # Testing with MATLAB discovered J must be /2.\n    # Believe it is related to the fact that in the simulation freqs are *2pi,\n    # but Js by pi only. Video is supposed to explain why.\n\n    for n in range(nspins):\n        for k in range(nspins):\n            if n != k:\n                # noinspection PyTypeChecker\n                H += (couplings[n, k] / 2) * (Lx[n] * Lx[k] +\n                                              Ly[n] * Ly[k] +\n                                              Lz[n] * Lz[k])\n    print('Hamiltonian computed')\n    return H\n\n\n# noinspection PyPep8Naming\n@timefn\ndef simsignals(H, nspins):\n    \"\"\"\n    Solves the spin Hamiltonian H and returns a list of (frequency, intensity)\n    tuples. Nuclei must be spin-1/2.\n    Inputs:\n        :param H: a sparse spin Hamiltonian\n        :param nspins: number of nuclei\n    Returns:\n        peaklist: a list of (frequency, intensity) tuples.\n\n\n    \"\"\"\n    print('Calculating eigensystem')\n    # Using eigh so that answers have only real components and no residual small\n    # j components b/c of rounding errors\n\n    E, V = eigh(H.todense())  # V will be eigenvectors, v will be frequencies\n    print('Eigensystem solved; converting eigenvectors to sparse')\n    V = np.asmatrix(V.real)\n    V = csc_matrix(V)         # Consider refactoring if this is confusing\n    print('V converted to csc matrix.')\n\n    print('Calculating the transition matrix')\n    T = transition_matrix(2**nspins)\n    print('Transition matrix calculated')\n\n    print('Collecting spectrum')\n    spectrum = []\n    for i in range(2**nspins):\n        for j in range(i, 2**nspins):\n            if j != i:\n                intensity = (V[:, i].T * T * V[:, j])[0, 0]**2\n                # apparently returns 2D matrix\n                # consider refactor to float\n                if intensity > 0.01:\n                    v = abs(E[i] - E[j])\n                    spectrum.append((v, intensity))\n    print('Spectrum obtained.')\n    return spectrum\n\n\n# noinspection PyUnreachableCode,PyPep8Naming\n@timefn\ndef nspinspec(freqs, couplings):\n    \"\"\"\n    Function that calculates a spectrum for n spin-half nuclei.\n    Inputs:\n        :param freqs: a list of n nuclei frequencies in Hz\n        :param couplings: an n x n sparse matrix of couplings in Hz. The order\n        of nuclei in the list corresponds to the column and row order in the\n        matrix, e.g. couplings[0][1] and [1]0] are the J coupling between\n        the nuclei of freqs[0] and freqs [1].\n    \"\"\"\n    nspins = len(freqs)\n    H = hamiltonian(freqs, couplings)\n    return simsignals(H, nspins)\n\n\nif __name__ == '__main__':\n    from nspin import reich_list\n    from nmrplot import nmrplot as nmrplt\n\n    test_freqs, test_couplings = reich_list()[7]\n    test_spectrum = nspinspec(test_freqs, test_couplings)\n    nmrplt(test_spectrum, y=12)\n","sub_path":"history/nmprint.py","file_name":"nmprint.py","file_ext":"py","file_size_in_byte":7360,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"270012249","text":"# Licensed under a 3-clause BSD style license - see LICENSE.rst\nfrom __future__ import print_function, division\nfrom astropy.tests.helper import pytest\nfrom .. import simulate\n\ntry:\n    import scipy\n    HAS_SCIPY = True\nexcept ImportError:\n    HAS_SCIPY = False\n\n\n@pytest.fixture\ndef example_table():\n    from ..spatial import YK04\n    from ..velocity import H05\n    nsources = 42\n    max_age = 1e6\n    return simulate.make_cat_gal(nsources=nsources, rad_dis=YK04, vel_dis=H05, max_age=max_age)\n\n\ndef has_columns(table, names):\n    \"\"\"Helper function to check if a table has a list of columns.\"\"\"\n    return set(names).issubset(table.colnames)\n\n\ndef test_make_cat_cube():\n    nsources = 100\n    table = simulate.make_cat_cube(nsources=nsources)\n    assert len(table) == nsources\n\n\ndef test_make_cat_gal():\n    from ..spatial import YK04\n    from ..velocity import H05\n    nsources = 42\n    max_age = 1e6\n\n    table = simulate.make_cat_gal(nsources=nsources, rad_dis=YK04, vel_dis=H05, max_age=max_age)\n    assert len(table) == nsources\n\n\ndef test_add_par_snr(example_table):\n    table = simulate.add_par_snr(example_table)\n    assert len(table) == len(example_table)\n    assert has_columns(table, ['E_SN'])\n\n\ndef test_add_par_psr(example_table):\n    table = simulate.add_par_psr(example_table)\n    assert len(table) == len(example_table)\n    assert has_columns(table, ['P0'])\n\n\n@pytest.mark.skipif('not HAS_SCIPY')\ndef test_add_par_pwn(example_table):\n    # To compute PWN parameters we need PSR and SNR parameters first\n    table = simulate.add_par_snr(example_table)\n    table = simulate.add_par_psr(table)\n    table = simulate.add_par_pwn(table)\n    assert len(table) == len(example_table)\n    assert has_columns(table, ['r_out_PWN'])\n\n\n@pytest.mark.skipif('not HAS_SCIPY')\ndef test_add_par_obs(example_table):\n    table = simulate.add_par_snr(example_table)\n    table = simulate.add_par_psr(table)\n    table = simulate.add_par_pwn(table)\n    table = simulate.add_observed_parameters(table)\n    table = simulate.add_par_obs(table)\n    assert len(table) == len(example_table)\n    assert has_columns(table, ['ext_in_SNR'])\n\n\ndef test_add_observed_parameters(example_table):\n    table = simulate.add_cylindrical_coordinates(example_table)\n    assert len(table) == len(example_table)\n    assert has_columns(table, ['r', 'phi'])\n","sub_path":"gammapy/astro/population/tests/test_simulate.py","file_name":"test_simulate.py","file_ext":"py","file_size_in_byte":2327,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"306837844","text":"from .first_order_lag import FirstOrderLagConfigModule\nfrom .two_wheeled import TwoWheeledConfigModule\nfrom .cartpole import CartPoleConfigModule\n\ndef make_config(args):\n    \"\"\"\n    Returns:\n        config (ConfigModule class): configuration for the each env\n    \"\"\"\n    if args.env == \"FirstOrderLag\":\n        return FirstOrderLagConfigModule()\n    elif args.env == \"TwoWheeledConst\" or args.env == \"TwoWheeledTrack\":\n        return TwoWheeledConfigModule()\n    elif args.env == \"CartPole\":\n        return CartPoleConfigModule()","sub_path":"PythonLinearNonlinearControl/configs/make_configs.py","file_name":"make_configs.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"263465216","text":"from __future__ import unicode_literals, absolute_import\n\nfrom zipfile import ZipFile, ZIP_DEFLATED\nimport re\n\nfrom django.conf.urls import url\nfrom django.contrib import admin\nfrom django.core.files.base import ContentFile\nfrom django.shortcuts import get_object_or_404, HttpResponse\nfrom django.template import Context, loader\nfrom django.utils.html import mark_safe\nfrom django.utils.six import b\nfrom django.utils.timezone import now, get_default_timezone\n\nfrom mezzanine.core.admin import StackedDynamicInlineAdmin, TabularDynamicInlineAdmin\n\nfrom .models import Project, Track, Comment, FinalFile\n\nTZ = get_default_timezone()\n\n\ndef to_folder_name(value):\n    \"\"\"\n    Only allow alphanumeric characters, dashes, and spaces.\n    \"\"\"\n    value = re.sub(r\"[^ \\w-]\", \"\", value).strip()\n    return value if len(value) else \"unknown_name\"\n\n\ndef serve_tracks_as_zipfile(request, pk):\n    \"\"\"\n    Create a Zip archive with all the Tracks in a Project.\n    The Tracks will be organized in folders according to Songs and Groups.\n    Based on https://github.com/thibault/django-zipview/\n    \"\"\"\n    project = get_object_or_404(Project, pk=pk)\n    timestamp = now().astimezone(TZ).strftime(\"%Y-%m-%d %H-%M-%S\")\n    name = \"%s %s.zip\" % (to_folder_name(project.title), timestamp)\n    temp_file = ContentFile(b(\"\"), name=name)\n\n    with ZipFile(temp_file, mode=\"w\", compression=ZIP_DEFLATED) as zip_file:\n        for track in Track.objects.filter(group__song__project=project):\n            path = \"{}/{}/{}\".format(\n                to_folder_name(track.group.song.title),\n                to_folder_name(track.group.title),\n                track.file.name.split(\"/\")[-1]\n            )\n            zip_file.writestr(path, track.file.read())\n\n    file_size = temp_file.tell()\n    temp_file.seek(0)\n\n    response = HttpResponse(temp_file, content_type=\"application/zip\")\n    response[\"Content-Disposition\"] = \"attachment; filename=\\\"%s\\\"\" % name\n    response[\"Content-Length\"] = file_size\n    return response\n\n\nclass CommentInlineAdmin(StackedDynamicInlineAdmin):\n    model = Comment\n    fields = [\"created\", \"author\", \"content\", \"attachment\"]\n    readonly_fields = [\"created\", \"author\"]\n\n\nclass FinalFileInlineAdmin(TabularDynamicInlineAdmin):\n    model = FinalFile\n    fields = [\"created\", \"title\", \"attachment\"]\n    readonly_fields = [\"created\"]\n\n\n@admin.register(Project)\nclass ProjectAdmin(admin.ModelAdmin):\n    inlines = [FinalFileInlineAdmin, CommentInlineAdmin]\n    ordering = [\"priority\", \"-created\"]\n    date_hierarchy = \"created\"\n    list_display = [\"title\", \"owner\", \"created\", \"status\", \"priority\"]\n    list_editable = [\"priority\"]\n    list_filter = [\"status\"]\n    search_fields = [\"title\", \"owner__username\"]\n    readonly_fields = [\"created\", \"updated\", \"track_browser\"]\n    fieldsets = (\n        (None, {\n            \"fields\": [\"title\", \"owner\", \"created\", \"updated\", \"status\", \"priority\"],\n        }),\n        (\"Tracks\", {\n            \"fields\": [\"track_browser\"],\n            \"classes\": (\"collapse-closed\",)\n        }),\n    )\n\n    class Media:\n        \"\"\"\n        Include our style and script customizations for the Project admin.\n        \"\"\"\n        css = {\n            \"all\": (\"admin/mixing/styles.css\",),\n        }\n        js = (\"admin/mixing/scripts.js\",)\n\n    def save_formset(self, request, form, formset, change):\n        \"\"\"\n        Populate the author field on inline Comments since it's marked as read-only.\n        \"\"\"\n        if formset.model is Comment:\n            comments = formset.save(commit=False)\n            for comment in comments:\n                if not comment.author_id:\n                    comment.author = request.user\n                comment.save()\n            formset.save_m2m()\n        else:\n            super(ProjectAdmin, self).save_formset(request, form, formset, change)\n\n    def get_urls(self):\n        \"\"\"\n        Add our custom admin views.\n        \"\"\"\n        info = self.model._meta.app_label, self.model._meta.model_name\n        default_urls = super(ProjectAdmin, self).get_urls()\n        urls = [\n            url(\n                r\"^(?P<pk>[0-9]+)/download/$\",\n                self.admin_site.admin_view(serve_tracks_as_zipfile),\n                name=\"%s_%s_download\" % info\n            ),\n        ]\n        return urls + default_urls\n\n    def track_browser(self, project=None):\n        \"\"\"\n        Generates a collapsible tree of Songs / Groups / Tracks.\n        \"\"\"\n        template = loader.get_template(\"admin/mixing/includes/track_browser.html\")\n        context = Context({\"project\": project})\n        output = template.render(context)\n        # Remove all newlines because Django converts them into <br>\n        nonewlines = re.sub(r\"[\\n\\r\\t]+\", \"\", output)\n        return mark_safe(nonewlines)\n","sub_path":"mixing/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"628866589","text":"import numpy as np\n\n# Only works for two classes\n\ndef get_fold_indices(fold_index, fold_size, num_folds, N):\n    '''Given a fold index and the size, number of folds and number of objects in the dataset,\n    returns the indices of the objects in the fold. These indices can be used to obtain the\n    measurements of the objects in the data matrix.'''\n    \n    if fold_index<num_folds-1:\n        return list(range(fold_index*fold_size, (fold_index+1)*fold_size))\n    else:\n        return list(range(fold_index*fold_size, N))\n    \ndef get_splits(Ns, Nl, folds):\n    '''Given the number of objects in the smaller class (Ns) and in the larger class (Nl) and the number of folds,\n    returns the indices of all the folds used in the unbalanced cross-validation.'''\n\n    fold_size = int(round(Ns/folds))\n    if (Ns-fold_size*folds) > ((fold_size+1)*folds-Ns):\n        fold_size += 1\n\n    if fold_size == 0:\n        fold_size = 1\n        \n    num_folds_larger = int(Nl/fold_size)\n    if (Nl-fold_size*num_folds_larger) > (fold_size*(num_folds_larger+1)-Nl):\n        num_folds_larger += 1\n\n    num_folds_test = np.array([num_folds_larger//folds]*folds)\n    if num_folds_larger%folds>0:\n        num_folds_test[:num_folds_larger%folds] += 1\n    cumsum_num_folds_test = [0] + np.cumsum(num_folds_test).tolist()\n\n    #print(folds, fold_size, num_folds_larger)\n\n    fold_inds_smaller = set(range(folds))\n    fold_inds_larger = set(range(num_folds_larger))\n    splits = []\n    for fold_idx in range(folds):\n\n        fold_inds_smaller_test = fold_idx\n        fold_inds_smaller_train = fold_inds_smaller - set([fold_idx])\n\n        fold_inds_larger_test = list(range(cumsum_num_folds_test[fold_idx],cumsum_num_folds_test[fold_idx+1]))\n        fold_inds_larger_train_pool = list(fold_inds_larger - set(fold_inds_larger_test))\n        fold_inds_larger_train = sorted(np.random.choice(fold_inds_larger_train_pool, folds-1, False))\n\n        inds_smaller_test = get_fold_indices(fold_inds_smaller_test, fold_size, folds, Ns)\n        inds_smaller_train = []\n        for idx in fold_inds_smaller_train:\n            inds_smaller_train.extend(get_fold_indices(idx, fold_size, folds, Ns))\n\n        inds_larger_test = []\n        for idx in fold_inds_larger_test:\n            inds_larger_test.extend(get_fold_indices(idx, fold_size, num_folds_larger, Nl))\n\n        inds_larger_train = []\n        for idx in fold_inds_larger_train:\n            inds_larger_train.extend(get_fold_indices(idx, fold_size, num_folds_larger, Nl))\n            \n        splits.append([inds_smaller_train, inds_larger_train, inds_smaller_test, inds_larger_test])\n\n        '''print(fold_inds_smaller_train)\n        print(inds_smaller_train)\n        print(fold_inds_smaller_test)\n        print(inds_smaller_test)\n        print(fold_inds_larger_train)\n        print(inds_larger_train)\n        print(fold_inds_larger_test)\n        print(inds_larger_test)\n        print('\\n')'''\n        \n    return splits\n\ndef get_fold(data, classes, num_folds):\n    '''Yields the folds of an unbalanced cross-validation.\n    \n    Parameters\n    ----------\n    data : numpy array\n        Data matrix where each column represents a feature and each row an object.\n    classes : numpy array\n        Array containing the classes for each row of `data`. Length should be the same as\n        the number of rows in `data`.\n    num_folds : int\n        Number of folds for cross-validation\n    Returns\n    -------\n        Generator containing for each call of the function:\n    data_train : numpy array\n        Data used for training the classifier. Contains the same number of objects from each class.\n    classes_train : numpy array\n        Classes of the training data\n    data_test : numpy array\n        Data used for testing the classifier. May contain different number of objects from each class.\n    classes_test : numpy array\n        Classes of the test data\n    indices_test : list\n        Indices of the test data in matrix `data`\n    '''\n    \n    data = np.array(data)\n    classes = np.array(classes)\n\n    num_elem_in_class = np.bincount(classes)\n    smaller_class = np.argmin(num_elem_in_class)\n    larger_class = 1 - smaller_class\n\n    inds_smaller = np.nonzero(classes==smaller_class)[0]\n    inds_larger = np.nonzero(classes==1-smaller_class)[0]\n\n    Ns = len(inds_smaller)\n    Nl = len(inds_larger)\n\n    splits = get_splits(Ns, Nl, num_folds)\n\n    data_smaller = data[inds_smaller]\n    data_larger = data[inds_larger]\n\n    perm_smaller = np.random.permutation(Ns)\n    perm_larger = np.random.permutation(Nl)\n    data_smaller = data_smaller[perm_smaller]\n    data_larger = data_larger[perm_larger]\n\n    for split in splits:\n\n        inds_smaller_train, inds_larger_train, inds_smaller_test, inds_larger_test = split\n        data_train = np.concatenate((data_smaller[inds_smaller_train], data_larger[inds_larger_train]), axis=0)\n        classes_train = np.concatenate(([smaller_class]*len(inds_smaller_train), \n                                        [larger_class]*len(inds_larger_train)))\n\n        data_test = np.concatenate((data_smaller[inds_smaller_test], data_larger[inds_larger_test]), axis=0)\n        classes_test = np.concatenate(([smaller_class]*len(inds_smaller_test), \n                                        [larger_class]*len(inds_larger_test)))\n\n        or_ind_smaller = inds_smaller[perm_smaller[inds_smaller_test]].tolist()\n        or_ind_larger = inds_larger[perm_larger[inds_larger_test]].tolist()\n        indices_test = or_ind_smaller+or_ind_larger\n        \n        yield data_train, classes_train, data_test, classes_test, indices_test","sub_path":"unbalanced_cv.py","file_name":"unbalanced_cv.py","file_ext":"py","file_size_in_byte":5584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"231310752","text":"\"\"\"Second stage implementation of adventure game.\nImplementation of the game story, choices and outcomes\"\"\"\n\nfrom sys import exit\nfrom os import listdir\n\n\ndef main_menu():\n    print(\"***Welcome to the Journey to Mount Qaf***\\n\",\n          \"1- Press key '1' or type 'start' to start a new game\",\n          \"2- Press key '2' or type 'load' to load your progress\",\n          \"3- Press key '3' or type 'quit' to quit the game\", sep='\\n')\n\n\nclass Game:\n    def __init__(self):\n        self.input_list = ['1', '2', '3', 'start', 'load', 'quit']\n        self.character = []\n        self.inventory = []\n        self.difficulty = ''\n        self.user_name = ''\n        self.lives = 5\n        self.level = 1\n        self.progress = 0\n        with open('story/story.txt') as s, open('story/choices.txt') as c, open('story/outcomes.txt') as o:\n            self.story = [i.strip() for i in s.read().split('+')]\n            self.choices = c.read().split('\\n')\n            self.outcomes = [i.strip() for i in o.read().split('*')]\n\n    def menu_input(self):\n        \"\"\"Taking input from user and checking if it valid or not,\n        Then checking user's choice and moving to next step accordingly\"\"\"\n        main_menu()\n        user_input = input(\"> \").lower()\n        if user_input in self.input_list:\n            if user_input == '1' or user_input == 'start':\n                print(\"Starting a new game...\")\n                self.creating_new_character()\n            elif user_input == '2' or user_input == 'load':\n                for f in listdir('saves'):\n                    print(f[:-4])\n                print(\"Type your username\")\n                self.user_name = input()\n                with open(f\"saves/{self.user_name}.txt\") as g:\n                    game = g.readlines()\n                    print(game)\n                    for i in range(4):\n                        print(game[i].strip())\n                    self.character = game[0]\n                    self.inventory = game[1]\n                    self.difficulty = game[2].split()[0]\n                    self.level = game[3]\n                    self.progress = int(game[4])\n                self.game_loop()\n            elif user_input == '3' or user_input == 'quit':\n                print(\"Goodbye!\")\n                exit()\n        else:\n            print(\"Unknown input! Please enter a valid one.\\n\")\n            self.menu_input()\n\n    def creating_new_character(self):\n        self.user_name = input(\"Enter a user name to save your progress or type '/b' to go back\\n> \")\n        if self.user_name == '/b':\n            print(\"Going back to menu...\")\n            self.menu_input()\n\n        character = [\"Name\", \"Species\", \"Gender\"]\n        print(\"Create your character:\")\n        for i, e in enumerate(character, start=1):\n            self.character.append(input(f\"{i}- {e} \").lower().capitalize())\n\n        inventory = [\"Favourite Snack\", \"A weapon for the journey\", \"A traversal tool\"]\n        print(\"Pack your bag for the journey:\")\n        for i, e in enumerate(inventory, start=1):\n            self.inventory.append(input(f\"{i}- {e} \").lower().capitalize())\n\n        difficulty_level = [\"Easy\", \"Medium\", \"Hard\"]\n        print(\"Choose your difficulty:\")\n        for i, e in enumerate(difficulty_level, start=1):\n            print(f\"{i}- {e}\")\n        difficulty_choice = input()\n        try:\n            self.difficulty = difficulty_level[int(difficulty_choice) - 1]\n        except ValueError:\n            self.difficulty = difficulty_choice.lower().capitalize()\n\n        print(\"Good luck on your journey!\",\n              f\"Your character: {self.character}\",\n              f\"Your inventory: {self.inventory}\",\n              f\"Difficulty: {self.difficulty}\", sep='\\n')\n        self.game_loop()\n\n    def game_loop(self):\n        while self.progress != 'break':\n            if self.story[self.progress] == \"Level 2\":\n                self.level = 2\n                print(\"You've found a safe spot to rest. Saving your progress...\")\n                with open(f'saves/{self.user_name}.txt', 'w') as save:\n                    print(self.character, self.inventory,\n                          (self.difficulty + ' ' + str(self.lives) + '\\n'),\n                          (str(self.level) + '\\n'), self.progress, file=save)\n            print(self.story[self.progress])\n            print(self.choices[self.progress])\n            print(self.outcomes[self.progress])\n            move = input()\n            if move == '/q':\n                print(\"Goodbye!\")\n                exit()\n                \"\"\"print(\"You sure you want to quit the game? y/n\\n> \")\n                inp = input()\n                if inp == 'y':\n                    print(\"Goodbye!\")\n                    exit()\"\"\"\n            elif move == '/i':\n                print(f\"Inventory: {self.inventory}\")\n            elif move == '/c':\n                print(f\"Your character: {self.character}.\\n\"\n                      f\"Lives remaining: {self.lives}\")\n            elif move == '/h':\n                print(\"Type the number of the option you want to choose.\",\n                      \"Commands you can use:\",\n                      \"/i => Shows inventory.\",\n                      \"/q => Exits the game.\",\n                      \"/c => Shows the character traits.\",\n                      \"/h => Shows help.\", sep='\\n')\n            else:\n                print(\"Unknown input! Please enter a valid one\")\n                pass\n            self.progress += 1\n\n\ngame_start = Game()\ngame_start.menu_input()\n","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":5491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"120623751","text":"import gamegridp\nimport logging\nimport sys\n\nclass MyGrid(gamegridp.GameGrid):\n    \"\"\"My Grid with custom setup method.\"\"\"\n\n    def setup(self):\n        self.set_image(img_path=\"images/soccer_green.jpg\")\n        player1 = Player(grid=self, location=(3, 3), img_path=\"images/char_blue.png\",\n                         img_action=\"do_nothing\")\n        player2 = Player(grid=self, location=(8, 2), img_path=\"images/char_blue.png\",\n                         img_action=\"do_nothing\")\n\n\nclass Player(gamegridp.Actor):\n    def setup(self):\n        pass\n\n    def act(self):\n        self.move()\n        if not self.is_valid_move():\n            self.turn_left()\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\nmy_grid = MyGrid(\"My Grid\", cell_size=12, columns=16, rows=16, margin=5, speed=60)\nmy_grid.show()\n","sub_path":"examples/basics/basicframework.py","file_name":"basicframework.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"236884398","text":"from math import ceil\r\ninputf = open('D-small-attempt1.in','r')\r\noutputf = open('fractal-small-attempt.txt','w')\r\nlines = inputf.readlines() \r\nt = int(lines[0])\r\nu = 0\r\nwhile t>0:\r\n    t-=1\r\n    u+=1\r\n    k, c, s = tuple(map(int, lines[u].split()))\r\n    if ceil(k/float(c))>s:\r\n        outputf.write(\"Case #%d: \" %u + \"IMPOSSIBLE\\n\")\r\n        continue\r\n    elif k<=s:\r\n        outputf.write(\"Case #%d: \" %u +\" \".join(map(str, range(1, k+1)))+\"\\n\")\r\n        continue\r\n    if c==1:\r\n        outputf.write(\"Case #%d: \" %u +\" \".join(map(str, range(1, k+1)))+\"\\n\")\r\n        continue\r\n    s = [0 for i in range(0, k, c)]\r\n    for i in range(len(s)):\r\n        a = c*i+1\r\n        f = a\r\n        b = c\r\n        while b>1:\r\n            if f>=k:\r\n                a *= k\r\n                b-=1\r\n                f+=1\r\n                continue\r\n            a = f+1+(a-1)*(k)\r\n            f+=1\r\n            b-=1\r\n        s[i] = str(a)\r\n    sol = ' '.join(s)\r\n    outputf.write(\"Case #%d: \" %(u) +sol+\"\\n\")\r\n    \r\ninputf.close()\r\noutputf.close()\r\n","sub_path":"solutions_5636311922769920_0/Python/myselfrishabh/fractal1.py","file_name":"fractal1.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"646573121","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nfrom rasa_core.actions.action import Action\nfrom rasa_core.events import SlotSet\n\nfrom rasa_core_sdk import Action\nfrom rasa_core_sdk.events import SlotSet\nimport requests\n\nclass EmployeeDetails(Action):\n\tdef name(self):\n\t\treturn 'API_action'\n\t\t\n\tdef run(self, dispatcher, tracker, domain):\n\t\t\n\t\tid = tracker.get_slot('empID')\n\t\trequest = tracker.get_slot('request')\n\t\tprint(\"Employee ID: \",id)\n\t\tprint(\"User Request: \",request)\n\t\tif request == 'details' or request == 'Details':\n\t\t\tApi = \"http://easygoprod.us-e2.cloudhub.io/api/employee?id={}\"\n\t\t\tresp = requests.get(Api.format(id))\n\t\t\tprint(resp)\n\t\t\tif resp.status_code != 200:\n\t\t\t\t# This means something went wrong.\n\t\t\t\traise ApiError('GET /tasks/ {}'.format(resp.status_code))\n\t\t\telse:\n\t\t\t\tjson_data = resp.json()\n\t\t\t\t#print(json_data)\n\t\t\t\tfullName = json_data[0]['ownerName']\n\t\t\t\temailID = json_data[0]['Email ID']\n\t\t\t\tdepartment = json_data[0]['Department']\n\t\t\t\tdateOfJoinning = json_data[0]['Date of joining']\n\t\t\t\tmobNo = json_data[0]['Mobile Phone']\n\t\t\t\treportingManager = json_data[0]['Reporting To']\n\t\t\t\tresponse = \"Employee Details:\\nEmployee_Name: {}\\nEmailID: {}\\nDepartment: {}\\nDateOfJoinning: {}\\nMobileNo: {}\\nReportingManager: {}\".format(fullName,emailID,department,dateOfJoinning,mobNo,reportingManager)\n\t\t\t\tdispatcher.utter_message(response)\t\t\t\n\t\telif request == 'timesheet' or request == 'Timesheet':\n\t\t\tApi = \"http://easygoprod.us-e2.cloudhub.io/api/employee/timesheet?userid={}&sheetstatus=approved\"\n\t\t\tresp = requests.get(Api.format(id))\n\t\t\tprint(resp)\n\t\t\tif resp.status_code != 200:\n\t\t\t\t# This means something went wrong.\n\t\t\t\traise ApiError('GET /tasks/ {}'.format(resp.status_code))\n\t\t\telse:\n\t\t\t\tjson_data = resp.json()\n\t\t\t\tjson_response = json_data['response']\n\t\t\t\tjson_result = json_response['result']\n\t\t\t\tstr1 = \"\"\n\t\t\t\tfor each in json_result:\n\t\t\t\t\tstr1=str1+\"WeeksLog: \"+each['timesheetName']+\" TotalHours: \"+each['totalHours']+\" ApprovedTotalHours: \"+each['approvedTotalHours']+\" Status: \"+each['status']+\"\\n\"\n\t\t\t\tdispatcher.utter_message(str1)\n\t\telif request == 'leaves' or request == 'Leaves':\n\t\t\tApi = \"http://easygoprod.us-e2.cloudhub.io/api/employee/leaves?userid={}\"\n\t\t\tresp = requests.get(Api.format(id))\n\t\t\tprint(resp)\n\t\t\tif resp.status_code != 200:\n\t\t\t\t# This means something went wrong.\n\t\t\t\traise ApiError('GET /tasks/ {}'.format(resp.status_code))\n\t\t\telse:\n\t\t\t\tjson_data = resp.json()\n\t\t\t\tjson_response = json_data['response']\n\t\t\t\tjson_result = json_response['result']\n\t\t\t\tstr2 = \"\"\n\t\t\t\tfor each in json_result:\n\t\t\t\t\tstr2=str2+\"LeaveName: \"+each['Name']+\" TotalLeaves: \"+str(each['PermittedCount'])+\" UsedLeaves: \"+str(each['AvailedCount'])+\" BalanceLeave: \"+str(each['BalanceCount'])+\"\\n\"\n\t\t\t\tdispatcher.utter_message(str2)\n\t\telse:\n\t\t\tdispatcher.utter_message('Else Condition is working')\n\t\t\n\n","sub_path":"action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"102702852","text":"from family import Doraemon_family, members\nfrom janken import pats, judge_result, jankenpon\nfrom tqdm import tqdm\nimport random\nimport pytest\n\n# ジャンケンの手の名前を取得\nguu = pats()[0]\ncho = pats()[1]\npaa = pats()[2]\n# 1/3の計算\nsanbun = 1 / 3\n# 1/3の前後10％\nsan_zen = sanbun - 0.1\nsan_go = sanbun + 0.1\n\nwin = judge_result()[0]\nlose = judge_result()[1]\n\n\n# 静香ちゃんが出す手の確率チェック\ndef test_shizuka():\n    # それぞれの手をカウントする変数\n    guu_count = 0\n    cho_count = 0\n    paa_count = 0\n    # 10000回実行した時のグーの結果をカウントする\n    for i in tqdm(range(10000)):\n        dora = Doraemon_family(\"\", \"\", \"\", \"\", \"\")\n        shizuka = dora.shizuka_hand()\n        # 出した手をそれぞれカウントしていく\n        if shizuka == guu:\n            guu_count += 1\n        if shizuka == cho:\n            cho_count += 1\n        if shizuka == paa:\n            paa_count += 1\n    # それぞれの手が1/3の前後10％ならOK\n    assert san_zen <= guu_count / 10000 <= san_go\n    assert san_zen <= cho_count / 10000 <= san_go\n    assert san_zen <= paa_count / 10000 <= san_go\n\n\n# ドラミちゃんが出す手の確率チェック\ndef test_dorami_guu():\n    # それぞれの手をカウントする変数\n    guu_count = 0\n    cho_count = 0\n    paa_count = 0\n    # 10000回実行した時のグーの結果をカウントする\n    for i in tqdm(range(10000)):\n        dora = Doraemon_family(\"\", \"\", \"\", \"\", \"\")\n        dorami = dora.dorami_hand()\n        # 出した手をそれぞれカウントしていく\n        if dorami == guu:\n            guu_count += 1\n        if dorami == cho:\n            cho_count += 1\n        if dorami == paa:\n            paa_count += 1\n    # グーは50%の前後10%ならOK\n    assert 0.4 <= guu_count / 10000 <= 0.6\n    # チョキは25%の前後10%ならOK\n    assert 0.15 <= cho_count / 10000 <= 0.35\n    # パーは25%の前後10%ならOK\n    assert 0.15 <= paa_count / 10000 <= 0.35\n\n\n# ドラえもんが必ずグーを出すチェック\ndef test_doraemon_guu():\n    # カウントする変数\n    count = 0\n    # 10000回実行した時のグーの結果をカウントする\n    for i in tqdm(range(10000)):\n        dora = Doraemon_family(\"\", \"\", \"\", \"\", \"\")\n        dorachan = dora.dorachan_hand()\n        if dorachan == guu:\n            count += 1\n    per = count / 10000\n    # 全てグーならならOK\n    assert per == 1.0\n\n\n# スネ夫が出す手のチェック\ndef test_suneo():\n    # それぞれの手をカウントする変数\n    guu_count = 0\n    cho_count = 0\n    paa_count = 0\n    # 10000回実行した時のグーの結果をカウントする\n    for i in tqdm(range(10000)):\n        dora = Doraemon_family(\"\", \"\", \"\", \"\", \"\")\n        suneo = dora.suneo_hand()\n        # 出した手をそれぞれカウントしていく\n        if suneo == guu:\n            guu_count += 1\n        if suneo == cho:\n            cho_count += 1\n        if suneo == paa:\n            paa_count += 1\n    # グーは0ならOK\n    assert guu_count == 0\n    # チョキは50%の前後10%ならOK\n    assert 0.4 <= cho_count / 10000 <= 0.6\n    # パーは50%の前後10%ならOK\n    assert 0.4 <= paa_count / 10000 <= 0.6\n\n\n# 通常ののび太出す手の確率チェック\ndef test_nobita():\n    # それぞれの手を���ウントする変数\n    guu_count = 0\n    cho_count = 0\n    paa_count = 0\n    # 10000回実行した時のグーの結果をカウントする\n    for i in tqdm(range(10000)):\n        dora = Doraemon_family(\"\", \"\", \"\", \"\", \"\")\n        nobita = dora.nobita_hand()\n        # 出した手をそれぞれカウントしていく\n        if nobita == guu:\n            guu_count += 1\n        if nobita == cho:\n            cho_count += 1\n        if nobita == paa:\n            paa_count += 1\n    # それぞれの手が1/3の前後10％ならOK\n    assert san_zen <= guu_count / 10000 <= san_go\n    assert san_zen <= cho_count / 10000 <= san_go\n    assert san_zen <= paa_count / 10000 <= san_go\n\n\n# 前回firstで勝ったのび太が前回と同じ手を出すかチェック\ndef test_nobita_first_win():\n    # カウントする変数\n    count = 0\n    # のび太の名前を取得\n    nobita_name = members()[1]\n    # グーチョキパーをランダムで出す\n    janken_randam = random.choice(pats())\n    # 前回と同じ手を必ず出しているか10000回チェック\n    for i in tqdm(range(10000)):\n        # 前回firstののび太が勝った後ののび太の手をランダムで作成\n        dora = Doraemon_family(nobita_name, \"\", win, janken_randam, \"\")\n        nobita = dora.nobita_hand()\n        # 前回の自分(first)と同じ手を出していればカウントする\n        if nobita == dora.first_before:\n            count += 1\n    # すべての勝負で同じ手を出していたらOK\n    assert count == 10000\n\n\n# 前回secondで相手を負けさせたのび太が前回と同じ手を出すかチェック\ndef test_nobita_second_lose():\n    # カウントする変数\n    count = 0\n    # のび太の名前を取得\n    nobita_name = members()[1]\n    # グーチョキパーをランダムで出す\n    janken_randam = random.choice(pats())\n    # 前回と同じ手を必ず出しているか10000回チェック\n    for i in tqdm(range(10000)):\n        # 前回secondののび太が勝った後ののび太の手をランダムで作成\n        dora = Doraemon_family(\"\", nobita_name, lose, \"\", janken_randam)\n        nobita = dora.nobita_hand()\n        # 前回の自分(second)と同じ手を出していればカウントする\n        if nobita == dora.second_before:\n            count += 1\n    # 100%ならOK\n    assert count == 10000\n\n\n# ジャンケンしたときの結果のチェック\ndef test_jankenpon():\n    assert jankenpon(\"グー\", \"グー\") == \"Draw\"\n    assert jankenpon(\"グー\", \"チョキ\") == \"Win\"\n    assert jankenpon(\"グー\", \"パー\") == \"Lose\"\n    assert jankenpon(\"チョキ\", \"グー\") == \"Lose\"\n    assert jankenpon(\"チョキ\", \"パー\") == \"Win\"\n    assert jankenpon(\"チョキ\", \"チョキ\") == \"Draw\"\n    assert jankenpon(\"パー\", \"グー\") == \"Win\"\n    assert jankenpon(\"パー\", \"チョキ\") == \"Lose\"\n    assert jankenpon(\"パー\", \"パー\") == \"Draw\"\n\n\n# ジャンケンをしたときの引数の値をチェック\ndef test_jankenpon_failure():\n    # 間違った文字列が入っていた場合\n    with pytest.raises(ValueError):\n        assert jankenpon(\"グー\", \"バー\")\n    # firstに数字が入っていた場合\n    with pytest.raises(TypeError):\n        assert jankenpon(1, \"チョキ\")\n    # secondに数字が入っていた場合\n    with pytest.raises(TypeError):\n        assert jankenpon(\"パー\", 1)\n","sub_path":"test_family.py","file_name":"test_family.py","file_ext":"py","file_size_in_byte":6757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"529971156","text":"from django.urls import path\n\nfrom . import views\n\nurlpatterns = [\n\tpath('',views.index,name='index'),\n\tpath('home',views.home,name='home'),\n\tpath('login',views.login,name='login'),\n\tpath('authenticate',views.authenticate,name='authenticate'),\n\tpath('finalize',views.finalize,name='finalize'),\n]\n\n","sub_path":"BasicApp/BackendDjango/productinfo/products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"65990954","text":"\"\"\"\n                                         _  _\n                                        | || |\n                     ___  _ __ ___    __| || |__\n                    / __|| '_ ` _ \\  / _` || '_ \\.\n                    \\__ \\| | | | | || (_| || |_) |\n                    |___/|_| |_| |_| \\__,_||_.__/\n\n\n        A simple map database of JSON \"table\" objects/files.\n\nThis module provides support for a simple file-system database of JSON\nfile tables that define/track the state, description, location, and other\nmeta data associated with received DICOM files.\n\nThree core map file/tables exist:\n\n    <dataLogDir>/patientMap/patientMap-<%PatientID>.json\n    <dataLogDir>/studyMap/studyMap-<%StudyInstanceUID>.json\n    <dataLogDir>/seriesMap/<%SeriesInstanceUID>/seriesMap-%%imageFile.json\n\nNB: This module is currently NOT THREAD SAFE as of April 2021! Collisions\noccur if multiple jobs try and read/write to the map files concurrently which\nhappens if scheduled in an async xinetd storescp pipeline.\n\nTypical safe calling spec for an xinetd controlled storescp is\n\n    storescp -od /tmp/data -pm -sp \\\n        -xcr \"/home/rudolphpienaar/src/pypx/bin/px-repack\n              --xcrdir #p --xcrfile #f --verbosity 0\"       \\\n        11113\n\n\"\"\"\n\nimport  os, os.path\nimport  json\nimport  pudb\nimport  datetime\n\nfrom    retry           import  retry\nfrom    pypx            import  repack\nimport  pfmisc\nimport  inspect\n\nclass SMDB():\n\n    def fileSpec_process(self):\n        \"\"\"\n        Parse a file spec -- used if the module is called from the CLI\n        on a specific DICOM file to update.\n        \"\"\"\n\n        if len(self.args.str_xcrdirfile):\n            self.args.str_xcrdir        = os.path.dirname(\n                                                self.args.str_xcrdirfile\n                                        )\n            self.args.str_xcrfile       = os.path.basename(\n                                                self.args.str_xcrdirfile\n                                        )\n        try:\n            self.args.str_xcrdir        = os.path.expanduser(\n                                                self.args.str_xcrdir\n                                        )\n        except:\n            pass\n        return len(self.args.str_xcrdir) and len(self.args.str_xcrfile)\n\n    def housingDirs_create(self):\n        \"\"\"\n        Create various directories to contain logging, data, and\n        maps.\n        \"\"\"\n        # Probably not scrictly speaking necessary to create\n        # dirs here, but for completeness sake...\n        l_vars      : list  = [\n                    'str_logDir',\n                    'str_dataDir',\n                    'str_patientMapDir',\n                    'str_studyMapDir',\n                    'str_seriesMapDir'\n                ]\n        for ns in [self, self.args]:\n            for k,v in vars(ns).items():\n                if k in l_vars: os.makedirs(v, exist_ok = True)\n\n    def debugloggers_create(self):\n        \"\"\"\n        Create the loggers.\n        \"\"\"\n        if 'verbosity'      not in self.args:\n            self.args.verbosity     = 0\n        if 'b_debug'        not in self.args:\n            self.args.b_debug       = False\n        if 'str_debugFile'  not in self.args:\n            self.args.str_debugFile = '/dev/null'\n        self.str_debugFile      = '%s/smdb.log' % self.args.str_logDir\n        self.dp                 = pfmisc.debug(\n                                    verbosity   = int(self.args.verbosity),\n                                    level       = 2,\n                                    within      = self.__name__,\n                                    debugToFile = self.args.b_debug,\n                                    debugFile   = self.str_debugFile\n                                )\n        self.log                = self.dp.qprint\n\n    def __init__(self, args):\n        \"\"\"\n        Constructor for database. Core map element structures are defined.\n        Call separate methods for actually creating file system paths.\n        \"\"\"\n\n        self.__name__           : str   = 'smdb'\n        self.args                       = args\n        self.d_DICOM            : dict  = {}\n\n        self.str_patientMap     : str   = \"patientMap\"\n        self.str_studyMap       : str   = \"studyMap\"\n        self.str_seriesMap      : str   = \"seriesMap\"\n\n        if 'str_logDir' not in self.args:\n            self.args.str_logDir        = '/tmp'\n        self.str_patientMapDir  : str   = os.path.join(\n                                            self.args.str_logDir,\n                                            self.str_patientMap\n                                        )\n        self.str_studyMapDir    : str   = os.path.join(\n                                            self.args.str_logDir,\n                                            self.str_studyMap\n                                        )\n        self.str_seriesMapDir   : str   = os.path.join(\n                                            self.args.str_logDir,\n                                            self.str_seriesMap\n                                        )\n\n\n        self.d_patientMap       : dict  = {}\n        self.d_patientInfo      : dict  = {\n            'PatientID'                         : 'Not defined',\n            'PatientName'                       : 'Not defined',\n            'PatientAge'                        : 'Not defined',\n            'PatientSex'                        : 'Not defined',\n            'PatientBirthDate'                  : 'Not defined'        }\n\n        self.d_studyMap         : dict  = {}\n        self.d_studyInfo        : dict  = {\n            'PatientID'                         : 'Not defined',\n            'StudyDescription'                  : 'Not defined',\n            'StudyDate'                         : 'Not defined',\n            'StudyInstanceUID'                  : 'Not defined',\n            'PerformedStationAETitle'           : 'Not defined'\n        }\n\n        self.d_seriesMap        : dict  = {}\n        self.d_seriesInfo       : dict  = {\n            'PatientID'                         : 'Not defined',\n            'StudyInstanceUID'                  : 'Not defined',\n            'SeriesInstanceUID'                 : 'Not defined',\n            'SeriesDescription'                 : 'Not defined',\n            'SeriesDate'                        : 'Not defined',\n            'Modality'                          : 'Not defined'\n        }\n        self.debugloggers_create()\n\n    def DICOMobj_set(self, d_DICOM) -> dict:\n        self.d_DICOM        = d_DICOM.copy()\n        return {\n            'status'    : True,\n            'd_DICOM'   : d_DICOM\n        }\n\n    def patientInfo_init(self) -> dict:\n        \"\"\"\n        Initialize a d_patientInfo dictionary with information\n        pertinent to the current series, as parsed from the\n        d_DICOM object.\n        \"\"\"\n        for key in self.d_patientInfo.keys():\n            if key in self.d_DICOM:\n                self.d_patientInfo[key]     = self.d_DICOM[key]\n        self.d_patientInfo['StudyList']     = []\n        return self.d_patientInfo\n\n    def studyInfo_init(self) -> dict:\n        \"\"\"\n        Initialize a d_studyInfo dictionary with information\n        pertinent to the current study, as parsed from the\n        d_DICOM object.\n        \"\"\"\n        for key in self.d_studyInfo.keys():\n            if key in self.d_DICOM:\n                self.d_studyInfo[key]       = self.d_DICOM[key]\n        self.d_studyInfo['SeriesList']      = []\n        return self.d_studyInfo\n\n    def seriesInfo_init(self) -> dict:\n        \"\"\"\n        Initialize a d_seriesInfo dictionary with information\n        pertinent to the current series, as parsed from the\n        d_DICOM object.\n        \"\"\"\n        for key in self.d_seriesInfo.keys():\n            if key in self.d_DICOM:\n                self.d_seriesInfo[key]      = self.d_DICOM[key]\n        self.d_seriesInfo['imageObj']       = {}\n        return self.d_seriesInfo\n\n    def json_read(self, fromFile, intoObject):\n        try:\n            intoObject.update(json.load(fromFile))\n            return True\n        except:\n            return False\n\n    @retry(Exception, delay = 1, backoff = 2, max_delay = 4, tries = 10)\n    def json_write(self, fromObject, intoFile):\n        json.dump(fromObject, intoFile, indent = 4)\n\n    def patientMap_DBtablesGet(self):\n        \"\"\"\n        Return the patientMap table files\n        \"\"\"\n        str_patientMapFile      : str = '%s/%s.json' % (\n                                        self.str_patientMapDir,\n                                        self.d_DICOM['PatientID']\n                                    )\n        return {\n            'status'            : True,\n            'patientMapFile'    : {\n                'name'      : str_patientMapFile,\n                'exists'    : os.path.isfile(str_patientMapFile)\n            }\n        }\n\n    def patientMap_process(self) -> dict:\n        \"\"\"\n        Process the patient map data.\n        \"\"\"\n        self.patientInfo_init()\n        d_patientTable      = self.patientMap_DBtablesGet()\n        if d_patientTable['patientMapFile']['exists']:\n            with open(d_patientTable['patientMapFile']['name']) as fj:\n                self.json_read(fj, self.d_patientMap)\n                # self.d_patientMap   = json.load(fj)\n            fj.close()\n        if self.d_DICOM['PatientID'] not in self.d_patientMap.keys():\n            self.d_patientMap[self.d_DICOM['PatientID']] =                  \\\n                self.d_patientInfo\n        if self.d_DICOM['StudyInstanceUID'] not in                          \\\n            self.d_patientMap[self.d_DICOM['PatientID']]['StudyList']:\n            self.d_patientMap[self.d_DICOM['PatientID']]['StudyList'].      \\\n                append(\n                    self.d_DICOM['StudyInstanceUID']\n                )\n            with open(d_patientTable['patientMapFile']['name'], 'w') as fj:\n                self.json_write(self.d_patientMap, fj)\n                # json.dump(self.d_patientMap, fj, indent = 4)\n        self.d_patientInfo   = self.d_patientMap[self.d_DICOM['PatientID']]\n\n    def studyMap_DBtablesGet(self):\n        \"\"\"\n        Return the patientMap table files\n        \"\"\"\n        str_studySeries         : str = ''\n        str_studyMapFile        : str = '%s/%s.json' % (\n                                    self.str_studyMapDir,\n                                    self.d_DICOM['StudyInstanceUID']\n                                )\n        str_seriesDir           : str = '%s/%s-series' % (\n                                    self.str_studyMapDir,\n                                    self.d_DICOM['StudyInstanceUID']\n                                )\n        if 'SeriesInstanceUID' in self.d_DICOM.keys():\n            str_studySeries     = '%s/%s.json' % (\n                                    str_seriesDir,\n                                    self.d_DICOM['SeriesInstanceUID']\n                                )\n            if not os.path.isdir(str_seriesDir): os.makedirs(str_seriesDir)\n        return {\n            'status'            : True,\n            'studyMapDir'       : self.str_studyMapDir,\n            'studySeriesDir'    : str_seriesDir,\n            'studyMapFile'      : {\n                'name'              : str_studyMapFile,\n                'exists'            : os.path.isfile(str_studyMapFile)\n                                },\n            'studySeriesFile'   : {\n                'name'              : str_studySeries,\n                'exists'            : os.path.isfile(str_studySeries)\n                                }\n        }\n\n    def dictexpand(self, d) -> dict:\n        \"\"\"\n        Expand a dictionary of key,value pairs:\n\n            d[key] = value\n\n        to\n\n            d[key]  = {\n                'value':    value\n                'label':    key\n            }\n\n        and return result. This is mainly to reconstruct the structure to which\n        some pypx utils expect a DICOM dictionary conformance.\n        \"\"\"\n        d_ret       : dict  = {}\n        d_ret       = {k:{'value': v, 'label': k} for (k,v) in d.items()}\n        return d_ret\n\n    def studyMap_process(self) -> dict:\n        \"\"\"\n        Process the study map data.\n\n        This merely checks if study map and series-related map json\n        files exist, and if not simply creates them.\n\n        Since multiple processes might attempt this, this method attempts\n        to be conceptually thread safe. If collisions occur, then no data\n        should be lost since effectively only the same information is ever\n        stamped/saved by each thread that might clobber this.\n        \"\"\"\n        self.studyInfo_init()\n        d_studyTable    = self.studyMap_DBtablesGet()\n        if not d_studyTable['studyMapFile']['exists']:\n            self.d_studyMap[self.d_DICOM['StudyInstanceUID']] =             \\\n                self.d_studyInfo\n            with open(d_studyTable['studyMapFile']['name'], 'w') as fj:\n                self.json_write(self.d_studyMap, fj)\n            fj.close()\n        if not d_studyTable['studySeriesFile']['exists']:\n            with open(d_studyTable['studySeriesFile']['name'], 'w') as fj:\n                self.json_write({\n                    self.d_DICOM['StudyInstanceUID'] : {\n                        'SeriesInstanceUID' :   self.d_DICOM['SeriesInstanceUID'],\n                        'SeriesBaseDir'     :   self.str_outputDir,\n                        'DICOM'             :   self.dictexpand(self.d_DICOM)\n                    }\n                }, fj)\n        return self.d_studyInfo\n\n    def seriesMapMeta(self, str_field, *args)   -> dict:\n        \"\"\"\n        get/set the <str_field> in the seriesMapMeta\n        \"\"\"\n        b_status                        = False\n        str_error                       = 'File does not exist at time of read'\n        d_meta                          = {}\n        d_seriesTable                   = self.seriesMap_DBtablesGet(\n                SeriesInstanceUID       = self.d_DICOM['SeriesInstanceUID']\n        )\n        if d_seriesTable['status']:\n            if d_seriesTable['seriesMapMetaFile']['exists']:\n                with open(d_seriesTable['seriesMapMetaFile']['name']) as fj:\n                    self.json_read(fj, d_meta)\n                fj.close()\n                if str_field in d_meta.keys():\n                    b_status            = True\n                    str_error           = ''\n                else:\n                    str_error           = \"No field '%s' found\" % str_field\n            if len(args):\n                d_meta[str_field]       = args[0]\n                try:\n                    with open(d_seriesTable['seriesMapMetaFile']['name'], 'w') as fj:\n                        self.json_write(d_meta, fj)\n                    str_error           = ''\n                except Exception as e:\n                    str_error           = '%s' % e\n                    b_status            = False\n        return {\n            'status'        : b_status,\n            'error'         : str_error,\n            'meta'          : d_meta\n        }\n\n    def study_statusGet(self, str_StudyInstanceUID) -> dict:\n        \"\"\"\n        Return the status of the passed StudyInstanceUID as well as\n        an embedded list in the returned object of the contents of\n        all the related study-series files.\n        \"\"\"\n        b_status        : bool  = False\n\n        d_DICOM         = self.d_DICOM.copy()\n        self.d_DICOM['StudyInstanceUID'] = str_StudyInstanceUID\n        d_studyTable    : dict  = self.studyMap_DBtablesGet()\n        self.d_DICOM    = d_DICOM.copy()\n\n        b_status        = d_studyTable['studyMapFile']['exists']\n        return {\n            'status'        : b_status,\n            'studyTable'    : d_studyTable\n        }\n\n    def study_seriesListGet(self, str_StudyInstanceUID) -> dict:\n        \"\"\"\n        Return a list of the series associated with given\n        str_StudyInstanceUID\n        \"\"\"\n        b_status            : bool  = False\n        d_studyTable        : dict  = self.study_statusGet(str_StudyInstanceUID)\n        d_series            : dict  = {}\n        l_series            : list  = []\n        str_studySeriesDir  : str = ''\n        str_studySeriesFile : str = ''\n        if d_studyTable['status']:\n            with open(d_studyTable['studyTable']['studyMapFile']['name']) as fp:\n                d_studyInfo     = json.load(fp)\n            fp.close()\n            str_studySeriesDir  = d_studyTable['studyTable']['studySeriesDir']\n            l_studySeries       = os.listdir(str_studySeriesDir)\n            if len(l_studySeries):\n                b_status        = True\n            for f in l_studySeries:\n                str_studySeriesFile = '%s/%s' % (str_studySeriesDir, f)\n                with open(str_studySeriesFile, 'r') as fp:\n                    d_series    = json.load(fp)\n                l_series.append(d_series[str_StudyInstanceUID]['SeriesInstanceUID'])\n        return {\n            'status'            : b_status,\n            'seriesList'        : l_series\n        }\n\n    def study_seriesContainsVerify(self,\n                    str_StudyInstanceUID,\n                    str_SeriesInstanceUID,\n                    b_verifySeriesInStudy) -> dict:\n        \"\"\"\n        Check if the passed str_StudyInstanceUID contains\n        the passed str_SeriesInstanceUID -- at least as far\n        as the smdb is concerned.\n        \"\"\"\n        d_status                : dict      = {}\n        d_status['status']                  = False\n        d_status['error']       : str       = 'Study not found'\n        d_status['study']       : dict      = self.study_statusGet(\n                                                str_StudyInstanceUID\n                                            )\n        d_status['study']['state']  : str   = 'StudyNotFound'\n\n        d_status['series']      : dict      = {}\n\n        if d_status['study']['status'] or not b_verifySeriesInStudy:\n            if d_status['study']['status']:\n                d_status['study']['state']  = 'StudyOK'\n            d_status['error']               = 'Series not found'\n            d_status['study']['seriesListInStudy']   = \\\n                                            self.study_seriesListGet(\n                                                str_StudyInstanceUID\n                                            )\n            d_status['series']              = self.series_statusGet(\n                                                str_SeriesInstanceUID\n                                            )\n            d_status['series']['state']     = 'SeriesNotFound'\n            if d_status['series']['status']:\n                d_status['error']           = ''\n                d_status['series']['state'] = 'SeriesMapMetaOK'\n                d_status['status']          = True\n            if str_SeriesInstanceUID in d_status['study']\\\n                                        ['seriesListInStudy']\\\n                                        ['seriesList']:\n                d_status['study']['state'] = 'StudyContainsSeriesOK'\n            else:\n                d_status['study']['state'] = 'StudyDoesNotContainSeries'\n                d_status['study']['status']= False\n        return d_status\n\n    def series_receivedAndRequested(self, str_SeriesInstanceUID) -> dict:\n        \"\"\"\n        Return a dictionary with requested vs received file count.\n        \"\"\"\n        d_count                 : dict  = {}\n        d_count['received']     = self.series_receivedFilesCount(\n                                        str_SeriesInstanceUID\n                                )\n        d_count['requested']    = self.series_requestedFilesCount(\n                                        str_SeriesInstanceUID\n                                )\n        if d_count['received']['count'] >= d_count['requested']['count']:\n            d_count['state']    = 'ImagesAllReceivedOK'\n            d_count['status']   = True\n        elif not d_count['received']['count']:\n            d_count['state']    = 'NoImagesReceived'\n            d_count['status']   = False\n        else:\n            d_count['state']    = 'ImagesInFlight'\n            d_count['status']   = False\n        return d_count\n\n    def series_statusGet(self, str_SeriesInstanceUID) -> dict:\n        \"\"\"\n        Return the status of the passed SeriesInstanceUID.\n        \"\"\"\n        b_status        : bool  = False\n        d_seriesTable   : dict  = self.seriesMap_DBtablesGet(\n                            SeriesInstanceUID = str_SeriesInstanceUID\n        )\n        b_status        = d_seriesTable['seriesMapMetaFile']['exists']\n        return {\n            'status'        : b_status,\n            'seriesTable'   : d_seriesTable\n        }\n\n    def series_requestedFilesCount(self, str_SeriesInstanceUID) -> dict:\n        \"\"\"\n        Return the requested files count for a given series.\n\n        This assumes that the series was received as the result of a\n        DICOM movescu which allows the requestor to ask the PACS for\n        the NumberOfSeriesRelatedInstances. If a DICOM series was simply\n        pushed directly to the listener, this information is not\n        available.\n        \"\"\"\n        b_status        : bool  = False\n        d_DICOM         = self.d_DICOM.copy()\n        self.d_DICOM['SeriesInstanceUID'] = str_SeriesInstanceUID\n        d_get           : dict  = \\\n            self.seriesMapMeta('NumberOfSeriesRelatedInstances')\n        self.d_DICOM    = d_DICOM.copy()\n        b_status        = d_get['status']\n        return {\n            'status'    :   b_status,\n            'count'     :   int(d_get['meta']['NumberOfSeriesRelatedInstances'])\n        }\n\n    def series_receivedFilesCount(self, str_SeriesInstanceUID) -> dict:\n        \"\"\"\n        Return the number of actual received files by \"counting\" the\n        object json files for a given series.\n\n        Note this returns the count in the seriesMapDir for a given\n        series, which assumes that a file has been processed and\n        recorded -- this does not return the count of files in the\n        packed location.\n        \"\"\"\n        b_status            : bool  = False\n        l_files             : list  = []\n        str_processedDir    : str   = os.path.join( self.args.str_logDir,\n                                                    self.str_seriesMap,\n                                                    str_SeriesInstanceUID)\n        if os.path.isdir(str_processedDir):\n            b_status        = True\n            l_files         : list  = [\n                f for f in os.listdir(str_processedDir)\n                        if os.path.isfile(os.path.join(str_processedDir, f))\n            ]\n        return {\n            'status'    : b_status,\n            'count'     : len(l_files)\n        }\n\n    def seriesMap_DBtablesGet(self, **kwargs) -> dict:\n        \"\"\"\n        Return the location in the DB (i.e. the file system) where\n        the map information for a given SeriesInstanceUID is stored.\n        \"\"\"\n        b_status                        = False\n        str_SeriesInstanceUID           = ''\n        str_outputFile                  = ''\n        str_seriesBaseDir               = ''\n        str_seriesMapSingleImageFile    = ''\n        str_seriesMapMetaFile           = ''\n        for k, v in kwargs.items():\n            if k == 'SeriesInstanceUID' :   str_SeriesInstanceUID   = v\n            if k == 'outputFile'        :   str_outputFile          = v\n        if len(str_SeriesInstanceUID):\n            b_status                    = True\n            str_seriesBaseDir           = '%s/%s' % \\\n                    (self.str_seriesMapDir,\n                     str_SeriesInstanceUID)\n            str_seriesMapMetaFile           = '%s/%s-meta.json' % \\\n                    (self.str_seriesMapDir,\n                     str_SeriesInstanceUID)\n            if len(str_outputFile):\n                str_seriesMapSingleImageFile    = '%s/%s.json' % (\n                    str_seriesBaseDir, str_outputFile\n                )\n        return {\n            'status'                    : b_status,\n            'seriesBaseDir'             : {\n                'name'      :   str_seriesBaseDir,\n                'exists'    :   os.path.isdir(str_seriesBaseDir)\n            },\n            'seriesMapSingleImageFile'  : {\n                'name'      :   str_seriesMapSingleImageFile,\n                'exists'    :   os.path.isfile(str_seriesMapSingleImageFile)\n            },\n            'seriesMapMetaFile'         : {\n                'name'      :   str_seriesMapMetaFile,\n                'exists'    :   os.path.isfile(str_seriesMapMetaFile)\n            }\n        }\n\n    def seriesMap_process(self, **kwargs) -> dict:\n        \"\"\"\n        Process the series map data.\n        \"\"\"\n        str_outputDir   = self.str_outputDir\n        str_outputFile  = self.str_outputFile\n\n        for k, v in kwargs.items():\n            if k == 'outputDir'     : str_outputDir     = v\n            if k == 'outputFile'    : str_outputFile    = v\n\n        self.seriesInfo_init()\n        d_seriesTable       = self.seriesMap_DBtablesGet(\n                outputDir           = str_outputDir,\n                outputFile          = str_outputFile,\n                SeriesInstanceUID   = self.d_DICOM['SeriesInstanceUID'],\n                **kwargs\n        )\n        if not d_seriesTable['seriesBaseDir']['exists']:\n            os.makedirs(\n                    d_seriesTable['seriesBaseDir']['name'],\n                    exist_ok = True\n            )\n        if d_seriesTable['seriesMapSingleImageFile']['exists']:\n            with open(d_seriesTable['seriesMapSingleImageFile']['name']) as fj:\n                self.json_read(fj, self.d_seriesMap)\n                # self.d_seriesMap    = json.load(fj)\n            fj.close()\n        if self.d_DICOM['SeriesInstanceUID'] not in self.d_seriesMap.keys():\n            self.d_seriesMap[self.d_DICOM['SeriesInstanceUID']] =           \\\n                self.d_seriesInfo\n        if str_outputFile not in                                            \\\n            self.d_seriesMap[self.d_DICOM['SeriesInstanceUID']]             \\\n                                            ['imageObj'].keys():\n            ofs = os.stat('%s/%s' % (str_outputDir, str_outputFile))\n            self.d_seriesMap[self.d_DICOM['SeriesInstanceUID']]['imageObj'] \\\n                                    [str_outputFile] =                      \\\n                {k.replace('st_', '') :                                     \\\n                    ('%s' % datetime.datetime.fromtimestamp(getattr(ofs, k))\\\n                    if 'time' in k and not 'ns' in k                        \\\n                        else getattr(ofs, k))                               \\\n                            for k in dir(ofs)                               \\\n                                if 'st' in k and not 'ns' in k and not '__'  in k}\n            self.d_seriesMap[self.d_DICOM['SeriesInstanceUID']]['imageObj'] \\\n                                    [str_outputFile]['FSlocation'] =        \\\n                                        '%s/%s' % (str_outputDir, str_outputFile)\n            with open(d_seriesTable['seriesMapSingleImageFile']['name'], 'w') as fj:\n                self.json_write(self.d_seriesMap, fj)\n                # json.dump(self.d_seriesMap, fj, indent = 4)\n        else:\n            self.d_seriesInfo     = self.d_seriesMap[self.d_DICOM['SeriesInstanceUID']]\n        with open(d_seriesTable['seriesMapSingleImageFile']['name'], 'w') as fj:\n            json.dump(self.d_seriesMap, fj, indent = 4)\n\n    def mapsUpdateForFile(self, str_file):\n        b_status        :   bool    = True\n\n        # Store the <str_file>, i.e. the file location where the DICOM\n        # has been repacked.\n        self.str_outputDir  = os.path.dirname(str_file)\n        self.str_outputFile = os.path.basename(str_file)\n\n        self.patientMap_process()\n        self.studyMap_process()\n        self.seriesMap_process()\n\n        return {\n            'status'            : b_status,\n            'd_patientInfo'     : self.d_patientInfo,\n            'd_studyInfo'       : self.d_studyInfo,\n            'd_seriesInfo'      : self.d_seriesInfo,\n        }\n\n    def endOfStudy(self, str_xcrdir):\n        \"\"\"\n        Perform an end-of-study event\n        \"\"\"\n        str_eosfile = '/tmp/eos.txt'\n        str_message = 'EOS for dir %s on %s' % (\n                            str_xcrdir,\n                            datetime.datetime.now()\n                        )\n        with open(str_eosfile, 'a') as f:\n            f.write(str_message)\n        return {\n            'method'            : inspect.stack()[0][3],\n            'status'            : True,\n            'message'           : str_message\n        }\n\n\n    def run(self):\n        \"\"\"\n        Generic run handler -- mostly called when an \"action\" needs\n        to be processed in relation to a specific DICOM file.\n\n        The DICOM file to process is typically passed by a storescu\n        process as #p/#f\n        \"\"\"\n        d_run   = {\n            'status'    : False\n        }\n        if self.args.str_action == 'mapsUpdateForFile':\n            d_DICOMread     = repack.Process.DICOMfile_read(\n                                file = '%s/%s' % (\n                                    self.args.str_xcrdir,\n                                    self.args.str_xcrfile\n                                )\n                            )\n            if d_DICOMread['status']:\n                self.DICOMobj_set(d_DICOMread['d_DICOM']['d_dicomSimple'])\n                d_run['status'] = True\n                d_run['mapsUpdateForFile'] = \\\n                    self.mapsUpdateForFile('%s/%s' % (\n                                self.args.str_xcrdir,\n                                self.args.str_xcrfile\n                            )\n                    )\n        if self.args.str_action == 'endOfStudy':\n            d_run['status'] = True\n            d_run['endOfStudy'] = \\\n                self.endOfStudy('%s' % (\n                            self.args.str_xcrdir,\n                        )\n                )\n        if 'DBtablesGet' in self.args.str_action:\n            if self.fileSpec_process():\n                d_DICOMread     = repack.Process.DICOMfile_read(\n                                    file = '%s/%s' % (\n                                        self.args.str_xcrdir,\n                                        self.args.str_xcrfile\n                                    )\n                                )\n                if d_DICOMread['status']:\n                    self.DICOMobj_set(d_DICOMread['d_DICOM']['d_dicomSimple'])\n                    d_run['status'] = True\n                    if 'eries' in self.args.str_action:\n                        d_run['seriesMap_DBtablesGet']  = \\\n                            self.seriesMap_DBtablesGet(\n                                SeriesInstanceUID   = self.d_DICOM['SeriesInstanceUID'],\n                                outputFile          = self.args.str_xcrfile\n                            )\n                    if 'tudy' in self.args.str_action:\n                        d_run['studyMap_DBtablesGet']   = \\\n                            self.studyMap_DBtablesGet()\n                    if 'atient' in self.args.str_action:\n                        d_run['patientMap_DBtablesGet'] = \\\n                            self.patientMap_DBtablesGet()\n            if not d_run['status']:\n                d_run['error_message']  = \\\n                    'Unable to process a valid DICOM %s/%s' % (\n                        self.args.str_xcrdir,\n                        self.args.str_xcrfile\n                    )\n        if not d_run['status']:\n            d_run['error_on_action']  = '%s action called' % self.args.str_action\n        return d_run\n\n\n\n","sub_path":"pypx/smdb.py","file_name":"smdb.py","file_ext":"py","file_size_in_byte":31712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"105334906","text":"\"\"\"\nMódulos Builtin ( módulos integrados que ja vem instalado no python)\n\n\n\"\"\"\n\nfor i in dir(__builtins__):\n    print(i)\n\n# # apelidando importações\n# import random as aleatorio\n# import matplotlib as graficos\n#\n# print(graficos)\n# print(aleatorio.random())\n\n# # improtando todas as funções\n# from random import *\n#\n# print(random())\n# # importando mais de uma função\n# from random import randint, random\n\n# # apelidando função preferenciada\n# from random import randint as randomint\n#\n# print(randomint(1, 9))\n\n# para melhor visualização solocamos as funções importadas em uma tupla\nfrom random import (\n    random,\n    randint,\n    choice)\n\n\n","sub_path":"Cursos Python/Python 3  Básico ao avançado - Geek University/80 - módulos Builtin.py","file_name":"80 - módulos Builtin.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"487706410","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\n\nfrom torch.utils.data import DataLoader\nfrom torch.utils.data import TensorDataset\nfrom torch.utils.data.sampler import SubsetRandomSampler\n\nnb_of_classes = 500\nopts = {\n  'batch_size': 1000,\n  'learning_rate': 0.001\n  }\n\nclass Data_Buffer:\n  def __init__(self, max_batches_per_class=60, batch_size=100):\n    self.max_batches_per_class = max_batches_per_class\n    self.batch_size = batch_size\n    self.dbuffer = {}\n    self.oldest_batches = {}\n  \n  def add_batch(self, batch, class_label):\n    \"\"\"\n    Adding a batch to the buffer to the corresponding class storage, \n    The oldest batches are replaced when the buffer is full\n    If an unknown class appears - a new storage is added\n    \"\"\"\n    if str(class_label) in self.dbuffer.keys():\n      if len(self.dbuffer[str(class_label)]) < self.max_batches_per_class:\n        #print('adding new batch')\n        self.dbuffer[str(class_label)].append(batch.clone())\n      else:\n        self.dbuffer[str(class_label)][self.oldest_batches[str(class_label)]] = batch.clone()\n        self.oldest_batches[str(class_label)] = (self.oldest_batches[str(class_label)] + 1) % self.max_batches_per_class\n        #print('replacing old batch')\n    else:\n      #print('Class label:{}'.format(class_label))\n      #print('initializing new class')\n      self.dbuffer[str(class_label)] = [batch.clone()]\n      self.oldest_batches[str(class_label)] = 0\n      \n  def transform_data(self, transform):\n    # Inplace apply a given transform to all the batches in the buffer\n    for class_label in self.dbuffer.keys():\n      for idx in range(len(self.dbuffer[str(class_label)])):\n        self.dbuffer[str(class_label)][idx] = transform(self.dbuffer[str(class_label)][idx]).data\n        \n  def make_tensor_dataset(self):\n    # Transform the buffer into a single tensor dataset\n    tensor_data = []\n    tensor_labels = []\n    for key in self.dbuffer.keys():\n      tensor_data += self.dbuffer[key]\n      tensor_labels += [int(key)]*(self.batch_size*len(self.dbuffer[key]))\n    tensor_data = torch.stack(tensor_data)\n    tensor_data = tensor_data.reshape(tensor_data.shape[0]*tensor_data.shape[1], tensor_data.shape[2])\n    return TensorDataset(tensor_data, torch.FloatTensor(tensor_labels))\n  \nclass Classifier_128_features(nn.Module):\n  def __init__(self, nb_classes):\n    super(Classifier_128_features, self).__init__()\n    self.fc1 = nn.Linear(128, 256)\n    self.fc2 = nn.Linear(256, 256)\n    self.fc3 = nn.Linear(256, nb_classes)\n\n  def forward(self, x):\n    x = F.relu(self.fc1(x))\n    x = F.relu(self.fc2(x))\n    x = self.fc3(x)\n    return x\n\ndef test_classifier(classif, data_loader):\n  total = 0\n  correct = 0\n  for test_X, test_Y in data_loader:\n    input_test = test_X.float().cuda()\n    outputs = classif(input_test)\n    _, predicted = torch.max(outputs.data, 1)\n    labels = test_Y.long()\n    total += labels.size(0)\n    correct += (predicted.cpu().long() == labels).sum().item()\n  return correct/total*100\n\ndef get_indices_for_classes(data, data_classes):\n  # Creates a list of indices of samples from the dataset, corresponding to given classes\n  indices = torch.FloatTensor(list((data.tensors[1].long()==class_).tolist() for class_ in data_classes)).sum(0).nonzero().long().squeeze()\n  return indices[torch.randperm(len(indices))]\n\n# Loading the datasets\nfull_data = torch.load('./data/data_train_test_500_classes_128_features_2000_samples.pth')\ntrainset = TensorDataset(full_data['data_train'], full_data['labels_train'])\ntestset = TensorDataset(full_data['data_test'], full_data['labels_test'])\n\n# Initializing data loaders for first 5 classes\npretrain_on_classes = range(500)\n\nindices_train = get_indices_for_classes(trainset, pretrain_on_classes)\nindices_test = get_indices_for_classes(testset, pretrain_on_classes)\ntrain_loader = DataLoader(trainset, batch_size=opts['batch_size'], sampler = SubsetRandomSampler(indices_train))\ntest_loader = DataLoader(testset, batch_size=opts['batch_size'], sampler = SubsetRandomSampler(indices_test))\n\n# Initializing classification model, criterion and optimizer function\nclassifier = Classifier_128_features(nb_of_classes)\nclassification_optimizer = optim.Adam(classifier.parameters(), lr=opts['learning_rate'], betas=(0.9, 0.999), weight_decay=1e-5)\nclassification_criterion = nn.CrossEntropyLoss()\nclassifier.cuda()\nclassification_criterion.cuda()\n\nmax_accuracy = 0\ntraining_epochs = 5\nfor epoch in range(training_epochs):\n  # Training the classifer\n  for idx, (X, Y) in enumerate(train_loader):\n    inputs = X.cuda()\n    labels = Y.long().cuda()\n    outputs = classifier(inputs)\n    classification_loss = classification_criterion(outputs, labels)\n    classification_loss.backward()\n    classification_optimizer.step()\n    classification_optimizer.zero_grad()\n    if idx%50==0:\n      print('epoch [{}/{}], classification loss: {:.4f}'.format(epoch, training_epochs,  classification_loss.item()))\n  acc = test_classifier(classifier, test_loader)\n  print('Test accuracy after {} epochs: {:.8f}'.format(epoch+1, acc))    \n  if acc > max_accuracy:\n    max_accuracy = acc\n    torch.save(classifier.state_dict(), './pretrained_models/classifier_250_classes_original_data.pth')\n    \n    \n","sub_path":"Synthetic_step_test/step1_pretraining_half_classifier.py","file_name":"step1_pretraining_half_classifier.py","file_ext":"py","file_size_in_byte":5256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"126954595","text":"from .models import Venue\n\n## CLASS CONTAINS HELPER FUNCTIONS FOR VENUE MODEL INTERACTIONS ##\nclass VenueHelpers:\n\n    def __init__(self):\n        self.name = 'Test Cafe'\n        self.address_1 = '10'\n        self.address_2 = 'Test Street'\n        self.post_code = 'W1D 3PU'\n        self.description = 'I am a cafe.'\n\n    def create_default_venue(self):\n        new_venue = Venue.objects.create(name=self.name,\n                            address_1=self.address_1,\n                            address_2=self.address_2,\n                            post_code=self.post_code,\n                            description=self.description)\n        return new_venue\n\n\n    def check_default_venue_exists(self):\n        try:\n            venue = Venue.objects.get(name=self.name)\n        except Venue.DoesNotExist as e:\n            print('No Default Venue in your Database: {}'.format(e))\n            return False\n        return True\n\n    def get_default_venue(self):\n        try:\n            venue = Venue.objects.get(name=self.name)\n        except Venue.DoesNotExist as e:\n            print('No Default Venue in your Database: {}'.format(e))\n            return None\n\n        return venue\n\n    def get_venue(self, id):\n        try:\n            venue = Venue.objects.get(id=id)\n        except Venue.DoesNotExist as e:\n            print('Venue Not in Database: {}'.format(e))\n            return None\n       \n        return venue","sub_path":"core/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"60426893","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed May 26 22:50:25 2021\n\n@author: bananasacks\n\"\"\"\n\nimport numpy as np\nimport os\nos.chdir(\"/Users/bananasacks/Desktop/Optimal Transport Internship/Optimal_Transport/pascalle_s_drafts/test_algos_draft\")\nimport matplotlib.pyplot as plt\n#from load_data import load_data\n\n\n\n\n#bary_lvl_0.000_mean_0.000_eps_0.7.npy\n#bary_lvl_0.200_mean_0.000_eps_0.7.npy\n#bary_lvl_0.050_mean_0.000_eps_0.7.npy\n\n\nlvl = [\"0.000\", \"0.050\", \"0.100\", \"0.200\", \"0.500\", \"0.100\"]\neps = [\"0.5\"]\n    \n\nbary0 = np.load(\"results/debiased_sink_bary/bary_lvl_0.000_mean_0.000_eps_\"+eps[0]+\".npy\")#, allow_pickle=True)\n    \nprint(bary0.max())\nprint(bary0.min())\n\nbary2 = np.load(\"results/debiased_sink_bary/bary_lvl_0.500_mean_0.000_eps_\"+eps[0]+\".npy\")#, allow_pickle=True)\n    \nprint(bary2.max())\nprint(bary2.min())\n    \n    #plt.imshow(bary, vmin=0, vmax=1)\n\n#k=1\n#plt.subplot(2, 3, k)\n#plt.title(title[:len(title)//2]+\"\\n\"+title[len(title)//2:])\n#plt.imshow(bary, vmin=0, vmax=1)\n#k += 1\n\n        \n\n        \n","sub_path":"pascalle_s_drafts/test_algos_draft/untitled2.py","file_name":"untitled2.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"403565368","text":"# Printing movie name director name year of release and ratings\nimport random\nimport requests\nfrom bs4 import BeautifulSoup\nurl = \"https://www.imdb.com/india/top-rated-tamil-movies/\"\n\n\ndef main():\n    response = requests.get(url).text\n    soup = BeautifulSoup(response, 'lxml')\n    movies = soup.find_all('tbody', class_='lister-list')\n    for movie in movies:\n        movies_tr = movie.find_all('tr')\n        for movie_tr in movies_tr:\n            # print(movies_tr)\n            movie_name = movie_tr.find('td', class_='titleColumn').a.text\n\n            movie_director = movie_tr.find(\n                'td', class_='titleColumn').a['title'].split(',')[0].replace('(dir.)', '')\n\n            release_year = movie_tr.find(\n                'td', class_='titleColumn').span.text.replace('(', '').replace(')', '')\n\n            ratings = movie_tr.find(\n                'td', class_='ratingColumn imdbRating').text\n\n            print(f\"Movie Name: {movie_name}\")\n            print(f\"Release Year: {release_year}\")\n            print(f\"Director Name: {movie_director}\")\n            print(f\"Ratings: {ratings.strip()}\")\n            print(\"================================\")\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"web_scraping_tutorial/basics/movieslist/ratings_all.py","file_name":"ratings_all.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"254895553","text":"# coding:utf8\n\nimport pandas\nimport matplotlib\nfrom matplotlib import pyplot as plt\n\ndata = pandas.read_csv(\n    \"E:\\Workspace\\idata\\lab_data_analysis\\data\\lab36_01\\data.csv\"\n)\n\n# 对日期格式进行转换\ndata[\"购买日期\"] = pandas.to_datetime(data[\"日期\"])\n\n# 颜色\nmainColor = (42 / 256, 87 / 256, 141 / 256, 1)\n# 字体，为了显示中文\nfont = {\n    \"size\": 20,\n    \"family\": \"SimHei\"\n}\nmatplotlib.rc(\"font\", **font)\n\nplt.xlabel(\"购买日期\", color=mainColor)\nplt.ylabel(\"购买用户数\", color=mainColor)\nplt.tick_params(axis=\"x\", color=mainColor)\nplt.tick_params(axis=\"y\", color=mainColor)\n\n# 画图\nplt.plot(\n    data[\"购买日期\"],\n    data[\"购买用户数\"],\n    \"-\",\n    color=mainColor\n)\n\n# 设置线条粗细\nplt.plot(\n    data[\"购买日期\"],\n    data[\"购买用户数\"],\n    \"-\",\n    color=mainColor,\n    lineWidth=10\n)\n\n# 虚线\nplt.plot(\n    data[\"购买日期\"],\n    data[\"购买用户数\"],\n    \"--\",\n    color=mainColor\n)\n","sub_path":"lab_data_analysis/lab36_01.py","file_name":"lab36_01.py","file_ext":"py","file_size_in_byte":960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"380407939","text":"import os.path\nimport tensorflow as tf\nimport helper\nimport warnings\nfrom distutils.version import LooseVersion\nimport project_tests as tests\nimport numpy as np\n\n\n# Check TensorFlow Version\nassert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)\nprint('TensorFlow Version: {}'.format(tf.__version__))\n\n# Check for a GPU\nif not tf.test.gpu_device_name():\n    warnings.warn('No GPU found. Please use a GPU to train your neural network.')\nelse:\n    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))\n\n\ndef load_vgg(sess, vgg_path):\n    \"\"\"\n    Load Pretrained VGG Model into TensorFlow.\n    :param sess: TensorFlow Session\n    :param vgg_path: Path to vgg folder, containing \"variables/\" and \"saved_model.pb\"\n    :return: Tuple of Tensors from VGG model (image_input, keep_prob, layer3_out, layer4_out, layer7_out)\n    \"\"\"\n\n    #   Use tf.saved_model.loader.load to load the model and weights\n    vgg_tag = 'vgg16'\n    vgg_input_tensor_name = 'image_input:0'\n    vgg_keep_prob_tensor_name = 'keep_prob:0'\n    vgg_layer3_out_tensor_name = 'layer3_out:0'\n    vgg_layer4_out_tensor_name = 'layer4_out:0'\n    vgg_layer7_out_tensor_name = 'layer7_out:0'\n\n    tf.saved_model.loader.load(sess, [vgg_tag], vgg_path)\n\n    return sess.graph.get_tensor_by_name(vgg_input_tensor_name), \\\n           sess.graph.get_tensor_by_name(vgg_keep_prob_tensor_name), \\\n           sess.graph.get_tensor_by_name(vgg_layer3_out_tensor_name), \\\n           sess.graph.get_tensor_by_name(vgg_layer4_out_tensor_name), \\\n           sess.graph.get_tensor_by_name(vgg_layer7_out_tensor_name)\ntests.test_load_vgg(load_vgg, tf)\n\n\ndef layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes):\n    \"\"\"\n    Create the layers for a fully convolutional network.  Build skip-layers using the vgg layers.\n    :param vgg_layer7_out: TF Tensor for VGG Layer 3 output\n    :param vgg_layer4_out: TF Tensor for VGG Layer 4 output\n    :param vgg_layer3_out: TF Tensor for VGG Layer 7 output\n    :param num_classes: Number of classes to classify\n    :return: The Tensor for the last layer of output\n    \"\"\"\n\n    # Encoder\n    vgg3 = tf.layers.conv2d(vgg_layer3_out, num_classes, 1, strides=(1, 1), padding='SAME',\n                            kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n\n    vgg4 = tf.layers.conv2d(vgg_layer4_out, num_classes, 1, strides=(1, 1), padding='SAME',\n                            kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n\n    vgg7 = tf.layers.conv2d(vgg_layer7_out, num_classes, 1, strides=(1, 1), padding='SAME',\n                            kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n\n    # Decoder\n    vgg7_t = tf.layers.conv2d_transpose(vgg7, num_classes, 4, strides=(2, 2), padding='SAME',\n                                        kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n\n    vgg4_s = tf.add(vgg4, vgg7_t)\n    vgg4_t = tf.layers.conv2d_transpose(vgg4_s, num_classes, 8, strides=(2, 2), padding='SAME',\n                                        kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n\n    vgg3_s = tf.add(vgg3, vgg4_t)\n    vgg3_t = tf.layers.conv2d_transpose(vgg3_s, num_classes, 16, strides=(8, 8), padding='SAME',\n                                        kernel_initializer=tf.truncated_normal_initializer(stddev=0.001))\n\n    return vgg3_t\ntests.test_layers(layers)\n\n\ndef optimize(nn_last_layer, correct_label, learning_rate, num_classes):\n    \"\"\"\n    Build the TensorFLow loss and optimizer operations.\n    :param nn_last_layer: TF Tensor of the last layer in the neural network\n    :param correct_label: TF Placeholder for the correct label image\n    :param learning_rate: TF Placeholder for the learning rate\n    :param num_classes: Number of classes to classify\n    :return: Tuple of (logits, train_op, cross_entropy_loss)\n    \"\"\"\n\n    # Output tensor is 4D so we have to reshape it to 2D\n    logits = tf.reshape(nn_last_layer, (-1, num_classes))\n    labels = tf.reshape(correct_label, (-1, num_classes))\n    # logits is now a 2D tensor where each row represents a pixel and each column a class. Standard cross entropy is used for the loss.\n    cross_entropy_loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=labels))\n    train_op = tf.train.AdamOptimizer(learning_rate).minimize(cross_entropy_loss)\n\n    return logits, train_op, cross_entropy_loss\ntests.test_optimize(optimize)\n\n\ndef add_images(images):\n    \"\"\"\n    Doubles the number of images by flipping them.\n    :param images: A 4 dimensional numpy ndarray\n    :return: A 4 dimensional numpy ndarray which contains the original images together with the flipped images\n    \"\"\"\n    if len(images.shape) == 4:\n        # Flip the images to generate more training data\n        flipped_images = images[:, :, ::-1, :]\n        return np.concatenate((images, flipped_images), axis=0)\n    return images\n\n\ndef train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, input_image,\n             correct_label, keep_prob, learning_rate):\n    \"\"\"\n    Train neural network and print out the loss during training.\n    :param sess: TF Session\n    :param epochs: Number of epochs\n    :param batch_size: Batch size\n    :param get_batches_fn: Function to get batches of training data.  Call using get_batches_fn(batch_size)\n    :param train_op: TF Operation to train the neural network\n    :param cross_entropy_loss: TF Tensor for the amount of loss\n    :param input_image: TF Placeholder for input images\n    :param correct_label: TF Placeholder for label images\n    :param keep_prob: TF Placeholder for dropout keep probability\n    :param learning_rate: TF Placeholder for learning rate\n    \"\"\"\n    for epoch_i in range(epochs):\n        for batch_i, (image, ground_truth) in enumerate(get_batches_fn(batch_size)):\n            image = add_images(image)\n            ground_truth = add_images(ground_truth)\n            train_loss, _ = sess.run([cross_entropy_loss, train_op],\n                                     feed_dict={input_image: image, correct_label: ground_truth, keep_prob: 0.7,\n                                                learning_rate: 0.0001})\n            # Display the loss after every tenth batch\n            if batch_i % 10 == 9:\n                print('Epoch {:>3} Batch {:>2}   Training loss = {:.3f}'.format(epoch_i+1, batch_i+1, train_loss))\n        # Display the loss after the epoch\n        print('Epoch {:>3} Batch {:>2}   Training loss = {:.3f}'.format(epoch_i+1, batch_i+1, train_loss))\ntests.test_train_nn(train_nn)\n\n\ndef run():\n    epochs = 10\n    batch_size = 8\n    num_classes = 2\n    image_shape = (160, 576)\n    data_dir = './data'\n    runs_dir = './runs'\n    tests.test_for_kitti_dataset(data_dir)\n\n    # Download pretrained vgg model\n    helper.maybe_download_pretrained_vgg(data_dir)\n\n    # OPTIONAL: Train and Inference on the cityscapes dataset instead of the Kitti dataset.\n    # You'll need a GPU with at least 10 teraFLOPS to train on.\n    #  https://www.cityscapes-dataset.com/\n\n    with tf.Session() as sess:\n        # Path to vgg model\n        vgg_path = os.path.join(data_dir, 'vgg')\n        # Create function to get batches\n        get_batches_fn = helper.gen_batch_function(os.path.join(data_dir, 'data_road/training'), image_shape)\n\n        # OPTIONAL: Augment Images for better results\n        #  https://datascience.stackexchange.com/questions/5224/how-to-prepare-augment-images-for-neural-network\n\n        # Build NN using load_vgg, layers, and optimize function\n        input_image, keep_prob, vgg_layer3_out, vgg_layer4_out, vgg_layer7_out = load_vgg(sess, vgg_path)\n        nn_last_layer = layers(vgg_layer3_out, vgg_layer4_out, vgg_layer7_out, num_classes)\n\n        learning_rate = tf.placeholder(tf.float32)\n        correct_label = tf.placeholder(tf.float32, shape=(None, None, None, num_classes))\n        logits, train_op, cross_entropy_loss = optimize(nn_last_layer, correct_label, learning_rate, num_classes)\n\n        # Train NN using the train_nn function\n        sess.run(tf.global_variables_initializer())\n        train_nn(sess, epochs, batch_size, get_batches_fn, train_op, cross_entropy_loss, input_image, correct_label,\n                 keep_prob, learning_rate)\n\n        # Save inference data using helper.save_inference_samples\n        helper.save_inference_samples(runs_dir, data_dir, sess, image_shape, logits, keep_prob, input_image)\n\n\nif __name__ == '__main__':\n    run()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"587092111","text":"#!/usr/bin/env python3\n# Copyright (c) Facebook, Inc. and its affiliates.\n# All rights reserved.\n#\n# This source code is licensed under the BSD-style license found in the\n# LICENSE file in the root directory of this source tree.\n\nimport unittest\n\nfrom torchx.apps.io.copy import Copy\nfrom torchx.runtime.storage import upload_blob, download_blob, temppath\n\n\nclass CopyTest(unittest.TestCase):\n    def test_copy(self) -> None:\n        with temppath() as input_path, temppath() as output_path:\n            data = b\"banana\"\n            upload_blob(input_path, data)\n            c = Copy(input_path=input_path, output_path=output_path, a=10)\n            c.run(c.inputs, c.outputs)\n            out = download_blob(output_path)\n            self.assertEqual(out, data)\n","sub_path":"torchx/apps/io/test/copy_test.py","file_name":"copy_test.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"135738403","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # 문제\n# 양의 정수 x가 하샤드 수이려면 x의 자릿수의 합으로 x가 나누어져야 합니다. 예를들어 18의 자릿수 합은 1+8=9이고, 18은 9로 나누어 떨어지므로 18은 하샤드 수입니다.\n# \n# Harshad함수는 양의 정수 n을 매개변수로 입력받습니다. 이 n이 하샤드수인지 아닌지 판단하는 함수를 완성하세요. 예를들어 n이 10, 12, 18이면 True를 리턴 11, 13이면 False를 리턴하면 됩니다.\n\n# In[ ]:\n\n\ndef harshad(n):\n\tsum = 0\n\tfor i in str(n):\n\t\tsum = int(i) + sum\n\treturn False if n % sum else True\n\n\nprint(harshad(n))\n\n","sub_path":"하샤드 수.py","file_name":"하샤드 수.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"282370778","text":"from CircularLinkedList import CircularLinkedList\nfrom DoubleList import DoubleList\nfrom Honey import Honey\n\nclass StockTable:\n    # Constructor\n    # list: List containing ingredients\n    def __init__(self):\n        self.list = CircularLinkedList()\n\n    # Destructor\n    def __del__(self):\n        self.list = None\n\n    # Returns if stock table is empty\n    def isEmpty(self):\n        return self.list.isEmpty()\n\n    # Returns length of stock table\n    def getLength(self):\n        return self.list.getLength()\n\n    # Returns amount of shots in stock table with given types\n    def getShotLength(self, type):\n        count = 0\n        for i in range(0, self.getLength()):\n            if self.list.retrieve(i)[0].getType() == type:\n                count += 1\n        return count\n\n    # Inserts given item in stock table\n    def tableInsert(self, newitem):\n        self.list.insert(0, newitem)\n        self.sortTable()\n        return True\n\n    # Deletes ingredient with given expire date from stock table\n    def tableDelete(self, expiredate):\n        first = 0\n        last = self.getLength() - 1\n        found = False\n        while first <= last and not found:\n            mid = (first + last)//2\n            current = self.list.retrieve(mid)[0]\n            if current.getExpiredate() == expiredate:\n                self.list.delete(mid)\n                found = True\n            else:\n                if expiredate < current.getExpiredate():\n                    last = mid - 1\n                else:\n                    first = mid + 1\n        return found\n\n    # Deletes oldest ingredient from stock table\n    def tableDeleteOldest(self):\n        self.list.delete(0)\n\n    # Deletes oldest shot with given type from stock table\n    def tableDeleteOldestShot(self, type):\n        found = False\n        count = 0\n        while not found:\n            if self.list.retrieve(count)[0].getType() == type:\n                self.list.delete(count)\n                found = True\n            else:\n                count += 1\n\n    # Retrieves ingredient with given expire date from stock table\n    def tableRetrieve(self, expiredate):\n        first = 0\n        last = self.getLength() - 1\n        found = False\n        while first <= last and not found:\n            mid = (first + last) // 2\n            current = self.list.retrieve(mid)[0]\n            if current.getExpiredate() == expiredate:\n                return current, True\n            else:\n                if expiredate < current.getExpiredate():\n                    last = mid - 1\n                else:\n                    first = mid + 1\n        return None, False\n\n    # Traverses stock table on ascending expire date\n    def traverseTable(self):\n        return self.list.traverse()\n\n    # Sorts stock table on ascending expire date\n    def sortTable(self):\n        \"\"\"\n        >>> t = StockTable()\n        >>> h = Honey(5)\n        >>> h2 = Honey(2)\n        >>> t.tableInsert(h)\n        True\n        >>> t.tableInsert(h2)\n        True\n        >>> t.tableDelete(2)\n        True\n        >>> t.tableInsert(h2)\n        True\n        >>> t.tableRetrieve(5)\n        >>> t.traverseTable()\n        >>> t.sortTable()\n        >>> t.print()\n        \"\"\"\n        swap = True\n        while swap:\n            swap = False\n            for i in range(0, self.list.getLength()-1):\n                current = self.list.retrieve(i)[0]\n                if current.getExpiredate() > self.list.retrieve(i + 1)[0].getExpiredate():\n                    self.list.delete(i)\n                    self.list.insert(i + 1, current)\n                    swap = True\n\n    # Function for testing purposes: prints traverseTable\n    def print(self):\n        t = self.traverseTable()\n        for i in t:\n            print(i.getExpiredate())\n","sub_path":"StockTable.py","file_name":"StockTable.py","file_ext":"py","file_size_in_byte":3750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"15096623","text":"from polynomials import Polynomial\n\n\nclass Vector:\n\n    def __init__(self, dictionary={}, printer=None, multiplier=None):\n        self.dictionary = {key: value for key, value in dictionary.items() if value}\n        self.printer = printer\n        self.multiplier = multiplier\n\n    def __hash__(self):\n        return hash(str(self))\n\n    @classmethod\n    def print_matrix(cls, matrix):\n        print()\n        w = max([len(str(e)) for row in matrix for e in row])\n        for row in matrix:\n            print('  ', '[' + ', '.join(map(lambda e: (w - len(str(e))) * ' ' + str(e), row)) + ']')\n        print()\n\n    def is_expressable(self, *args):\n        keys = set(self.dictionary)\n        for a in args:\n            keys |= set(a.dictionary)\n        keylist = sorted(keys)\n        # keydict = {k: i for i, k in enumerate(keylist)}\n        m, n = len(keylist), len(args) + 1\n        matrix = [[args[j][keylist[i]] for j in range(n - 1)] + [self[keylist[i]]] for i in range(m)]\n        rref = self.rref(matrix)\n        # self.print_matrix(rref)\n\n        pivots = []\n        for i in range(m):\n            for j in range(n):\n                if rref[i][j] != 0:\n                    pivots.append(j)\n                    break\n\n        return n - 1 not in pivots\n\n    @classmethod\n    def rref(cls, matrix):\n        assert all(type(e) == int for row in matrix for e in row)\n        m = len(matrix)\n        n = 0 if m == 0 else len(matrix[0])\n        matrix = [[e for e in row] for row in matrix]\n\n        def gcd(*args):\n            if len(args) <= 1:\n                return args[0]\n            if len(args) > 2:\n                return gcd(args[0], gcd(*args[1:]))\n            a, b = args[0], args[1]\n            while b != 0:\n                a, b = b, a % b\n            return a\n\n        def swap(mat, i, j):\n            mat[i], mat[j] = mat[j], mat[i]\n\n        def scale(mat, i, v):\n            for j in range(len(mat[i])):\n                mat[i][j] *= v\n\n        def iscale(mat, i, v=None):\n            v = gcd(*mat[i]) if v is None else v\n            for j in range(len(mat[i])):\n                assert mat[i][j] % v == 0\n                mat[i][j] //= v\n\n        def replace(mat, i, j, v):\n            for t in range(len(mat[j])):\n                mat[j][t] += v * mat[i][t]\n\n        def find_nonzeros_in_column(mat, rowstart, j):\n            return [t for t in range(rowstart, m) if mat[t][j] != 0]\n\n        row = 0\n        for col in range(n):\n            nonzeros = find_nonzeros_in_column(matrix, row, col)\n            if len(nonzeros) == 0:\n                continue\n\n            i = nonzeros[0]\n            iscale(matrix, i)\n            if matrix[i][col] < 0:\n                iscale(matrix, i, -1)\n\n            for j in nonzeros[1:]:\n                a, b = matrix[i][col], matrix[j][col]\n                d = gcd(a, b)\n                scale(matrix, j, a // d)\n                replace(matrix, i, j, -b // d)\n\n            if i != row:\n                swap(matrix, row, i)\n\n            row += 1\n\n        return matrix\n\n    @classmethod\n    def base(cls, key, printer=None, multiplier=None):\n        return cls({key: 1}, printer, multiplier)\n\n    def keys(self):\n        return self.dictionary.keys()\n\n    def values(self):\n        return self.dictionary.values()\n\n    def items(self):\n        return self.dictionary.items()\n\n    def is_singleton(self):\n        if len(self.dictionary) != 1:\n            return False\n        return list(self.dictionary.values())[0] == 1\n\n    def is_nonnegative(self):\n        return all(v > 0 for v in self.values())\n\n    def is_positive(self):\n        return not self.is_zero() and self.is_nonnegative()\n\n    def __len__(self):\n        return len(self.dictionary)\n\n    def __eq__(self, other):\n        return len((self - other).dictionary) == 0\n\n    def __iter__(self):\n        return self.dictionary.__iter__()\n\n    def __getitem__(self, item):\n        return self.dictionary.get(item, 0)\n\n    def __radd__(self, other):\n        return self.__add__(other)\n\n    def __add__(self, other):\n        if other == 0:\n            return self\n        if type(other) == type(self):\n            keys = self.keys() | other.keys()\n            return self.__class__(\n                {key: self[key] + other[key] for key in keys},\n                self.printer or other.printer,\n                self.multiplier or other.multiplier\n            )\n        else:\n            return other.__radd__(self)\n\n    def __sub__(self, other):\n        if other == 0:\n            return self\n        if type(other) == type(self):\n            keys = self.keys() | other.keys()\n            return self.__class__(\n                {key: self[key] - other[key] for key in keys},\n                self.printer or other.printer,\n                self.multiplier or other.multiplier\n            )\n        else:\n            return other.__rsub__(self)\n\n    def __mul__(self, other):\n        if type(other) in [int, Polynomial]:\n            return self.__class__(\n                {key: self[key] * other for key in self.keys()},\n                self.printer,\n                self.multiplier\n            )\n        elif type(other) == type(self):\n            ans = {}\n            for a, x in self.items():\n                for b, y in other.items():\n                    for m, coeff in self.multiplier(a, b) if self.multiplier else [(a * b, 1)]:\n                        ans[m] = ans.get(m, 0) + x * y * coeff\n            return self.__class__(\n                ans,\n                self.printer or other.printer,\n                self.multiplier or other.multiplier\n            )\n        else:\n            return self * self.base(other)\n\n    def __floordiv__(self, other):\n        assert type(other) == int\n        return Vector({key: value // other for (key, value) in self.items()})\n\n    def __rmul__(self, other):\n        return self.__mul__(other)\n\n    def __neg__(self):\n        return self.__mul__(-1)\n\n    def __pow__(self, other):\n        assert type(other) == int and other > 0\n        if other == 1:\n            return self\n        x = self ** (other // 2)\n        if other % 2 == 0:\n            return x * x\n        else:\n            return x * x * self\n\n    def is_zero(self):\n        return len(self) == 0\n\n    def _repr_coeff(self, coeff, key):\n        if type(coeff) == Polynomial and len(coeff) > 1:\n            return ' + (%s)*' % str(coeff)\n        try:\n            if key == '' and coeff > 0:\n                return ' + %s' % coeff\n            if key == '' and coeff < 0:\n                return ' - %s' % -coeff\n            if coeff == 1:\n                return ' + '\n            elif coeff == -1:\n                return ' - '\n            elif coeff > 0:\n                return ' + %s*' % coeff\n            else:\n                return ' - %s*' % -coeff\n        except TypeError:\n            return ' + (%s)*' % str(coeff)\n\n    def _print_sorted(self, sorted_items):\n        base = ''.join(self._repr_coeff(value, key) + key for key, value in sorted_items)\n        if base.startswith(' + '):\n            return base[3:]\n        elif base.startswith(' - '):\n            return '-' + base[3:]\n        else:\n            return '0'\n\n    def __repr__(self):\n        printer = self.printer or repr\n        sorted_items = sorted([(printer(key), value) for key, value in self.items()])\n        return self._print_sorted(sorted_items)\n","sub_path":"vectors.py","file_name":"vectors.py","file_ext":"py","file_size_in_byte":7329,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"652395880","text":"class Solution:\n    def __init__(self, ip):\n        self.ip = ip\n    # user input\n    @classmethod\n    def IP_input(self):\n        ip = input('Enter ip ip adrress: ')\n        return self(ip)\n\n\n    def check(self):\n        ip_adrress=self.ip\n        # seperate prefix from network id\n        seperate=[]\n        for i in ip_adrress.split('/'):\n            seperate.append(i)\n        # check if prefix in the range or not\n        if seperate[1]=='8' or seperate[1]=='16' or seperate[1]=='24':\n\n            try:\n            # seperate network id and convert it to integer\n                check_ip=[]\n                for j in seperate[0].split('.'):\n                    check_ip.append(int(j))\n\n \n                index=len(check_ip)\n                # check if ip in format : x.x.x.x/x \n                if index == 4:\n                    netID1=check_ip[0]\n                    netID2=check_ip[1]\n                    netID3=check_ip[2]\n                    netID4=check_ip[3]\n\n                    # =========== check class a =============\n                    if netID1 >=1 | netID1 <=127 and netID2 ==0 and netID3 ==0 and netID4 ==0:\n                        print('Class: A, Designation: Public')\n\n                    elif netID1 ==10 and netID2 >=0 | netID2 <=255 and netID3 >=0 | netID3 <=255 and netID4 >=0 | netID4 <=255:\n                        print('Class: A, Designation: Private')\n\n                    elif netID1 ==127 and netID2 >=0 | netID2 <=255 and netID3 >=0 | netID3 <=255 and netID4 >=1 | netID4 <=255:\n                        print('Class: A, Designation: Special')\n\n                    # ============= check class b================\n                    elif netID1 >=128 | netID1 <=191 and netID2 >=0 | netID2 <=255 and netID3 ==0 and netID4 ==0:\n                        print('Class: B, Designation: Public')\n\n                    elif netID1 ==172 and netID2 >=16 | netID2 <=31 and netID3 >=0 | netID3 <=255 and netID4 >=0 | netID4 <=255:\n                        print('Class: B, Designation: Private')\n\n                    # ============= check class c ================\n                    elif netID1 >=192 | netID1 <=223 and netID2 >=0 | netID2 <=255 and netID3 >=0 | netID3 <=255 and netID4 ==0:\n                        print('Class: C, Designation: Public')\n\n                    elif netID1 ==192 and netID2 ==168 and netID3 >=0 | netID3 <=255 and netID4 >=0 | netID4 <=255:\n                        print('Class: C, Designation: Private')\n\n                    # ============= check class d ===============\n                    elif netID1 >=224 | netID1 <=239 and netID2 >=0 | netID2 <=255 and netID3 >=0 | netID3 <=255 and netID4 >=0 | netID4 <=255:\n                        print('Class: D, Designation: Special')\n\n                    # ==============check class e ===============\n                    elif netID1 >=240 | netID1 <=255 and netID2 >=0 | netID2 <=255 and netID3 >=0 | netID3 <=255 and netID4 >=0 | netID4 <=255:\n                        print('Class: E, Designation: Special')\n                    else:\n                        print('number range between 0 and 255')\n                else:\n                    print('Please type IP address in format: x.x.x.x/x')\n                    \n            except:\n                print('please enter numbers (-_-)')\n\n            \n        else:\n            print('/x must be 8, 16 or 24')\n\n\n\n        \nIP = Solution.IP_input()\n\n\nif __name__ == '__main__':\n    IP.check()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"19508545","text":"import tensorflow as tf\n\nfrom tensorflow.python.framework import ops\n\nimport os\n\nfrom utils import montage_tf, weights_montage\nfrom constants import LOG_DIR\n\nslim = tf.contrib.slim\n\n\nclass SDNetTester:\n    def __init__(self, model, dataset, pre_processor, tag='default'):\n        tf.logging.set_verbosity(tf.logging.DEBUG)\n        self.sess = tf.Session()\n        self.graph = tf.Graph()\n        self.model = model\n        self.dataset = dataset\n        self.tag = tag\n        self.additional_info = None\n        self.im_per_smry = 4\n        self.summaries = {}\n        self.pre_processor = pre_processor\n        self.num_eval_steps = None\n        with self.sess.as_default():\n            with self.graph.as_default():\n                self.global_step = slim.create_global_step()\n\n    def get_save_dir(self):\n        fname = '{}_{}_{}_finetune'.format(self.dataset.name, self.model.name, self.tag)\n        if self.additional_info:\n            fname = '{}_{}'.format(fname, self.additional_info)\n        return os.path.join(LOG_DIR, '{}/'.format(fname))\n\n    def get_test_batch(self, dataset_id=None):\n        with tf.device('/cpu:0'):\n            if dataset_id:\n                test_set = self.dataset.get_split(dataset_id)\n                self.num_eval_steps = (self.dataset.get_num_dataset(dataset_id) / self.model.batch_size)\n            else:\n                test_set = self.dataset.get_testset()\n                self.num_eval_steps = (self.dataset.get_num_test() / self.model.batch_size)\n            print('Number of evaluation steps: {}'.format(self.num_eval_steps))\n            provider = slim.dataset_data_provider.DatasetDataProvider(test_set, num_readers=1, shuffle=False,\n                                                                      common_queue_capacity=20 * self.model.batch_size,\n                                                                      common_queue_min=10 * self.model.batch_size)\n\n            [img_test, label_test] = provider.get(['image', 'label'])\n            label_test -= self.dataset.label_offset\n\n            # Pre-process data\n            img_test = self.pre_processor.process_test(img_test)\n\n            # Make batches\n            imgs_test, labels_test = tf.train.batch([img_test, label_test], batch_size=self.model.batch_size,\n                                                    num_threads=8,\n                                                    capacity=512)\n            batch_queue = slim.prefetch_queue.prefetch_queue([imgs_test, labels_test])\n\n            return batch_queue.dequeue()\n\n    def get_random_test_crops(self):\n        with tf.device('/cpu:0'):\n            # Get eval-data\n            test_set = self.dataset.get_testset()\n            self.num_eval_steps = (self.dataset.get_num_test() / self.model.batch_size)\n            test_provider = slim.dataset_data_provider.DatasetDataProvider(test_set, num_readers=1, shuffle=False)\n            [img_test, label_test] = test_provider.get(['image', 'label'])\n            labels_test = tf.tile(tf.expand_dims(label_test, axis=0), [10, 1])\n            imgs_test_t = tf.tile(tf.expand_dims(img_test, axis=0), [10, 1, 1, 1])\n            imgs_test_p = tf.unstack(imgs_test_t, axis=0, num=10)\n            imgs_test_p = [self.pre_processor.process_transfer_test(im) for im in imgs_test_p]\n            imgs_test = tf.stack(imgs_test_p, axis=0)\n\n        return imgs_test, labels_test\n\n    def test_classifier(self, num_conv_trained=None, dataset_id=None):\n        if not self.additional_info:\n            self.additional_info = 'conv_{}'.format(num_conv_trained)\n        print('Restoring from: {}'.format(self.get_save_dir()))\n        with self.sess.as_default():\n            with self.graph.as_default():\n                # Get training batches\n                imgs_test, labels_test = self.get_test_batch(dataset_id)\n\n                # Get predictions\n                predictions = self.model.classifier(imgs_test, self.dataset.num_classes, training=False)\n\n                # Compute predicted label for accuracy\n                preds_test = tf.argmax(predictions, 1)\n\n                # Choose the metrics to compute:\n                names_to_values, names_to_updates = slim.metrics.aggregate_metric_map({\n                    'accuracy': slim.metrics.streaming_accuracy(preds_test, labels_test),\n                })\n                summary_ops = self.make_summaries(names_to_values)\n\n                # Start evaluation\n                slim.evaluation.evaluation_loop('', self.get_save_dir(), self.get_save_dir(),\n                                                num_evals=self.num_eval_steps,\n                                                max_number_of_evaluations=10,\n                                                eval_op=names_to_updates.values(),\n                                                summary_op=tf.summary.merge(summary_ops))\n\n    def test_classifier_voc(self, num_conv_trained=None):\n        self.additional_info = 'conv_{}'.format(num_conv_trained)\n        with self.sess.as_default():\n            with self.graph.as_default():\n                # Get training batches\n                imgs_test, labels_test = self.get_random_test_crops()\n\n                # Get predictions\n                preds_test = self.model.classifier(imgs_test, self.dataset.num_classes, training=False)\n                preds_test = tf.nn.sigmoid(preds_test)\n                preds_test = tf.reduce_mean(preds_test, axis=0, keep_dims=True)\n\n                # Compute mean Average Precision\n                map_test, summary_ops, update_ops = self.compute_mAP(labels_test, preds_test)\n                op = tf.summary.scalar('map_test', map_test)\n                op = tf.Print(op, [map_test], 'map_test', summarize=30)\n                summary_ops.append(op)\n\n                # Start evaluation\n                slim.evaluation.evaluation_loop('', self.get_save_dir(), self.get_save_dir(),\n                                                num_evals=self.num_eval_steps,\n                                                max_number_of_evaluations=100,\n                                                eval_op=update_ops,\n                                                summary_op=tf.summary.merge(summary_ops))\n\n    def compute_mAP(self, labels_test, preds_test):\n        summary_ops = []\n        update_ops = []\n        thresholds = [0.01 * i for i in range(101)]\n        map_test = tf.Variable(0, dtype=tf.float32, collections=[ops.GraphKeys.LOCAL_VARIABLES])\n        for c in range(20):\n            class_pred_test = tf.slice(preds_test, [0, c], size=[self.model.batch_size, 1])\n            class_label_test = tf.slice(labels_test, [0, c], size=[self.model.batch_size, 1])\n\n            # Choose the metrics to compute:\n            prec_test, update_prec_test = slim.metrics.streaming_precision_at_thresholds(\n                class_pred_test, class_label_test, thresholds)\n            rec_test, update_rec_test = slim.metrics.streaming_recall_at_thresholds(\n                class_pred_test, class_label_test, thresholds)\n            update_ops.append([update_prec_test, update_rec_test])\n\n            ap_test = tf.Variable(0, dtype=tf.float32, collections=[ops.GraphKeys.LOCAL_VARIABLES])\n            for i in range(11):\n                ap_test += tf.reduce_max(\n                    prec_test * tf.cast(tf.greater_equal(rec_test, 0.1 * i), tf.float32)) / 10\n            map_test += tf.to_float(ap_test) / 20.\n\n            op = tf.summary.scalar('ap_test_{}'.format(c), ap_test)\n            op = tf.Print(op, [ap_test], 'ap_test_{}'.format(c), summarize=30)\n            summary_ops.append(op)\n        return map_test, summary_ops, update_ops\n\n    def test_reconstruction(self):\n        model_dir = os.path.join(LOG_DIR, '{}_{}_{}/'.format(self.dataset.name, self.model.name, self.tag))\n        print('Restoring from: {}'.format(model_dir))\n        with self.sess.as_default():\n            with self.graph.as_default():\n                imgs_test, __ = self.get_test_batch()\n\n                # Create the model\n                dec_im, dec_pdrop = self.model.net(imgs_test, train=False)\n\n                # Create the summary ops such that they also print out to std output:\n                summary_ops = [tf.summary.image('images/autoencoded', montage_tf(dec_im[:24], 4, 6), max_outputs=1),\n                               tf.summary.image('images/fake', montage_tf(dec_pdrop[:24], 4, 6), max_outputs=1),\n                               tf.summary.image('images/original', montage_tf(imgs_test[:24], 4, 6), max_outputs=1)]\n                with tf.variable_scope('discriminator', reuse=True):\n                    weights_disc_1 = slim.variable('conv_1/weights')\n                summary_ops.append(tf.summary.image('images/weights_disc_1', weights_montage(weights_disc_1, 6, 16),\n                                                    max_outputs=1))\n\n                slim.evaluation.evaluation_loop('', model_dir, model_dir,\n                                                num_evals=1,\n                                                max_number_of_evaluations=50,\n                                                summary_op=tf.summary.merge(summary_ops))\n\n    def make_summaries(self, names_to_values):\n        # Create the summary ops such that they also print out to std output:\n        summary_ops = []\n        for metric_name, metric_value in names_to_values.iteritems():\n            op = tf.summary.scalar(metric_name, metric_value)\n            op = tf.Print(op, [metric_value], metric_name)\n            summary_ops.append(op)\n        return summary_ops\n\n    def test_classifier_cv(self, num_conv_trained=None, fold=0):\n        self.additional_info = 'conv_{}_fold_{}'.format(num_conv_trained, fold)\n        self.test_classifier(num_conv_trained)\n\n","sub_path":"eval/SDNetTester.py","file_name":"SDNetTester.py","file_ext":"py","file_size_in_byte":9723,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"612614106","text":"#************************************************************************************************\n#                       Convert Data nc to csv V3.0\n#************************************************************************************************\n# name: Jose Feliciano Ibañez Quiroz\n#************************************************************************************************\n \nimport matplotlib.pyplot as plt\nimport netCDF4\nimport numpy as np\nimport pandas as pd\nfrom netCDF4 import MFDataset\nfrom glob import glob           # to look for files.nc\nimport os\nfrom shutil import rmtree       # to remove a Directory that contain files\nimport os.path as path          # to verify whether exits a Directory\n \n \n# choosing every file.nc \ndef ls(expr = 'wrfout_d03*'):\n    return glob(expr)\n# Importing every file.nc\n \nFiles = ls()\nprint(len(Files))\n \nncfile = 'wrfout_d03_2018-05-31_18:00:00'     # el programa debe estar en la misma carpeta de las extensiones .nc\nrootgroup = netCDF4.Dataset(ncfile,'r', format='NETCDF3') # import data extension nc\ny = rootgroup.variables['XLAT'][:]      # read latitutde variable\nx = rootgroup.variables['XLONG'][:]     # read longitude variable\nalt = rootgroup.variables[\"HGT\"][:]     # read hight variables\nrootgroup.close()\n#********************************************************************************\n#                   convert  byte to string\n#                   ***********************\n#strs = [\"\" for x in range(0,24)]\n#for i in range(1,25):\n#    strs[i-1] = str(time1[i,:], \"utf-8\")\n#print(strs)\n#********************************************************************************\n#               looking for latitude near to Weather Station from Qollana\n#               *********************************************************\n \n#df2 = df2[['Time', 'T2s2','W10s2','Wds2']]          # set order of columns\n \n#********************************************************************************\n#               looking for latitude near to Weather Station from Qollana\n#               *********************************************************\nidx_lat = (y[0,:,1]>=-17.63)*(y[0,:,1]<=-17.62) # comparison of domains in each grid of 1000m2\nidxlat=np.nonzero(idx_lat)[0]                   # excluding domains out of range\nlat_near = y[0,idxlat,1].min()                  # excluding only one latitude\nidx_latf = y[0,:,1]==lat_near                   # getting the index of latitude\nidxlat=np.nonzero(idx_latf)[0]                  # ...........\nidxlat=idxlat+1\nprint(idxlat) \n#print(lat_near)   \n#********************************************************************************\n#               looking for longitude near to the Weather Station from Qollana\n#               **************************************************************\nidx_lon = (x[0,1,:]>=-65.29)*(x[0,1,:]<=-65.20) # comparison of domains in each grid of 1000m2\nidxlon=np.nonzero(idx_lon)[0]                   # excluding domains out of range\n#print(x[1,1,idx_lon])\nlon_near = x[0,1,idx_lon].min()                 # excluding only one latitude\nidx_lonf = x[0,1,:]==lon_near                   # getting the index of latitude\nidxlon=np.nonzero(idx_lonf)[0]                  # ...........\nidxlon = idxlon+1\nprint(idxlon)       \nprint(lon_near,lat_near)\n \nprint(idxlon)\nprint(idxlat)\nprint(x[0,idxlat,idxlon])\nprint(y[0,idxlat,idxlon])\n \nresolucion = x[0,0,1]-x[0,0,2]\nprint(resolucion)\nprint(alt.shape)\n \n \n#******************************************************************\n#           Capturando todas las variables con MFDATATEST\n#******************************************************************\n \nf = MFDataset(\"wrfout*00\")\ntime1 = f.variables[\"Times\"][:]             # read time in format byte\ntemp2 = f.variables['T2'][:]        # read temperature at 2m\nv10 = f.variables[\"V10\"][:]                 # read wind velocity component at 10m\nu10 = f.variables[\"U10\"][:]                 # read wind velocity component at 10m\n \n#********************************************************************************\n#                   convert  byte to string\n#                   ***********************\nstrs = [\"\" for x in range(0,len(Files))]\nfor i in range(0,len(Files)):\n    strs[i] = str(time1[i,:], \"utf-8\")\nprint(strs)\n \n#********************************************************************************\n#               Extract Data T2 at 2m\n#               *********************\nprint((temp2[0,idxlat,idxlon]))\ntemp2m = np.zeros(len(Files),dtype=\"f\")   # generating space for temp2 of 24 spaces\nfor i in range(0,len(Files)):\n    temp2m[i] = float(temp2[i,idxlat,idxlon]) #saving each temperature by date\n \ntemp2m = temp2m-273.15           # converter Kelvin degree to celsius degree\nprint(temp2m)\n \n#********************************************************************************\n#               Extract Data V10 at 10m\n#               ***********************\nprint((v10[0,idxlat,idxlon]))   \n \nv10m = np.zeros(len(Files),dtype=\"f\")    # generating a vector of zeros of 24 spaces\nfor i in range(0,len(Files)):\n    v10m[i] = float(v10[i,idxlat,idxlon]) # saving each component of velocity by date\n \nprint(v10m)\n \n#********************************************************************************\n#               Extract Data U10 at 10m\n#               ***********************\nprint((u10[0,idxlat,idxlon]))   \n \nu10m = np.zeros(len(Files),dtype=\"f\")    # generating a vector of zeros of 24 spaces\nfor i in range(0,len(Files)):\n    u10m[i] = float(u10[i,idxlat,idxlon]) # saving each component of velocity by date\n \nprint(u10m)\n \n#********************************************************************************\n#               Calculating the wind resultant\n#               ******************************\n#   both components generated the resultant R <- sqrt(U²+V²)\n# V ^   \n#   |  / R=sqrt(U²+V²)\n#   | /\n#   |/)  tetha= arctg(V/U)\n#   +---------->\n#               U\nR = np.sqrt((u10m*u10m) + (v10m*v10m))\nprint(\"\\n Resultant of the velocity of the wind in m/s\")\nprint(R)                                # Print the resultant\n \n#  generating the direction of the resultant\ntetha = np.arctan((v10m/u10m))          # tetha in radians\ntetha = tetha * 180/np.pi               # Converting to degrees\nprint(\"\\n Direccion of the velocity in degrees\")\n     \n# to convert tetha in positive degrees\nfor i,tetha_i in enumerate(tetha):\n    if tetha_i<0.0:\n        tetha[i] = tetha[i] + 360                 # Positive degrees\nprint(tetha)\n \nprint(len(tetha))\n \n#********************************************************************************\n#               Exporting Data to CSV format\n#               ****************************\n \ndf2 = pd.DataFrame({'Time': strs,\n                    'T2s_1s': temp2m,\n                    'W10s_1s': R,\n                    'Wds_1s': tetha\n                    })\n \ndf2 = df2[['Time', 'T2s_1s','W10s_1s','Wds_1s']]          # set order of columns\n \nprint(df2)\nprint(df2.dtypes)\n \n# generating date for file.csv\n \n \n \n#file_csv_name = \"df\"+file_sep[0] + \".csv\"   # generating the name for csv file\n \nprint(\"df_1s.csv\")                        # name of the extension csv\ndf2.to_csv(path_or_buf=\"df_1s.csv\")       # save dataframe dataframe.to_csv()\n","sub_path":"nctocsvN.py","file_name":"nctocsvN.py","file_ext":"py","file_size_in_byte":7180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"478644952","text":"\nfrom com.dtmilano.android.viewclient import ViewClient\nimport time\nimport os, subprocess, sys\nimport random\nimport itertools\nimport argparse\n\n######利用python3 -m weditor来查看安卓控件id#########################\nresolutions1 = ['1280,720,5', '1280,720,10', '1280,720,15', '1280,720,30', '1920,1080,5', '1920,1080,10',\n                   '1920,1080,15', '1920,1080,30']\nresolutions2 = ['1280,720,30', '1920,1080,30', '3840,2160,15']\nresolutions = ['1280,720,5', '1280,720,10', '1280,720,15', '1280,720,30', '1920,1080,5', '1920,1080,10',\n                   '1920,1080,15', '1920,1080,30', '3840,2160,5', '3840,2160,10', '3840,2160,15']\nfovs = ['50']\nglobal Switchdisplayfail\nSwitchdisplayfail = 0\ndef log(value):\n    print(value)\ndef adb_shell(cmd):\n    p = subprocess.getstatusoutput(cmd)\n    return p\ndef start_test():\n    global vc, device, cmd\n    \"\"\"采集指定抖音账号的关注推荐数据\n    \"\"\"\n    # pic = 0\n    log(u'准备测试SDK')\n    # 连设备\n    serialno = argv.s\n    # serialno = '10.33.48.245'\n    if serialno:\n        os.system('adb connect {}'.format(serialno or ''))\n        time.sleep(3)\n\n    device, serialno = ViewClient.connectToDeviceOrExit(serialno=serialno)\n    vc = ViewClient(device, serialno, autodump=False)\n\n\n    op = adb_shell('adb shell getprop ro.build.version.release')\n    if int(op[0]) == 0:\n        AndroidId = int(op[1])\n        print(AndroidId)\n        if AndroidId == 10:\n            log(u'Android 10')\n            cmd = \"adb shell 'cd /sdcard/Android/data/com.llvision.glass3.api.test/files/Movies && ls -l |wc -l'\"\n        else:\n            log(u'Android 9-')\n            cmd = \"adb shell 'cd sdcard/DCIM/LLVisionCamera && ls -l |wc -l'\"\n    else:\n        print('获取安卓版本失败，退出！！')\n    # 强制关闭SDK\n    log(u'强制关闭SDK.')\n    device.shell('am force-stop com.llvision.glass3.api.test')\n    time.sleep(2)\n\n    # 启动SDK\n    log(u'启动SDK.')\n    device.shell('am start -n com.llvision.glass3.api.test/com.llvision.glass3.api.test.MainActivity')\n    time.sleep(5)\n    # 等待连接成功\n    log(u'等待连接成功.')\n\n    # 点击搜索按钮\n    vc.dump()\n\ndef powerkey():\n    os.system('adb shell input keyevent 26')\ndef screenon():\n    p = subprocess.Popen('adb shell dumpsys window policy', shell=True, stdout=subprocess.PIPE)\n    # stdout, stderr = cmdutils.run_main('adb shell dumpsys window policy', shell=Ture, args=None)\n    stdout = p.stdout.readlines()\n    # for line in out:\n    #     stdout = line.strip()\n        # print(stdout)\n    # print(str(stdout))\n    if 'screenState=SCREEN_STATE_ON' in str(stdout):\n        log(u'解锁屏幕')\n    else:\n        powerkey()\n\n    os.system('adb shell input keyevent 82')\ndef ocscreen():\n    for octime in range(99999999):\n        powerkey()\n        time.sleep(5)\n        screenon()\n        log(u'熄灭屏' + str(octime+1) + '次')\n        time.sleep(5)\n\ndef camera_test():\n    start_test()\n    camera_test_btn = vc.findViewById('com.llvision.glass3.api.test:id/id_camera_btn')\n\n    if camera_test_btn:\n        log(u'点击camera test，跳转到camera test页面.')\n        # print('123')\n        camera_test_btn.touch()\n        time.sleep(0.2)\n        vc.dump()\nglobal pic\npic = -3\ndef takepicture(ti):\n    for takepicturetime in range(0, ti):\n        btn_takepicture = vc.findViewById('com.llvision.glass3.api.test:id/btn_takepicture')\n        if btn_takepicture:\n            log(u'抓帧.')\n            btn_takepicture.touch()\n            time.sleep(0.3)\n            vc.dump()\n            global pic\n            piclist = []\n            result = adb_shell(cmd)\n            if int(result[0]) == 0:\n                pic1 = int(result[1])\n                piclist.append(pic1)\n                if argv.OC_camera:\n                    shift = 1\n                else:\n                    shift = 3\n                if pic1 >= pic + shift:\n                    pic = pic1\n                    print(pic)\n                else:\n                    print(pic)\n                    print(piclist)\n                    log('camera 打开失败,打开关闭执行' + str((pic1 - piclist[-1])/shift))\n                    sys.exit()\n\n                log(u'抓帧' + str(takepicturetime+1) + '次')\n            else:\n                print('命令执行失败，退出！！')\n                sys.exit()\ndef takephoto(ti):\n    for takephototime in range(0, ti):\n        btn_takephoto = vc.findViewById('com.llvision.glass3.api.test:id/btn_takepicture2')\n        if btn_takephoto:\n            log(u'拍照.')\n            btn_takephoto.touch()\n            time.sleep(0.5)\n            vc.dump()\n            log(u'拍照' + str(takephototime+1) + '次')\ndef Record(ti):\n\n    for Recordtime in range(0, ti):\n        btn_record = vc.findViewById('com.llvision.glass3.api.test:id/btn_record')\n        if btn_record:\n            log(u'开始录像.')\n            btn_record.touch()\n            time.sleep(30)\n            vc.dump()\n            btn_record.touch()\n            vc.dump()\n            log(u'录像' + str(Recordtime + 1) + '次')\ndef render_Record(ti):\n\n    for Recordtime in range(0, ti):\n        render_recod_btn = vc.findViewById('com.llvision.glass3.api.test:id/id_render_recod_btn')\n        if not render_recod_btn:\n            device.drag((345, 1850), (345, 1250), duration=100)\n            vc.dump()\n        render_recod_btn = vc.findViewById('com.llvision.glass3.api.test:id/id_render_recod_btn')\n\n        if render_recod_btn:\n            log(u'开始叠加录像')\n            render_recod_btn.touch()\n            time.sleep(30)\n            vc.dump()\n            render_recod_btn.touch()\n            device.drag((345, 1250), (345, 1850), duration=100)\n            vc.dump()\n            # occameratime =+ 1\n            log(u'叠加录像' + str(Recordtime + 1) + '次')\n\ndef Ocamera(ti):\n\n    # occameratime = 0\n    for occameratime in range(ti):\n        btn_camera = vc.findViewById('com.llvision.glass3.api.test:id/btn_camera')\n        if btn_camera:\n            log(u'打开/关闭camera.')\n            btn_camera.touch()\n            time.sleep(10)\n            if (occameratime % 2) == 0:\n                time.sleep(5)\n                vc.dump()\n\n                if argv.OC_camera:\n                    takepicture(1)\n                else:\n                    takepicture(1)\n                    takephoto(1)\n                    Record(1)\n                    render_Record(1)\n\n            else:\n                log(u'打开/关闭camera' + str(occameratime/2 + 0.5) + '次')\ndef Set_resolution(resolution):\n\n    id_spinner_view = vc.findViewById('com.llvision.glass3.api.test:id/id_spinner_view')\n    if id_spinner_view:\n        log(u'选择分辨率.' + str(resolution))\n        id_spinner_view.touch()\n        time.sleep(0.5)\n        vc.dump()\n\n        # 切换分辨率\n        user_tab = vc.findViewWithText(resolution)\n        if not user_tab:\n            device.drag((345, 550), (345, 1550), duration=100)\n            vc.dump()\n        user_tab = vc.findViewWithText(resolution)\n        user_tab.touch()\n        time.sleep(0.2)\n        vc.dump()\n        Ocamera(2)\ndef Set_FOV(fov):\n    id_spinner_view = vc.findViewById('com.llvision.glass3.api.test:id/spinner_fov')\n    if id_spinner_view:\n        log(u'选择fov.' + str(fov))\n        id_spinner_view.touch()\n        time.sleep(0.5)\n        vc.dump()\n\n        # 切换分辨率\n        user_tab = vc.findViewWithText(fov)\n        if not user_tab:\n            device.drag((345, 550), (345, 1550), duration=100)\n            vc.dump()\n        user_tab = vc.findViewWithText(fov)\n        user_tab.touch()\n        time.sleep(0.2)\n        vc.dump()\n        Ocamera(2)\ndef Set_ReOv(resolution, fov):\n    id_spinner_view = vc.findViewById('com.llvision.glass3.api.test:id/id_spinner_view')\n    if id_spinner_view:\n        log(u'选择分辨率.' + str(resolution))\n        id_spinner_view.touch()\n        time.sleep(0.5)\n        vc.dump()\n\n        # 切换分辨率\n        user_tab = vc.findViewWithText(resolution)\n        if not user_tab:\n            device.drag((345, 550), (345, 1550), duration=100)\n            vc.dump()\n        user_tab = vc.findViewWithText(resolution)\n        user_tab.touch()\n        time.sleep(0.2)\n        vc.dump()\n    id_spinner_view = vc.findViewById('com.llvision.glass3.api.test:id/spinner_fov')\n    if id_spinner_view:\n        log(u'选择fov.' + str(fov))\n        id_spinner_view.touch()\n        time.sleep(0.5)\n        vc.dump()\n\n        # 切换分辨率\n        user_tab = vc.findViewWithText(fov)\n        if not user_tab:\n            device.drag((345, 550), (345, 1550), duration=100)\n            vc.dump()\n        user_tab = vc.findViewWithText(fov)\n        user_tab.touch()\n        time.sleep(0.2)\n        vc.dump()\n        Ocamera(2)\n\ndef Switch_fov():\n    for resolution in resolutions2:\n        Set_resolution(resolution)\n        if resolution in ['3840,2160,5', '3840,2160,10', '3840,2160,15']:\n            fovs1 = ['100', '101']\n        else:\n            fovs1 = ['101', '100', '70', '50']\n        for fov in fovs1:\n            Set_FOV(fov)\n\ndef Switch_fov1():\n    fovs1 = ['101', '100', '70', '50']\n    def lists_combination(lists, code=' '):\n        '''输入多个列表组成的列表, 输出其中每个列表所有元素可能的所有排列组合\n        code用于分隔每个元素'''\n        try:\n            import reduce\n        except:\n            from functools import reduce\n\n        def myfunc(list1, list2):\n            return [str(i) + code + str(j) for i in list1 for j in list2]\n\n        return reduce(myfunc, lists)\n    res = lists_combination([resolutions2, fovs1])\n    reslist = list(itertools.combinations(res, 2))\n    # print(reslist)\n    for i in range(len(reslist)):\n        # print(reslist[i])\n        if '3840,2160,15 70' in reslist[i]:\n            print(reslist[i])\n        elif '3840,2160,15 50' in reslist[i]:\n            print(reslist[i])\n        else:\n            ReOv1 = reslist[i][0].split(' ')\n            ReOv2 = reslist[i][1].split(' ')\n            resolution_1 = ReOv1[0]\n            fov_1 = ReOv1[1]\n            resolution_2 = ReOv2[0]\n            fov_2 = ReOv2[1]\n            print(resolution_1, fov_1, resolution_2, fov_2)\n            Set_ReOv(resolution_1, fov_1)\n            Set_ReOv(resolution_2, fov_2)\n\ndef Switch_resolution():\n    for fov in fovs:\n        Set_FOV(fov)\n        if fov in ['100', '101']:\n            for resolution in resolutions:\n                # resolution = random.choice(resolutions)\n                Set_resolution(resolution)\n        if fov in ['50', '70']:\n            for resolution in resolutions1:\n                # resolution = random.choice(resolutions1)\n                Set_resolution(resolution)\n    # Set_resolution(u'1280,720,5')\n    # # takepicture(1)\n    # # Switch_resolution(u'1280,720,1')\n    # Set_resolution(u'1280,720,15')\n    # # takepicture(1)\n    # Set_resolution(u'1280,720,30')\n    # # takepicture(1)\n    # Set_resolution(u'1920,1080,10')\n    # # takepicture(1)\n    # Set_resolution(u'1920,1080,5')\n    # # takepicture(1)\n    # Set_resolution(u'1920,1080,15')\n    # # takepicture(1)\n    # Set_resolution(u'1920,1080,30')\n    # # takepicture(1)\n    # Set_resolution(u'3840,2160,10')\n    # # takepicture(1)\n    # Set_resolution(u'3840,2160,5')\n    # # takepicture(1)\n    # Set_resolution(u'3840,2160,15')\n    # takepicture(1)\ndef OC_camera(resolution):\n\n    Set_ReOv(resolution,'100')\n    log(u'开始打开关闭camera测试' + str(resolution))\n    Ocamera(9999999999)\n\ndef OC_record(resolution):\n    Set_resolution(resolution)\n    Record(9999999999)\ndef displaytest():\n    start_test()\n    btn_display = vc.findViewById('com.llvision.glass3.api.test:id/id_display_btn')\n    if btn_display:\n        log(u'进入display界面.')\n        btn_display.touch()\n        time.sleep(0.5)\n        vc.dump()\ndef stopdisplay():\n    stopdisplay = vc.findViewById('com.llvision.glass3.api.test:id/btn_stoplcd')\n    if stopdisplay:\n        log(u'stop.')\n        stopdisplay.touch()\n        time.sleep(0.5)\n        vc.dump()\ndef sync_trasfer():\n    rb_sync_trasfe = vc.findViewById('com.llvision.glass3.api.test:id/rb_sync_trasfer')\n    if rb_sync_trasfe:\n        log(u'打开同屏.')\n        rb_sync_trasfe.touch()\n        time.sleep(1)\n        vc.dump()\n    start_bt = vc.findViewById('android:id/button1')\n    if start_bt:\n        log(u'立即开始.')\n        start_bt.touch()\n        time.sleep(5)\n        vc.dump()\n        stopdisplay()\n    else:\n        global Switchdisplayfail\n        Switchdisplayfail=Switchdisplayfail+1\n\n        log(Switchdisplayfail)\n\ndef overlay_trasfer():\n    rb_overlay_trasfer = vc.findViewById('com.llvision.glass3.api.test:id/rb_overlay_trasfer')\n    if rb_overlay_trasfer:\n        log(u'打开overlay_trasfer.')\n        rb_overlay_trasfer.touch()\n        time.sleep(5)\n        vc.dump()\n        stopdisplay()\n    else:\n        global Switchdisplayfail\n        Switchdisplayfail=Switchdisplayfail+1\n\n        log(Switchdisplayfail)\n\ndef fragment_trasfer():\n    rb_fragment_trasfer = vc.findViewById('com.llvision.glass3.api.test:id/rb_fragment_trasfer')\n    if rb_fragment_trasfer:\n        log(u'打开fragment_trasfer.')\n        rb_fragment_trasfer.touch()\n        time.sleep(5)\n        vc.dump()\n        stopdisplay()\n    else:\n        global Switchdisplayfail\n        Switchdisplayfail=Switchdisplayfail+1\n        log(Switchdisplayfail)\n\ndef Switchdisplay():\n    for Switchdisplaytime in range(99999999):\n        random.choice([sync_trasfer, overlay_trasfer, fragment_trasfer])()\n        log(Switchdisplayfail)\n        if Switchdisplayfail == 3:\n            return start_test()\n\n        log(u'display切换' + str(Switchdisplaytime+1) + '次')\n# displaytest()\n# Switchdisplay()\n# camera_test()\n# Switch_resolution()\n# Switch_fov1()\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Process some integers.')\n    parser.add_argument(\"-s\", '--s', help=\"devices\", default='192.168.1.148')\n    parser.add_argument(\"-cam\", '--camera', help=\"camera test or display test\", action='store_true')\n    parser.add_argument(\"-OC\", '--OC_camera', help=\"OC_camera test\", action='store_true')\n    parser.add_argument(\"-SR\", '--Switch_resolution', help=\"Switch resolution\", action='store_true')\n    parser.add_argument(\"-SF1\", '--Switch_fov1', help=\"Switch fov1循环完毕\", action='store_true')\n    parser.add_argument(\"-SF\", '--Switch_fov', help=\"Switch fov顺序执行\", action='store_true')\n    parser.add_argument(\"-re\", '--resolution', help=\"打开关闭设置resolution\", default=0, choices=[0, 1])\n    argv = parser.parse_args()\n    if argv.camera:\n        camera_test()\n\n        if argv.Switch_resolution:\n            Switch_resolution()\n        elif argv.Switch_fov:\n            Switch_fov()\n        elif argv.Switch_fov1:\n            Switch_fov1()\n        elif argv.OC_camera:\n            if argv.resolution == 0:\n                resolution123 = '3840,2160,15'\n            if argv.resolution == 1:\n                resolution123 = '1920,1080,30'\n            OC_camera(resolution123)\n    else:\n        displaytest()\n        Switchdisplay()","sub_path":"SDK.py","file_name":"SDK.py","file_ext":"py","file_size_in_byte":15117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"320613072","text":"from skyfield.api import load\nfrom skyfield.api import Topos\nfrom skyfield import almanac\nfrom datetime import datetime\nfrom pytz import timezone\n\n# global conj, sunset, m_alt, m_az, m_d \nconj = []\nsunset = []\nmoon_alt = []\nmoon_az = []\nsun_alt = []\nsun_az = []\n\n\njkt = timezone('Asia/Jakarta')\nts = load.timescale()\ne = load('de421.bsp')\n\nmoon = e['moon']\nearth = e['earth']\n\nobsUAD = Topos('7.83305556 S', '110.38305556 E')\nloc = earth + obsUAD\n\nt0 = ts.utc(2020)\nt1 = ts.utc(2021)\nf = almanac.oppositions_conjunctions(e, e['moon'])\nt, y = almanac.find_discrete(t0, t1, f)\n\n# print(t0.astimezone(jkt), t1.astimezone(jkt))\n\nfor ti, yi in zip(t, y):\n    \n    if(yi == 1):\n        # t0 = ts.utc(ti.utc[0], ti.utc[1], ti.utc[2])\n        # t1 = ts.utc(ti.utc[0], ti.utc[1], ti.utc[2]+1)\n        # t_rs, y_rs = almanac.find_discrete(t0, t1, almanac.sunrise_sunset(e, obsUAD))\n        # print(t0.astimezone(jkt), t1.astimezone(jkt))\n\n        # for t_sun, y_sun in zip(t_rs, y_rs):\n        #     global sunset\n        #     if (y_sun == False):\n        #         sunset = t_sun\n\n        \n        conj.append(ti)\n        # print(ti.astimezone(jkt))\n        # print(ti.astimezone(jkt))\n        # print('sunset: ', sunset.astimezone(jkt))\n    else:\n        pass\n\nfor i in conj:\n    i_utc = i.utc\n    t0 = ts.utc(i_utc[0], i_utc[1], i_utc[2], i_utc[3], i_utc[4], i_utc[5])\n    t1 = ts.utc(i_utc[0], i_utc[1], i_utc[2]+1, i_utc[3], i_utc[4], i_utc[5])\n    t_rs, y_rs = almanac.find_discrete(t0, t1, almanac.sunrise_sunset(e, obsUAD))\n    for t_sun, y_sun in zip(t_rs, y_rs):\n        if (y_sun == False):\n            sunset.append(t_sun)\n            # sunset = t_sun\n        else:\n            pass\n\nfor s in sunset:\n    s_utc = s.utc\n    astro_moon = loc.at(ts.utc(s_utc[0], s_utc[1], s_utc[2], s_utc[3], s_utc[4], s_utc[5])).observe(moon)\n    astro_sun = loc.at(ts.utc(s_utc[0], s_utc[1], s_utc[2], s_utc[3], s_utc[4], s_utc[5])).observe(e['sun'])\n\n    m_alt, m_az, m_d = astro_moon.apparent().altaz()\n    s_alt, s_az, s_d = astro_sun.apparent().altaz()\n\n    moon_alt.append(m_alt)\n    moon_az.append(m_az)\n    sun_alt.append(s_alt)\n    sun_az.append(s_az)\n\nf = open(\"Moon Conjunction.txt\",\"w+\")\n\n# Print All Values\nheader_text = \"Conjunction Time;Sunset Time;Sun Altitude;Sun Azimuth;Moon Altitude;Moon Azimuth \\n\"\nprint(header_text)\nf.write(header_text)\n\nfor t_conj, t_sunset, s_alt, s_azm, m_alt, m_az in zip(conj, sunset, sun_alt, sun_az, moon_alt, moon_az):\n    f.write(str(t_conj.astimezone(jkt)) + \";\")\n    f.write(str(t_sunset.astimezone(jkt)) + \";\")\n    f.write(str(s_alt) + \";\")\n    f.write(str(s_azm) + \";\")\n    f.write(str(m_alt) + \";\")\n    f.write(str(m_az) + \"\\n\")\n    print(t_conj.astimezone(jkt), end=\";\")\n    print(t_sunset.astimezone(jkt), end=\";\")\n    print(s_alt, end=\";\")\n    print(s_azm, end=\";\")\n    print(m_alt, end=\";\")\n    print(m_az)\n\nf.close()\n\n\n# print(i.astimezone(jkt), end=\"\\t\")\n# print(sunset.astimezone(jkt), end=\"\\t\")\n# print(m_alt, end=\"\\t\")\n# print(m_az)","sub_path":"programs/moon_conjunction.py","file_name":"moon_conjunction.py","file_ext":"py","file_size_in_byte":2980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"344509756","text":"\"\"\"\nA peak element is an element that is greater than its neighbors.\n\nGiven an input array where num[i] ≠ num[i+1], find a peak element and return its index.\n\nThe array may contain multiple peaks, in that case return the index to any one of the peaks is fine.\n\nYou may imagine that num[-1] = num[n] = -∞.\n\nFor example, in array [1, 2, 3, 1], 3 is a peak element and your function should return the index number 2.\n\nGiven an array of integers. Find a peak element in it.\nAn array element is peak if it is NOT smaller than its neighbors.\nFor corner elements, we need to consider only one neighbor.\nFor example, for input array {5, 10, 20, 15}, 20 is the only peak element.\nFor input array {10, 20, 15, 2, 23, 90, 67}, there are two peak elements: 20 and 90.\nNote that we need to return any one peak element.\n\nFollowing corner cases give better idea about the problem.\n1) If input array is sorted in strictly increasing order, the last element is always a peak element.\nFor example, 50 is peak element in {10, 20, 30, 40, 50}.\n2) If input array is sorted in strictly decreasing order, the first element is always a peak element. 1\n00 is the peak element in {100, 80, 60, 50, 20}.\n3) If all elements of input array are same, every element is a peak element.\n\n\nIt is clear from above examples that there is always a peak element in input array in any input array.\n\nA simple solution is to do a linear scan of array and as soon as we find a peak element, we return it.\nThe worst case time complexity of this method would be O(n).\n\nCan we find a peak element in worst time complexity better than O(n)?\nWe can use Divide and Conquer to find a peak in O(Logn) time. The idea is Binary Search based,\nwe compare middle element with its neighbors.\nIf middle element is not smaller than any of its neighbors, then we return it.\nIf the middle element is smaller than the its left neighbor, then there is always a peak in left half\n(Why? take few examples).\nIf the middle element is smaller than the its right neighbor,\nthen there is always a peak in right half (due to same reason as left half).\n\"\"\"\n\nclass Solution:\n    def findPeakElement(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        self.findPeakElement(nums,0,len(nums)-1,len(nums))\n        # A python 3 program to find a peak\n        # element element using divide and conquer\n\n        # A binary search based function\n        # that returns index of a peak element\n        def findPeakUtil(arr, low, high, n):\n\n            # Find index of middle element\n            # (low + high)/2\n            mid = low + (high - low) / 2\n            mid = int(mid)\n\n            # Compare middle element with its\n            # neighbours (if neighbours exist)\n\n        if ((mid == 0 or arr[mid - 1] <= arr[mid]) and\n                (mid == n - 1 or arr[mid + 1] <= arr[mid])):\n            return mid\n\n            # If middle element is not peak and\n            # its left neighbour is greater\n            # than it, then left half must\n            # have a peak element\n        elif (mid > 0 and arr[mid - 1] > arr[mid]):\n            return findPeakUtil(arr, low, (mid - 1), n)\n\n            # If middle element is not peak and\n            # its right neighbour is greater\n            # than it, then right half must\n            # have a peak element\n        else:\n            return findPeakUtil(arr, (mid + 1), high, n)\n\n        # A wrapper over recursive\n        # function findPeakUtil()\n        def findPeak(arr, n):\n\n            return findPeakUtil(arr, 0, n - 1, n)\n\n        # Driver code\n        arr = [1, 3, 20, 4, 1, 0]\n        n = len(arr)\n        print(\"Index of a peak point is\", findPeak(arr, n))","sub_path":"Coding Interview Questions/find_peak_element.py","file_name":"find_peak_element.py","file_ext":"py","file_size_in_byte":3690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"318806307","text":"import scrapy\nimport bs4\nfrom ..items import DangdangwangItem\n\nclass DangdangwangSpider(scrapy.Spider):\n    name = 'dangdangwang'\n    allowed_domains = ['http://bang.dangdang.com']\n    start_urls = []\n    for i in range(3):\n        url = 'http://bang.dangdang.com/books/bestsellers/01.00.00.00.00.00-year-2018-0-1-' + str(i + 1)\n        start_urls.append(url)\n    print(start_urls)\n\n    #def parse(self, response):\n    def parse(self, response):\n        datas = bs4.BeautifulSoup(response.text,'html.parser').find('ul',class_='bang_list clearfix bang_list_mode').find_all('li')\n        for data in datas:\n            item = DangdangwangItem()\n            item['name'] = data.find('div', class_=\"name\").find('test_1')['title']\n            item['author'] = data.find('div', class_=\"publisher_info\").text\n            item['price'] = data.find('div', class_=\"price\").find('span', class_=\"price_n\").text\n            yield item\n","sub_path":"风变编程/python爬虫-山顶班/fb_13/dangdangwang/dangdangwang/spiders/bestsellers.py","file_name":"bestsellers.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"427148527","text":"import json\nfrom .. import handler, access, tasks, protocol, forms, error, task\n\n\nclass StartHandler(handler.Handler):\n    \"\"\"\n        start robot trade service handler\n    \"\"\"\n    @access.exptproc\n    @access.needtoken\n    def post(self):\n        \"\"\"\n            process start robot trade service task\n        :param args:\n        :param kwargs:\n        :return:\n        \"\"\"\n        if self.request.body is None:\n            raise error.missing_parameters\n\n        # get config from post json data\n        config = json.loads(self.request.body.decode())\n\n        # get callback url\n        callback = config.get('callback')\n\n        # manage task\n        task.manager.take(tasks.trade.TradeService.__name__, tasks.trade.TradeService(callback=callback, config=config))\n\n        self.write(protocol.success())\n\n\nclass StopHandler(handler.Handler):\n    \"\"\"\n        stop robot trade service handler\n    \"\"\"\n    @access.exptproc\n    @access.needtoken\n    def post(self):\n        \"\"\"\n            process start robot trade service task\n        :param args:\n        :param kwargs:\n        :return:\n        \"\"\"\n        # manage task\n        task.manager.free(tasks.trade.TradeService.__name__)\n\n        self.write(protocol.success())","sub_path":"app/svc/app/atm/handlers/trade.py","file_name":"trade.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"459383536","text":"import urllib.request\r\nimport re\r\n\r\nnames = ['path', 'author', 'sex', 'birthday', 'header', 'created', 'sphere', 'genre_fi', 'type', 'topic', 'chronotop',\r\n         'style', 'audience_age', 'audience_level', 'audience_size', 'source', 'publication', 'publisher', 'publ_year',\r\n         'medium', 'country', 'region', 'language']\r\n\r\n\r\ndef generate_links():\r\n    base = 'http://vechorka.ru/rubrics/russia/news/'\r\n    res = []\r\n    for i in range(1, 45):\r\n        page_link = base + str(i)\r\n        try:\r\n            page_text = urllib.request.urlopen(page_link).read().decode('utf-8')\r\n            page_news_links = get_local_links(page_text)\r\n            for elem in page_news_links:\r\n                res.append(elem)\r\n        except:\r\n            print('Error generation')\r\n    return res\r\n\r\n\r\ndef get_local_links(text):\r\n    arr = re.findall('<h3><a href=\"(.*)\">', text)\r\n    res = []\r\n    base = 'http://vechorka.ru'\r\n    for elem in arr:\r\n        res.append(base + elem)\r\n    return res\r\n\r\n\r\ndef get_page_text(link):\r\n    text = None\r\n    try:\r\n        data = urllib.request.urlopen(link)\r\n        text = data.read().decode('utf-8')\r\n    except:\r\n        print('Error download')\r\n    return text\r\n\r\n\r\ndef get_metadata(text, link):\r\n    global links\r\n    print(links.index(link), ' of ', len(links))\r\n    general_dict = dict()\r\n    for elem in names:\r\n        date = ''\r\n        if elem in ['sex', 'birthday', 'genre_fi', 'type', 'chronotop', 'publisher']:\r\n            general_dict[elem] = ''\r\n        elif elem == 'sphere':\r\n            general_dict[elem] = 'публицистика'\r\n        elif elem == 'style':\r\n            general_dict[elem] = 'нейтральный'\r\n        elif elem == 'audience_age':\r\n            general_dict[elem] = 'н-возраст'\r\n        elif elem == 'audience_level':\r\n            general_dict[elem] = 'н-уровень'\r\n        elif elem == 'audience_size':\r\n            general_dict[elem] = 'городская'\r\n        elif elem == 'publication':\r\n            general_dict[elem] = 'Вечерний Ставрополь'\r\n        elif elem == 'medium':\r\n            general_dict[elem] = 'газета'\r\n        elif elem == 'country':\r\n            general_dict[elem] = 'Россия'\r\n        elif elem == 'region':\r\n            general_dict[elem] = 'Ставропольский край'\r\n        elif elem == 'language':\r\n            general_dict[elem] = 'ru'\r\n        elif elem == 'path':\r\n            general_dict[elem] = ''\r\n        elif elem == 'author':\r\n            general_dict[elem] = ''\r\n        elif elem == 'header':\r\n            head = re.findall('<title>(.*)</title>', text)\r\n            general_dict[elem] = head[0]\r\n        elif elem == 'created':\r\n            date = re.findall('datetime=\"(.*)T', text)\r\n            date = date[0].split('-')\r\n            date = reversed(date)\r\n            date = '.'.join(date)\r\n            general_dict[elem] = date\r\n        elif elem == 'topic':\r\n            general_dict[elem] = ''\r\n            tags = re.findall('<span class=\"icon14 tags\"><a href=\"/tags/(.*)</a></span> ', text)\r\n            tags = tags[0].split('</a>, <a href=\"/tags/')\r\n            for i, elem in enumerate(tags):\r\n                tags[i] = elem.split('>')[1]\r\n            general_dict[elem] = ', '.join(tags)\r\n        elif elem == 'source':\r\n            general_dict[elem] = link\r\n        elif elem == 'publ_year':\r\n            general_dict[elem] = date.split('-')[-1]\r\n    res = []\r\n    for elem in names:\r\n        res.append(general_dict[elem])\r\n    return '\\t'.join(res), general_dict\r\n\r\n\r\ndef main():\r\n    global links\r\n    links = generate_links()\r\n    csv_data = ['\\t'.join(names)]\r\n    for link in links:\r\n        text = get_page_text(link)\r\n        if text:\r\n            try:\r\n                metadata, dict_meta = get_metadata(text, link)\r\n                csv_data.append(metadata)\r\n            except:\r\n                pass\r\n    csv = open('metadata.csv', 'w')\r\n    csv.write('\\n'.join(csv_data))\r\n    csv.close()\r\n\r\nmain()\r\n","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":4011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"132650795","text":"import matplotlib.pyplot as plt\nimport pandas as pd\npd.set_option('display.max_columns', 10000, 'display.max_rows', 10000)\n\nplt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\nplt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号\n\n\ndata_dir = 'D:/python/venv/2020year/2020 IKCEST/train_data_all'\nfile = 'city_A'\nnames = ['density.csv', 'grid_attr.csv', 'infection.csv', 'migration.csv', 'transfer.csv', 'weather.csv']\n'''\ninfection.csv\n城市中每个区域每天增长人数\n'''\n'''\nmigration.csv\n城市之间的人口迁移，有一个迁移指数不知道定义\n迁移指数：可能就是一个衡量城市之间迁移强度的指标\n\ndensity.csv\n网格人流量指数\n\ntransfer.csv\n网格关联强度\n\ngrid_attr.csv\n网格归属区域\n\nweather.csv\n城市天气数据\n'''\nIndex = [\n    ['data', 'hour', 'grid_x', 'grid_y', 'Population_flow_index'],\n    ['grid_x', 'grid_y', 'region_id'],\n    ['city', 'region_id', 'data', 'num_new_persons'],\n    ['data', 'departure_city', 'arrival_city', 'migration_scale_index'],\n    ['hour', 'start_grid_x', 'start_grid_y', 'end_grid_x', 'end_grid_y', 'transfer_intensity'],\n    ['data', 'hour', 'temperature', 'humidity', 'wind_direction', 'wind_speed', 'wind_force', 'weather']\n]\n\ni = 2\nname = names[i]\ndir = data_dir + '/' + file + '/' + name\nprint('目前文件为{}'.format(name))\ndata = pd.read_csv(dir, header=0, names=Index[i])\nprint(data[:5])\n\ndays = data['data'].unique()\nprint(days)\nregion_id = data['region_id'].unique()\ntop_10 = {}\n\nfor id in region_id:\n    data_ = data[data['region_id'] == id]\n    x = range(data_.shape[0])\n    y_ = list(data_['num_new_persons'])\n    y = []\n    for i in range(len(y_)):\n        y.append(sum(y_[:i+1]))\n    if len(top_10.keys()) < 10:\n        top_10[id] = y[-1]\n    else:\n        if y[-1] > min(top_10.values()):\n            top_10[id] = y[-1]\n            m = min(top_10.keys(), key=(lambda x: top_10[x]))\n            top_10.pop(m)\n    plt.plot(x, y)\n\n\nx = range(len(days))\nplt.xticks(x, days, rotation=90)\nplt.title('A城市各区域增长数')\nplt.savefig(r'D:\\python\\venv\\2020year\\2020 IKCEST\\data visualization\\Plot\\city_A\\A城市各区域增长数')\nplt.show()\nprint('=================')\n\ntop_10_list = sorted(top_10.items(), key=lambda x: x[1])\nprint(top_10_list)\n\ntop_10_id = sorted(top_10, key=lambda x: top_10[x])\nfor id in top_10_id:\n    data_ = data[data['region_id'] == id]\n    x = range(data_.shape[0])\n    y_ = list(data_['num_new_persons'])\n    y = []\n    for i in range(len(y_)):\n        y.append(sum(y_[:i + 1]))\n    plt.plot(x, y)\n\nplt.legend(top_10_id)\nx = range(len(days))\nplt.xticks(x, days, rotation=90)\nplt.title('A城市Top10区域增长数')\nplt.savefig(r'D:\\python\\venv\\2020year\\2020 IKCEST\\data visualization\\Plot\\city_A\\A城市Top10区域增长数')\nplt.show()\nprint('=================')\n\n","sub_path":"data visualization/City A/所有区域数量变化_top10数量变化.py","file_name":"所有区域数量变化_top10数量变化.py","file_ext":"py","file_size_in_byte":2849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"334375263","text":"from sorted_nearest import makewindows\nfrom sorted_nearest import maketiles\n\n\ndef _windows(df, kwargs):\n\n    window_size = kwargs[\"window_size\"]\n\n    idxs, starts, ends = makewindows(df.index.values, df.Start.values,\n                                     df.End.values, window_size)\n\n    df = df.reindex(idxs)\n    df.loc[:, \"Start\"] = starts\n    df.loc[:, \"End\"] = ends\n\n    return df\n\n\ndef _tiles(df, kwargs):\n\n    window_size = kwargs[\"tile_size\"]\n\n    idxs, starts, ends = maketiles(df.index.values, df.Start.values,\n                                   df.End.values, window_size)\n\n    df = df.reindex(idxs)\n    df.loc[:, \"Start\"] = starts\n    df.loc[:, \"End\"] = ends\n\n    return df\n","sub_path":"pyranges/methods/windows.py","file_name":"windows.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"139982475","text":"# Copyright 2016 Google Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"Support for user-defined Aggregators.\n\nAggregators allow a pipeline to have the workers do custom aggregation\nof statistics about the data processed.  To update an aggregator's value,\ncall aggregate_to() on the context passed to a DoFn.\n\nExample:\nimport google.cloud.dataflow as df\n\nsimple_counter = df.Aggregator('example-counter')\n\nclass ExampleDoFn(df.DoFn):\n  def process(self, context):\n    context.aggregate_to(simple_counter, 1)\n    ...\n\nThe aggregators defined here show up in the UI as \"Custom counters.\"\n\nYou can also query the combined value(s) of an aggregator by calling\naggregated_value() or aggregated_values() on the result of running a\npipeline.\n\n\"\"\"\n\nfrom __future__ import absolute_import\n\nfrom google.cloud.dataflow.transforms import combiners\nfrom google.cloud.dataflow.utils.counters import Counter\n\n\nclass Aggregator(object):\n  \"\"\"A user-specified aggregator of statistics about pipeline data.\n\n  Args:\n    combine_fn: how to combine values input to the aggregation.\n      It must be one of these arithmetic functions:\n\n       - Python's built-in sum\n       - Python's built-in min\n       - Python's built-in max\n       - df.Mean()\n\n      The default is sum.\n\n    type: describes the numeric type that will be accepted as input\n      for aggregation; by default types appropriate to the combine_fn\n      are accepted.\n\n  Example uses::\n\n    import google.cloud.dataflow as df\n    simple_counter = df.Aggregator('example-counter')\n    complex_counter = df.Aggregator('other-counter', df.Mean(), float)\n  \"\"\"\n\n  def __init__(self,\n               name,\n               combine_fn=sum,\n               input_type=None):  # inferred from combine_fn\n    self.name = name\n    self.combine_fn = combine_fn\n    self.aggregation_kind = self._aggregator_counter_kind(combine_fn)\n    self.input_type = input_type\n\n  def __str__(self):\n    return '<%s>' % self._str_internal()\n\n  def __repr__(self):\n    return '<%s at %s>' % (self._str_internal(), hex(id(self)))\n\n  def _str_internal(self):\n    \"\"\"Internal helper function for both __str__ and __repr__.\"\"\"\n    def get_name(thing):\n      try:\n        return thing.__name__\n      except AttributeError:\n        return thing.__class__.__name__\n\n    combine_fn_str = get_name(self.combine_fn)\n    input_arg = '(%s)' % get_name(self.input_type) if self.input_type else ''\n    if combine_fn_str == 'sum' and not input_arg:\n      combine_call = ''\n    else:\n      combine_call = ' %s%s' % (combine_fn_str, input_arg)\n    return 'Aggregator %s%s' % (self.name, combine_call)\n\n  @staticmethod\n  def _aggregator_counter_kind(combine_fn):\n    \"\"\"Returns the counter aggregation kind for the combine_fn passed in.\n\n    Args:\n      combine_fn: The combining function used in an Aggregator.\n\n    Returns:\n      The aggregation_kind (to use in a Counter) that matches combine_fn.\n\n    Raises:\n      ValueError if the combine_fn doesn't map to any supported\n      aggregation kind.\n    \"\"\"\n    # We don't have combiner types that implement AND or OR.\n    combine_kind_map = {sum: Counter.SUM, max: Counter.MAX, min: Counter.MIN,\n                        combiners.Mean: Counter.MEAN}\n    try:\n      return combine_kind_map[combine_fn]\n    except KeyError:\n      try:\n        return combine_kind_map[combine_fn.__class__]\n      except KeyError:\n        raise ValueError(\n            'combine_fn %r (class %r) '\n            'does not map to a supported aggregation kind'\n            % (combine_fn, combine_fn.__class__))\n","sub_path":"google/cloud/dataflow/transforms/aggregator.py","file_name":"aggregator.py","file_ext":"py","file_size_in_byte":4063,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"128703516","text":"import requests\nimport json\n\ndef validate_html():\n    # get script from website\n    print(\"\\n\",\"Which website you want to validate?\")\n    website_to_validate = input()\n    response = requests.get(\"https://validator.w3.org/nu/?doc=\"+website_to_validate)\n    script=response.text\n    \n    ## debuging website script\n    #print(script)\n    print(\"There are\",script.count(\"Error</strong>\"),\"errors to fix!\");\n   \n    \nvalidate_html()\n\n# def validate_css():\n#     # get script from website\n#     print(\"\\n\",\"Which website you want to validate?\")\n#     website_to_validate = input()\n#     response = requests.get(\"https://jigsaw.w3.org/css-validator/validator?uri=\"+website_to_validate+\"&profile=css3svg&usermedium=all&warning=1&vextwarning=&lang=en\")\n#     script=response.text\n  \n#     #finding index for error\n#     try:\n#         index_number = script.index(\"Sorry\")+38\n#         print(\"Therea are\",script[index_number:index_number+2],\"errors to fix!\")\n#     except:\n#         print(\"There are no errors\")\n    \n# validate_css()\n\n    \n\n#------references\n'''\nhttps://www.youtube.com/watch?v=hpc5jyVpUpw\nhttps://validator.w3.org\nhttps://jigsaw.w3.org/css-validator\n'''\n\n#-----test websites---------\n'''\nhttps://www.simplesite.com\nhttps://www.byuh.edu\nhttps://manoa.hawaii.edu\n\nhttps://jigsaw.w3.org/css-validator/\n'''\n\n\n\n\n\n","sub_path":"autoGrader.py","file_name":"autoGrader.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"261073845","text":"import os\nfrom datetime import datetime\nfrom json import loads\n\nimport requests\nfrom dotenv import load_dotenv\n\nimport src.conditions\n\n'''\nOpenWeather API\nWeather conditions: https://openweathermap.org/weather-conditions\n'''\n\nload_dotenv()\n\nMINUTES_IN_HOUR = 60\nOPENWEATHER_API_KEY = os.getenv(\"OPENWEATHER_API_KEY\")\nWEATHER_API = 'https://api.openweathermap.org/data/2.5/weather'\n\nactivity_tags = {\n    \"clear\": [\"outdoors\"],\n    \"clouds\": [\"indoors\", \"outdoors\"],\n    \"drizzle\": [\"indoors\"],\n    \"rain\": [\"indoors\"],\n    \"snow\": [\"cozy\", \"indoors\"],\n    \"thunderstorm\": [\"cozy\", \"indoors\"]\n}\n\ncountries = {\n    \"AU\": \"Australia\",\n    \"FR\": \"France\",\n    \"IT\": \"Italy\",\n    \"JP\": \"Japan\",\n    \"US\": \"United States of America\"\n}\n\ncities = {\n    \"sydney\": {\n        \"id\": 6619279,\n        \"name\": \"City of Sydney\",\n        \"state\": \"\",\n        \"country\": \"AU\",\n        \"coord\": {\n            \"lon\": 151.208435,\n            \"lat\": -33.867779\n        }\n    },\n    \"melbourne\": {\n        \"id\": 7839805,\n        \"name\": \"Melbourne\",\n        \"state\": \"\",\n        \"country\": \"AU\",\n        \"coord\": {\n            \"lon\": 144.944214,\n            \"lat\": -37.813061\n        }\n    },\n    \"paris\": {\n        \"id\": 2968815,\n        \"name\": \"Paris\",\n        \"state\": \"\",\n        \"country\": \"FR\",\n        \"coord\": {\n            \"lon\": 2.3486,\n            \"lat\": 48.853401\n        }\n    },\n    \"tokyo\": {\n        \"id\": 1850147,\n        \"name\": \"Tokyo\",\n        \"state\": \"\",\n        \"country\": \"JP\",\n        \"coord\": {\n            \"lon\": 139.691711,\n            \"lat\": 35.689499\n        }\n    },\n    \"new york\": {\n        \"id\": 5128581,\n        \"name\": \"New York City\",\n        \"state\": \"NY\",\n        \"country\": \"US\",\n        \"coord\": {\n            \"lon\": -74.005966,\n            \"lat\": 40.714272\n        }\n    },\n    \"rome\": {\n        \"id\": 3169070,\n        \"name\": \"Rome\",\n        \"state\": \"\",\n        \"country\": \"IT\",\n        \"coord\": {\n            \"lon\": 12.4839,\n            \"lat\": 41.894741\n        }\n    }\n}\n\n\ndef get_weather_by_city(city):\n    country = countries[cities[city][\"country\"]]\n\n    params = {\n        \"id\": cities[city][\"id\"],\n        \"appid\": OPENWEATHER_API_KEY,\n        \"units\": \"metric\"\n    }\n\n    # call OpenWeather API\n    response = loads(requests.get(WEATHER_API, params).text)\n\n    # calculate the machine's UTC offset\n    local_now = datetime.now()\n    utcnow = datetime.utcnow()\n    utc_offset = (local_now - utcnow).total_seconds()\n\n    # extract information from response\n    weather_icons = list(map(lambda wc: wc[\"icon\"], response[\"weather\"]))\n    weather_conditions = list(map(lambda wc: wc[\"main\"], response[\"weather\"]))\n    weather_descriptions = list(map(lambda wc: wc[\"description\"], response[\"weather\"]))\n    temp = response[\"main\"][\"feels_like\"]\n    humidity = response[\"main\"][\"humidity\"]\n    # visibility = response[\"visibility\"]\n    local_sunrise = response[\"sys\"][\"sunrise\"]\n    local_sunset = response[\"sys\"][\"sunset\"]\n\n    # convert into the city's time\n    sunrise = local_sunrise - utc_offset + response[\"timezone\"]\n    sunset = local_sunset - utc_offset + response[\"timezone\"]\n    now = utcnow.timestamp() + response[\"timezone\"]\n\n    # determine if sunrise/sunset is occurring within the next hour\n    is_sunrise_soon = sunrise > now and sunrise - now <= MINUTES_IN_HOUR * MINUTES_IN_HOUR\n    is_sunset_soon = sunset > now and sunset - now <= MINUTES_IN_HOUR * MINUTES_IN_HOUR\n\n    # TODO(michelle) - return a json object once data to be used is confirmed\n    print(f\"Location: {city.capitalize()}, {country.capitalize()}\")\n    print(f\"Time now: {datetime.fromtimestamp(now)}\")\n    print(f\"Weather condition: {weather_conditions[0]} - {weather_descriptions[0]}\")\n    print(f\"Weather icon: http://openweathermap.org/img/wn/{weather_icons[0]}@2x.png\")\n    print(f\"Feels like: {temp} degrees celcius\")\n    print(f\"Humidity: {humidity}%\")\n    # print(f\"Visibility: {visibility}m\")\n    print(f\"Activity tags: {activity_tags[weather_conditions[0].lower()]}\")\n    print(f\"Sunrise: {datetime.fromtimestamp(sunrise)}\")\n    print(f\"Sunset: {datetime.fromtimestamp(sunset)}\")\n    if is_sunrise_soon:\n        print(f\"Sunrise is within an hour from now\")\n    if is_sunset_soon:\n        print(f\"Sunset is within an hour from now\")\n\n    return src.conditions.Conditions(\n        f'{weather_conditions[0]} - {weather_descriptions[0]}',\n        datetime.fromtimestamp(now),\n        temp,\n        humidity,\n        # visibility,\n        activity_tags[weather_conditions[0].lower()],\n        datetime.fromtimestamp(sunrise),\n        datetime.fromtimestamp(sunset),\n        float(cities['sydney']['coord']['lon']),\n        float(cities['sydney']['coord']['lat'])\n    )\n","sub_path":"SourceCode_and_Documentation/backend/src/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":4681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"613385850","text":"import psutil\nimport time\nfrom datetime import datetime\nimport os\nimport sys\nimport csv\n\nlog_file_path = \"data/log/log.csv\"\n\ndef get_info():\n    battery = psutil.sensors_battery()\n    plugged = battery.power_plugged\n    percent = battery.percent\n    return plugged, percent\n\ndef notify():\n    # notification bar\n    command = \"osascript -e 'display notification \\\"Battery is now full charged. Unplugg the charger\\\" with title \\\"Battery Manager\\\"'\"\n    os.system(command)\n    # sound notify\n    command = \"afplay data/sound/sound.mp3\"\n    os.system(command)\n\n\ndef logger(msg,percent,plugged,notify): \n    datetime_info = datetime.now()\n    script_name = sys.argv[0]\n    log_data = [datetime_info,script_name,percent, plugged,notify]\n    with open(log_file_path, \"a\") as f:\n        writer = csv.writer(f)\n        writer.writerow(log_data)\n    print('[{}] : runing {} : {} : {}%'.format(str(datetime.now()), sys.argv[0],msg,percent))\n\ndef main():\n    while True:\n        time.sleep(10)\n        plugged, percent = get_info() \n        if percent == 100 and plugged:\n            notify()\n            logger('sent notify',percent,plugged,True)\n        else:\n            logger('found no problem',percent,plugged,False)\n\nif __name__ == '__main__':\n    main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"467724891","text":"import logging\nfrom dl_data_validation_toolset.framework import base_test\n\n\nclass WiresTests(base_test.BaseTest):\n    logger = logging.getLogger('data_tests.wires')\n\n    def test_nonempty(self):\n        data = self._file['image/wires']\n        valid = len(data) > 0\n        return {'Is Non Empty': valid}, valid\n\n    def test_digits_exist(self):\n        wiers = self._file['image/wires']\n        valid = int(wiers is not None)\n        valid += int(len(wiers) > 0)\n        return {'N Wires': len(wiers)}, valid\n","sub_path":"dl_data_validation_toolset/data_tests/wires.py","file_name":"wires.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"390415032","text":"from triage_ml.models.radius_variance import RadiusVariance\nfrom triage_ml.data.dataset import DataSet, TimeInterval\n\nimport pytest\nfrom datetime import datetime\nimport numpy as np\nfrom tensorflow.keras.optimizers import Adam\n\n\n@pytest.fixture(scope=\"module\")\ndef test_dataset():\n    data = []\n    with open('tests/test_data.txt') as f:\n        for line in f.readlines():\n            line = line.rstrip().split(',')\n            data.append((\n                int(line[0]),\n                int(line[1]),\n                datetime.strptime(line[2], '%Y-%m-%d'),\n            ))\n\n    return DataSet(data)\n\n\ndef test_get_model():\n    model = RadiusVariance()\n    model.get_model().summary()\n\n\ndef test_train_model(test_dataset):\n    \"\"\"SRS: MOD-2\"\"\"\n    model = RadiusVariance(seq_size=2, radius=1)\n    ml_dataset = model.create_ml_dataset(test_dataset)\n    weights = model.get_model().get_weights()\n\n    model.get_model().compile(\n        optimizer=Adam(lr=0.001),\n        loss='mse'\n    )\n    model.get_model().fit(x=ml_dataset.inputs, y=ml_dataset.outputs, epochs=1)\n\n    new_weights = model.get_model().get_weights()\n\n    assert not np.all([np.array_equal(w, new_w) for (w, new_w) in zip(weights, new_weights)])\n\n\ndef test_prediction(test_dataset):\n    \"\"\"SRS: MOD-7\"\"\"\n    model = RadiusVariance(seq_size=2, radius=1)\n    ml_dataset = model.create_ml_dataset(test_dataset)\n    model.get_model().compile(\n        optimizer=Adam(lr=0.001),\n        loss='mse'\n    )\n\n    y_hat = model.get_model().predict([x[np.newaxis, 0] for x in ml_dataset.inputs])\n\n    assert y_hat.shape == (1, 2)\n\n\ndef test_prediction_batch(test_dataset):\n    model = RadiusVariance(seq_size=2, radius=1)\n    ml_dataset = model.create_ml_dataset(test_dataset)\n    model.get_model().compile(\n        optimizer=Adam(lr=0.001),\n        loss='mse'\n    )\n\n    y_hat = model.get_model().predict([x[0:2] for x in ml_dataset.inputs])\n\n    assert y_hat.shape == (2, 2)\n\n\ndef test_create_ml_dataset(test_dataset):\n    model = RadiusVariance(seq_size=2, radius=3)\n    ml_dataset = model.create_ml_dataset(test_dataset)\n\n    assert np.array_equal(ml_dataset.inputs[0], np.array([[[1], [1]], [[1], [0]], [[0], [4]]]))\n\n\ndef test_create_ml_dataset_empty():\n    model = RadiusVariance(seq_size=3, radius=2)\n    dataset = DataSet([])\n    ml_dataset = model.create_ml_dataset(dataset)\n\n    assert np.array_equal(ml_dataset.inputs[0], np.array([]))\n    assert np.array_equal(ml_dataset.inputs[1], np.empty((0, 43)))\n    assert np.array_equal(ml_dataset.outputs[0], np.empty((0, 2)))\n\n\ndef test_create_ml_dataset_correct_length(test_dataset):\n    model = RadiusVariance(seq_size=1, radius=1)\n    dataset = DataSet(test_dataset)\n    ml_dataset = model.create_ml_dataset(dataset)\n\n    assert ml_dataset.inputs[0].shape[0] == 8\n\n\ndef test_create_ml_dataset_radius_affects_length(test_dataset):\n    model = RadiusVariance(seq_size=3, radius=3)\n    dataset = DataSet(test_dataset)\n    ml_dataset = model.create_ml_dataset(dataset)\n\n    assert ml_dataset.inputs[0].shape[0] == 2\n\n\ndef test_create_ml_dataset_week_interval(test_dataset):\n    model = RadiusVariance(seq_size=1, radius=0, time_interval=TimeInterval.WEEK)\n    ml_dataset = model.create_ml_dataset(test_dataset)\n\n    assert ml_dataset.inputs[0] == np.array([[4.]])\n","sub_path":"ml-training/tests/models/test_radius_variance.py","file_name":"test_radius_variance.py","file_ext":"py","file_size_in_byte":3270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"633149554","text":"# -*- coding: utf-8 -*-\n\nclass treeNode(object):\n    def __init__(self,val = None,left = None,right = None):\n        self.val = val\n        self.left = left\n        self.right = right\n\nclass solution(object):\n    def isSame(self,root):\n        if root == None:\n            return True\n        return self.same(root.left,root.right)\n\n    def same(self,lNode,rNode):\n        if lNode == None and rNode == None:\n            return True\n        if lNode and rNode:\n            return lNode.val == rNode.val and self.same(lNode.left,rNode.left) and self.same(lNode.right,rNode.right)\n\nif __name__ == '__main__':\n    root = treeNode('A',treeNode('B'),treeNode('B'))\n\n    s = solution()\n    print(s.isSame(root))","sub_path":"BinaryTree/binaryTree-Same.py","file_name":"binaryTree-Same.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"481845007","text":"import json\nimport requests\nfrom collections import Counter\n\ndef liker_ids_tornado(response, like_ids_list = None):\n\tlike_ids_list = [] if like_ids_list is None else like_ids_list\n\tdata_list = response['data']\n\n\tfor post_message_story in data_list:\n\t\tif 'likes' in post_message_story:\n\t\t\tfor liker in post_message_story['likes']['data']:\n\t\t\t\tlike_ids_list.append((liker['id'], liker['name']))\n\tif 'paging' in response and 'next' in response['paging']:\n\t\tr = requests.get(response['paging']['next']).json()\n\t\tliker_ids_tornado(r, like_ids_list)\n\treturn like_ids_list\n\ndef get_most_likers(like_ids_list):\n\tid_results_dict = Counter(like_ids_list)\n\treturn id_results_dict\n\ndef string_to_dict(json_string):\n    return json.loads(json_string)\n\ndef tornado_all_posts_dict(response_dict, master_posts = None ):\n\tmaster_posts = {'data':[]} if master_posts is None else master_posts\n\tposts = response_dict['data']\n\tmaster_posts['data'] = master_posts['data'] + posts\n\tif 'paging' in response_dict and 'next' in response_dict['paging']:\n\t\tr = requests.get(response_dict['paging']['next']).json()\n\t\ttornado_all_posts_dict(r, master_posts)\n\treturn master_posts\n\ndef remove_dicts_from_list_based_on_key(response_dict, key):\n\tthe_list = response_dict['data']\n\treturn [dicti for dicti in the_list if key in dicti]\n\ndef sort_posts_dict_by_likes(response_dict):\n\tlist_of_user_post_objects = response_dict['data']\n\tlist_of_user_post_objects = sorted(list_of_user_post_objects, key=lambda k: -k['likes']['summary']['total_count']) \n\treturn list_of_user_post_objects\n\ndef get_all_posts_dict(response):\n\treturn tornado_all_posts_dict(string_to_dict(response))","sub_path":"talentur/fun/work_fun.py","file_name":"work_fun.py","file_ext":"py","file_size_in_byte":1638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"312074548","text":"# coding:utf-8\nimport logging\nimport unittest\n\nfrom app.job.send_mail import SendMail\nfrom test.unit_test_base import UnitTestBase\n\n\"\"\"\nメール送信を行うサンプルアプリケーションのテストです\n\"\"\"\n__author__ = \"t.ebinuma\"\n__version__ = \"1.0\"\n__date__ = \"28 April 2018\"\n\n\nclass SendMailTest(UnitTestBase):\n\n    def test_send_mail_001(self):\n        \"\"\"\n        メール送信を検証するテストケースです\n        \"\"\"\n        # ログファイルをクリアする\n        self.cleanup_log_file()\n        try:\n            # メッセージ出力を行う\n            SendMail().send_mail()\n        except Exception as e:\n            logging.exception(e)\n        # メッセージコード検索してメール送信が行われたかをチェックする\n        self.assertTrue(self.check_text_in_log_file(\"I905\"))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"test/job/test_send_mail.py","file_name":"test_send_mail.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"214745521","text":"# Part Object\nimport numpy as np\nimport pandas as pd\nimport Queries as q\n\n\ndef getJobType(jobs):\n    refTypes = []\n    for job in jobs:\n        refTypes.append(job[0])\n\n    refTypes = np.array(refTypes)\n    unique = np.unique(refTypes)\n    if refTypes[-1] == 31:\n        type = \"P-PROD\"\n\n\nclass Part(object):\n    def __init__(self, parent, number, quantity, amount=None, level=None, bio=None):\n        self.parent = parent\n        self.number = number\n        self.bio = bio\n        self.quantity = quantity\n        self.amount = amount\n        self.name = ''\n\n        self.part_list = dict()\n\n        self.job = []\n        self.level = level\n        self.id = ''\n        self.job_quant = 0\n\n        self.pct = 0.0\n        self.isPctCalc = False\n        self.cur_opr = 'PROD'\n        self.cur_pct = 1.0\n        self.isCur = 0\n        self.type = \"\"\n\n        self.manipulated = False\n\n        self.isExpanded = False\n        self.hasChildren = False\n\n        self.delivery = ''\n\n    def get_tree(self, tree, conn, dbType):\n        self.get_pct(conn, dbType)\n        tree.append((self.level, self.number, self.pct, self.cur_pct, self.cur_opr, self.type, self.id, self.isExpanded, self.hasChildren, self))\n        subKeys = list(self.part_list.keys())\n\n        if self.isExpanded:\n            for part in subKeys:\n                self.part_list[part].get_tree(tree, conn, dbType)\n\n        return tree\n\n    def get_excel_tree(self, tree, conn, dbType):\n        self.get_pct(conn, dbType)\n        tree.append(self)\n        subKeys = list(self.part_list.keys())\n\n        for part in subKeys:\n            self.part_list[part].get_excel_tree(tree, conn, dbType)\n\n        return tree\n\n    def check_Software(self, name):\n        if name[:3] == \"95-\":\n            return False\n        else:\n            return True\n\n    def calc_pct(self, conn, dbType):\n        isProd = False\n        isOnHand = False\n        # conn = odbc.connect('DSN=Dynamics Live;Trusted_Connection=yes;')\n        # dbType = \"Live\"\n        eco = pd.read_sql(q.get_EcoResProductTranslation_new(dbType, self.number), conn)\n        self.name = eco[\"NAME\"].iloc[0]\n\n        for job in self.job:\n            if job[0] == 10:\n                self.type = \"TOP\"\n                self.id = job[3]\n            if job[0] == 31:\n                if self.type == \"TOP\":\n                    pass\n                else:\n                    self.type = \"SUB\"\n                    self.id = job[3]\n            if job[0] == 9:\n                isProd = True\n                self.id = job[3]\n                self.type = \"PROD\"\n                break\n            if job[0] == 1:\n                isOnHand = True\n                self.job_quant = job[1]\n                self.job_req = job[2]\n                self.type = \"OH\"\n                self.id = job[3]\n                break\n            if job[0] == 33:\n                self.type = \"P-PUR\"\n                self.id = job[3]\n                # conn = odbc.connect('DSN=Dynamics Live;Trusted_Connection=yes;')\n                pur_table = pd.read_sql(q.get_deliveryDateTable_new(dbType, self.id), conn)\n                self.delivery = str(pur_table[\"REQDATE\"].iloc[0])[:11]\n                break\n            if job[0] == 8:\n                self.type = \"PUR\"\n                self.id = job[3]\n                break\n\n        if isProd:\n            # conn = odbc.connect('DSN=Dynamics Live;Trusted_Connection=yes;')\n            # dbType = \"Live\"\n            id = self.id\n            pct = pd.read_sql(q.get_ProdCalcTrans_new(dbType, id), conn)\n            pr = pd.read_sql(q.get_ProdRoute_new(dbType, id), conn)\n\n            pct = pct.sort_values(by=[\"OPRNUM\"])\n            pr = pr.sort_values(by=[\"OPRNUM\"])\n\n            print(pct)\n            print(pr)\n\n            # Finish grabbing the data as oultine in notes\n            operations = pct.OPRNUM.unique()\n            weight = len(operations)\n\n            master = pct.loc[pct['BOM'] == 1]\n            masterReal = abs(float(master[\"REALCONSUMP\"].iloc[0]))\n            masterEst = abs(float(master[\"CONSUMPVARIABLE\"].iloc[0]))\n\n            if masterReal/masterEst != 1:\n                pts = 0\n                for opr in operations:\n                    oprID = \"TOT\"\n                    if opr != 0:\n                        try:\n                            isFinished = pr.loc[pr['OPRNUM'] == opr][\"OPRFINISHED\"].iloc[0]\n                            oprID = pr.loc[pr['OPRNUM'] == opr][\"OPRID\"].iloc[0][:3]\n                        except Exception:\n                            # MessagePopup(\"WARNING: Operation Number {} faulty entry into DB, estimate may be off.\".format(opr), button_bg=\"#33B86C\", button_fg=\"white\", text_fg=\"#7E8190\")\n                            isFinished = 0\n                            oprID = \"UNK\"\n                    else:\n                        isFinished = 0\n                    if isFinished:\n                        pts += 1\n                    else:\n                        # Check prodcalctrans for sub percentage\n                        rowcount = 0\n                        oprPts = 0\n                        pct_opr = pct.loc[pct[\"OPRNUM\"] == opr]\n                        for index, row in pct_opr.iterrows():\n                            rowcount += 1\n                            costGroup = row[\"COSTGROUPID\"]\n                            if (costGroup == \"TOT\") and (opr != 0):\n                                try:\n                                    costGroupName = row[\"RESOURCE_\"]\n                                    #print(\"PART LIST\", self.part_list)\n                                    if self.check_Software(costGroupName):\n                                        oprPts += self.part_list[costGroupName].get_pct(conn, dbType)\n                                except Exception:\n                                    print(\"Part is complete and removed from list...\")\n                                    real = abs(float(row[\"REALCONSUMP\"]))\n                                    est = abs(float(row[\"CONSUMPVARIABLE\"]))\n                                    #print(\"Sub Points:\", real, est)\n                                    if est == 0:\n                                        oprPts += 0.5\n                                    else:\n                                        oprPts += (real / est)\n\n                            else:\n                                real = abs(float(row[\"REALCONSUMP\"]))\n                                est = abs(float(row[\"CONSUMPVARIABLE\"]))\n                                #print(\"Sub Points:\", real, est)\n                                if est == 0:\n                                    oprPts += 0.5\n                                else:\n                                    oprPts += (real / est)\n                        pts += (oprPts / rowcount)\n                        if self.isCur == 1:\n                            self.cur_opr = oprID\n                            self.cur_pct = (oprPts / rowcount)\n                            self.isCur = 2\n                        elif self.isCur == 0:\n                            self.isCur = 1\n                        else:\n                            pass\n                #print(\"Main pts:\", pts, weight)\n                self.pct = pts / weight\n            else:\n                self.pct = 1.0\n\n        elif isOnHand:\n            if abs(self.job_quant / self.quantity) >= 1:\n                self.pct = 1.0\n            else:\n                self.pct = abs(self.job_quant / self.quantity)\n        else:\n            weight = len(list(self.part_list.keys()))\n            keys = list(self.part_list.keys())\n            pts = 0\n            for key in keys:\n                pts += self.part_list[key].get_pct(conn, dbType)\n            if weight != 0:\n                self.pct = pts / weight\n            else:\n                self.pct = 0\n\n        self.isPctCalc = True\n        return self.pct\n\n    def get_pct(self, conn=None, dbType=None):\n        if self.isPctCalc:\n            return self.pct\n        else:\n            return self.calc_pct(conn, dbType)\n","sub_path":"Part.py","file_name":"Part.py","file_ext":"py","file_size_in_byte":7996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"34027407","text":"from typing import List, Dict, Tuple, Callable, Any, Optional\n\nfrom drepr import DRepr, outputs\nfrom drepr.engine import complete_description\n\n\ndef test_complete_description(d_s04: DRepr):\n    model2subjects = {\n        \"d_s04\": {\n            (\"mint:Variable:1\", \"dnode:Rainf_f_tavg\"),\n            (\"mint:Variable:2\", \"dnode:Albedo_inst\")\n        }\n    }\n\n    for ds_model in [d_s04]:\n        plan = complete_description(ds_model)\n\n        # check all subjects are inferred correctly (which shows that all inferred alignments are correct)\n        subjects = model2subjects[ds_model.__name__]\n        assert len(subjects) > 0\n\n        for edge in plan.sm.edges.values():\n            pair = (edge.source_id, edge.target_id)\n            if pair in subjects:\n                assert edge.is_subject is True\n                subjects.remove(pair)\n        assert len(subjects) == 0\n","sub_path":"python/tests/engine/test_complete_description.py","file_name":"test_complete_description.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"545569434","text":"from selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import (NoSuchElementException,\n\t\tStaleElementReferenceException, ElementNotVisibleException)\nimport datePicker\nimport time\nfrom selenium.webdriver.support.wait import WebDriverWait as WDW\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\n\n\nclass FishTestForm():\n\n\tdef __init__(self, driver):\n\t\tself.driver = driver\n\n\tdef load(self):\n\t\tWDW(self.driver, 20).until_not(EC.presence_of_element_located((By.CLASS_NAME, 'overlay')))\n\t\t\n\t\tself.form = self.driver.find_elements_by_tag_name('form')[1]\n\t\tbuttons = self.form.find_elements_by_tag_name('button')\n\t\tself.rows = self.form.find_elements_by_class_name('form-group')\n\n\n\t\t# Make sure datepicker row has right stuff loaded\n\t\tif not self.rows:\n\t\t\treturn False\n\t\tdatepicker = self.rows[0].find_element_by_class_name('Select--single')\n\n\t\tself.comment_textarea = self.rows[0].find_element_by_class_name('textarea')\n\t\traw_input('comment area working')\n\n\t\tself.add_1q21_checkbox = self.form.find_element_by_id('fishtesting_Addition_1q21')\n\t\traw_input('gene additions working')\n\n\t\tself.deletion_1p_checkbox = self.form.find_element_by_id('fishtesting_Deletion_1p')\n\t\tself.deletion_17p_checkbox = self.form.find_element_by_id('fishtesting_Deletion_17p')\n\t\tself.deletion_13q_checkbox = self.form.find_element_by_id('fishtesting_Deletion_13q')\n\t\tself.deletion_16q_checkbox = self.form.find_element_by_id('fishtesting_Deletion_16q')\n\t\traw_input('gene deletions working')\n\n\t\tself.trans_FGFR3_checkbox =  self.form.find_element_by_id('fishtesting_Translocation_FGFR3')\n\t\tself.trans_CCND3_checkbox = self.form.find_element_by_id('fishtesting_Translocation_CCND3')\n\t\tself.trans_CCND1_checkbox = self.form.find_element_by_id('fishtesting_Translocation_CCND1')\n\t\tself.trans_cMAF_checkbox = self.form.find_element_by_id('fishtesting_Translocation_c-MAF')\n\t\tself.trans_MAFB_checkbox = self.form.find_element_by_id('fishtesting_Translocation_MAFB')\n\t\tself.trans_ETV6_checkbox = self.form.find_element_by_id('fishtesting_Translocation_ETV6')\n\t\traw_input('gene translations working')\n\n\t\tself.tri_3_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_3')\n\t\tself.tri_5_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_5')\n\t\tself.tri_7_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_7')\n\t\tself.tri_9_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_9')\n\t\tself.tri_11_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_11')\n\t\tself.tri_15_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_15')\n\t\tself.tri_17_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_17')\n\t\tself.tri_19_checkbox = self.form.find_element_by_id('fishtesting_Trisomies_19')\n\n\t\tself.tetra_3_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_3')\n\t\tself.tetra_5_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_5')\n\t\tself.tetra_7_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_7')\n\t\tself.tetra_9_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_9')\n\t\tself.tetra_11_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_11')\n\t\tself.tetra_15_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_15')\n\t\tself.tetra_17_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_17')\n\t\tself.tetra_19_checkbox = self.form.find_element_by_id('fishtesting_Tetrasomies_19')\n\n\t\tself.save_button = buttons[1]\n\t\tself.cancel_button = buttons[0]\n\n\t\treturn True\n\n\tdef submit(self, fishInfo, action='save'):\n\t\tif fishInfo:\n\t\t\tif fishInfo['test_fish_date'] is not None:\n\t\t\t\tpicker = datePicker.DatePicker(self.driver, self.rows[0])\n\t\t\t# self.dateDiagnosis_input.click()\n\t\t\t# picker.set_date(formInfo['diagnosis_date'], self.dateDiagnosis_input)\n\t\t\t\tdateSet = False\n\t\t\t\twhile not dateSet:\n\t\t\t\t\ttry:\n\t\t\t\t\t\t# self.dateDiagnosis_input.click()\n\n\t\t\t\t\t\tpicker.set_date(fishInfo['test_fish_date'])\n\t\t\t\t\t\tdateSet = True\n\t\t\t\t\texcept (ElementNotVisibleException, StaleElementReferenceException) as e:\n\t\t\t\t\t\tprint('Failed to set date. Page probably reloaded')\n\t\t\t\t\t\ttime.sleep(.4)\n\n\t\t\tif fishInfo['gene_additions']['1q21'] != self.add_1q21_checkbox.is_selected():\n\t\t\t\tself.add_1q21_checkbox.click()\n\n\t\t\tif fishInfo['gene_deletions']['deletion_1p'] != self.deletion_1p_checkbox.is_selected():\n\t\t\t\tself.deletion_1p_checkbox.click()\n\t\t\tif fishInfo['gene_deletions']['deletion_17p'] != self.deletion_17p_checkbox.is_selected():\n\t\t\t\tself.deletion_17p_checkbox.click()\n\t\t\tif fishInfo['gene_deletions']['deletion_13q'] != self.deletion_13q_checkbox.is_selected():\n\t\t\t\tself.deletion_13q_checkbox.click()\n\t\t\tif fishInfo['gene_deletions']['deletion_16q'] != self.deletion_16q_checkbox.is_selected():\n\t\t\t\tself.deletion_16q_checkbox.click()\n\n\t\t\tif fishInfo['gene_translocations']['trans_FGFR3'] != self.trans_FGFR3_checkbox.is_selected():\n\t\t\t\tself.trans_FGFR3_checkbox.click()\n\t\t\tif fishInfo['gene_translocations']['trans_CCND3'] != self.trans_CCND3_checkbox.is_selected():\n\t\t\t\tself.trans_CCND3_checkbox.click()\n\t\t\tif fishInfo['gene_translocations']['trans_CCND1'] != self.trans_CCND1_checkbox.is_selected():\n\t\t\t\tself.trans_CCND1_checkbox.click()\n\t\t\tif fishInfo['gene_translocations']['trans_cMAF'] != self.trans_cMAF_checkbox.is_selected():\n\t\t\t\tself.trans_cMAF_checkbox.click()\n\t\t\tif fishInfo['gene_translocations']['trans_MAFB'] != self.trans_MAFB_checkbox.is_selected():\n\t\t\t\tself.trans_MAFB_checkbox.click()\n\t\t\tif fishInfo['gene_translocations']['trans_ETV6'] != self.trans_ETV6_checkbox.is_selected():\n\t\t\t\tself.trans_ETV6_checkbox.click()\n\n\t\t\tif fishInfo['trisomies']['tri_3'] != self.tri_3_checkbox.is_selected():\n\t\t\t\tself.tri_3_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_5'] != self.tri_5_checkbox.is_selected():\n\t\t\t\tself.tri_5_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_7'] != self.tri_7_checkbox.is_selected():\n\t\t\t\tself.tri_7_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_9'] != self.tri_9_checkbox.is_selected():\n\t\t\t\tself.tri_9_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_11'] != self.tri_11_checkbox.is_selected():\n\t\t\t\tself.tri_11_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_15'] != self.tri_15_checkbox.is_selected():\n\t\t\t\tself.tri_15_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_17'] != self.tri_17_checkbox.is_selected():\n\t\t\t\tself.tri_17_checkbox.click()\n\t\t\tif fishInfo['trisomies']['tri_17'] != self.tri_17_checkbox.is_selected():\n\t\t\t\tself.tri_17_checkbox.click()\n\n\t\t\tif fishInfo['tetrasomies']['tetra_3'] != self.tetra_3_checkbox.is_selected():\n\t\t\t\tself.tetra_3_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_5'] != self.tetra_5_checkbox.is_selected():\n\t\t\t\tself.tetra_5_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_7'] != self.tetra_7_checkbox.is_selected():\n\t\t\t\tself.tetra_7_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_9'] != self.tetra_9_checkbox.is_selected():\n\t\t\t\tself.tetra_9_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_11'] != self.tetra_11_checkbox.is_selected():\n\t\t\t\tself.tetra_11_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_15'] != self.tetra_15_checkbox.is_selected():\n\t\t\t\tself.tetra_15_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_17'] != self.tetra_17_checkbox.is_selected():\n\t\t\t\tself.tetra_17_checkbox.click()\n\t\t\tif fishInfo['tetrasomies']['tetra_17'] != self.tetra_17_checkbox.is_selected():\n\t\t\t\tself.tetra_17_checkbox.click()\n\n\t\t\tif action == 'save':\n\t\t\t\tself.save_button.click()\n\t\t\tif action == 'cancel':\n\t\t\t\tself.cancel_button.click()\n\t\t\tif action == 'close':\n\t\t\t\tself.close_button.click()\n\t\t\treturn True\n\t\treturn False\n\n","sub_path":"Components/fishTestForm.py","file_name":"fishTestForm.py","file_ext":"py","file_size_in_byte":7615,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"47826053","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec  6 13:37:39 2018\n\n@author: Blaž\n\"\"\"\n\nrawDataInputArray = []\n#procesedInputDataArray = []\n\n''' Reading Input File\n'''\ninputFile = open (\"Input Txts/Day06.txt\", \"r\")\n\nfor line in inputFile:\n    rawDataInputArray.append(line.strip())\n\ninputFile.close()\n\n''' Functions\n'''\ndef organiseData(data):\n    procesedInputDataArray = []\n    \n    for line in data:\n        coordinatesOnly = line.split(\",\")\n        x = int(coordinatesOnly[0].strip())\n        y = int(coordinatesOnly[1].strip())\n        xyCoor = []\n        xyCoor.append(x)\n        xyCoor.append(y)\n        procesedInputDataArray.append(xyCoor)\n    \n    procesedInputDataArray.sort()\n    \n    return (procesedInputDataArray)\n\ndef create2DGrid(procData):\n    xMax = yMax = 0\n    \n    for coor in procData:\n        if (coor[0] > xMax):\n            xMax = coor[0]\n        if (coor[1] > yMax):\n            yMax = coor[1]\n    \n    gridValues = [[\"0\" for y in range (yMax + 1)] for x in range (xMax + 1)]\n    \n    coordinatesDict = {}\n    for i in range(1, len(procData) + 1):\n        coordinatesDict[i] = procData[i - 1]\n        \n    for y in range (yMax):\n        for x in range (xMax):\n            minDistance = xMax + yMax\n            minDistStr = \"\"\n            distance = 0\n            for i in range(1, len(procData) + 1):\n                \n                xDist = abs(coordinatesDict[i][0] - x)\n                yDist = abs(coordinatesDict[i][1] - y)\n                distance = xDist + yDist\n                if (minDistance > distance):\n                    minDistance = distance\n                    minDistStr = str(i)\n                elif (minDistance == distance):\n                    minDistStr += \", \" + str(i)\n            gridValues[x][y] = minDistStr\n    \n    return (gridValues)\n\n\n\ndef createBorderValuesList(gridValues):\n    borderValues = []\n    \n    xSize = len(gridValues)\n    ySize = len(gridValues[0]) \n    \n    for x in range (xSize):\n        value = gridValues[x][0]\n        if (value not in borderValues):\n            borderValues.append(value)\n            \n        value = gridValues[x][ySize - 1]\n        if (value not in borderValues):\n            borderValues.append(value)\n       \n    for y in range (ySize):\n        value = gridValues[0][y]\n        if (value not in borderValues):\n            borderValues.append(value)\n            \n        value = gridValues[xSize - 1][y]\n        if (value not in borderValues):\n            borderValues.append(value)\n\n    return (borderValues)\n\n\ndef is_intiger(s):\n    try:\n        int(s)\n        return True\n    except ValueError:\n        return False\n    \ndef countAreasInRange(procData, _range):\n     \n    xMax = yMax = 0\n     \n    for coor in procData:\n        if (coor[0] > xMax):\n            xMax = coor[0]\n        if (coor[1] > yMax):\n            yMax = coor[1]\n        \n    totalDistance = 0\n    regionsCount = 0\n    \n    coordinatesDict = {}\n    for i in range(1, len(procData) + 1):\n        coordinatesDict[i] = procData[i - 1]\n        \n    for x in range (xMax):\n        for y in range (yMax):\n            totalDistance = 0\n            for i in range(1, len(procData) + 1):\n                xDist = abs(coordinatesDict[i][0] - x)\n                yDist = abs(coordinatesDict[i][1] - y)\n                totalDistance += xDist + yDist\n                if (totalDistance > _range):\n                    break\n                \n            if (totalDistance < _range):\n                regionsCount += 1\n        \n    return (regionsCount)\n    \n''' Task 01\n'''\ndef task1(rawData):\n    procData = organiseData(rawData)\n    gridValues = create2DGrid(procData)\n    borderValues = createBorderValuesList(gridValues)\n    \n    valueCountDict = {}\n    maxValueCount = 0\n    valueWithMaxCount = \"\"\n    \n    for lineArray in gridValues:\n        for value in lineArray:\n            if (is_intiger(value) and value not in borderValues):\n                if (value not in valueCountDict):\n                    valueCountDict[value] = 1\n                else:\n                    valueCountDict[value] += 1\n    \n    for dictIndex in valueCountDict:\n        valueCount = valueCountDict[dictIndex]\n        if (valueCount > maxValueCount):\n            maxValueCount = valueCount\n            valueWithMaxCount = dictIndex\n    \n    result = maxValueCount\n#    print (valueWithMaxCount)\n    return result\n    \n    \n''' Task 02\n'''\ndef task2 (rawData):\n    procData = organiseData(rawData)\n    _range = 10000\n    result = countAreasInRange(procData, _range)\n    \n    return result\n    \n\n\nprint(\"Result of taks 01 is : \" + str(task1(rawDataInputArray)))\nprint(\"Result of taks 02 is : \" + str(task2(rawDataInputArray)))\n    ","sub_path":"Src/Day06.py","file_name":"Day06.py","file_ext":"py","file_size_in_byte":4659,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"486939177","text":"'''\n@author: DNP Enterprises Inc.\n'''\nfrom datetime import datetime\nfrom time import sleep\nimport re\n#from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom Quotes.Excel_utils2 import Excel_utils2\nfrom Cars.CreateDealerSheet2 import CreateDealerSheet\nfrom Cars.browser_start import browser_start\n\nif __name__ == '__main__':\n    file_in = 'C:/Users/Home/Desktop/Cars/CarData.xlsx'\n    data_in = Excel_utils2(file_in, 'Ford', 'in')\n    file_out = data_in.sht.cell(6,7).value\n    dealer = data_in.sht.cell(6,1).value\n    base_url = data_in.sht.cell(6,2).value\n    dealer_id = (data_in.sht.cell(6,3).value).split() # convert to a list for use later\n    date_time = datetime.now().strftime('%Y %B %d %I %M %p') # get the date and time\n    data_out = Excel_utils2(' ', date_time, 'out') # set the spreadsheet tab to the dealer name\n\n    headless = True\n    #headless = False\n    driver = browser_start(base_url, headless) # run browser in headless mode\n\n    wait = WebDriverWait(driver, 90)\n    wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, '.page-main-h1'))) # wait for USED VEHICLES to appear\n    print (driver.title)\n    num_cars = int(driver.find_elements_by_css_selector('.total-value')[1].text)\n    #if headless: # believe it or not, there's a difference in how # of cars is found headless vs non-headless\n    #    num_cars = int(driver.find_elements_by_css_selector('.total-value')[0].text)\n    #else:\n    #    num_cars = int(driver.find_elements_by_css_selector('.total-value')[1].text)\n    \n    print (\"Number of cars found on site: \" , num_cars)\n    \n    count = 0\n    zero = 0\n    car_info = []\n  \n    pages_remaining = True\n    while pages_remaining:\n        car_desc = driver.find_elements_by_css_selector('.vehicle-title') # year, make, model\n        car_trim = driver.find_elements_by_css_selector('.vehicle-trim') # model trim/details\n        prices = driver.find_elements_by_css_selector('.sale-price') # prices\n        stock = driver.find_elements_by_css_selector('.stock-number') # stock #\n        details_links = driver.find_elements_by_css_selector('.view-details [href]')\n                \n        for index, car in enumerate(car_desc):\n            car_make = (car.text).split()\n            year = car_make[0].split() # convert year to a list\n            make = car_make[1].split() # convert make to a list\n            model = car_make[2]\n            car_trim_temp = car_trim[index].text\n             \n            if \",\" in car_trim_temp:\n                car_trim_temp2 = car_trim_temp[0: car_trim_temp.index(\",\")] # remove commas in the car trim, since we don't care about what comes after the commas\n                model = model, \" \" , car_trim_temp2\n            else:\n                    model = model, \" \" , car_trim_temp\n                    \n            model = ''.join(model) # convert to a string\n            model = re.sub(' +', ' ',  model ) #remove extra spaces\n            model = [''.join(model)] # merge the model into one list element\n            car_details = year + make + model\n            price = re.sub(\"[^0-9]\", \"\", prices[index].text) #remove text and keep the numeric part\n            if len(price) == 0: # if the price is \"Call for price\" or something non-numeric, set the price to 0\n                price = '0'\n                zero += 1\n            price = price.split() # convert to a list\n            stock_num = (stock[index].text[8:]).split() # get the stock # for each car and convert to a list\n            link = (details_links[index].get_attribute('href')).split()\n            print (index,\":\", car_details, price, stock_num, link)\n            car_info.append(dealer_id + car_details + price + stock_num + link)\n            count +=1\n        print (\"Running count: \", count)\n            \n        try:\n            driver.find_element_by_css_selector('.right-arrow-svg').click() # click on Right arrow to get to the next page unless we're at the last page, then it won't be there\n            wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \".page-main-h1\"))) # wait for USED VEHICLES to appear\n            sleep (2)\n        except:\n            pages_remaining = False\n       \n    print (\"Total cars processed: \", count, \" Total unpriced cars: \", zero)\n    car_info = sorted(car_info)\n    for index, i in enumerate(car_info):\n        print (index, \":\", i)\n        \n    print (\"Saving data in a spreadsheet....\", file_out)\n    CreateDealerSheet(data_out, car_info, date_time)\n    print (dealer, \"Total cars: \" , count)\n    data_out.save_file(file_out)\n       \n    driver.quit() # Close the browser and end the session\n","sub_path":"CarData/src/Cars/Ford/Car_data_YorkdaleFord.py","file_name":"Car_data_YorkdaleFord.py","file_ext":"py","file_size_in_byte":4737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"304718400","text":"from flask import current_app, request\nfrom flask_jwt_extended import jwt_required\nfrom flask_restful import fields, marshal, reqparse\n\nfrom api.models.bio_hazard_level import BioHazardLevel\nfrom api.models.database import BaseModel\nfrom api.resources.base_resource import BaseResource\nfrom api.resources.decorators.user_role_decorators import is_theme_admin, is_lab_admin\nfrom api.utils import format_and_lower_str, has_required_request_params, log_update, log_delete, \\\n    standard_non_empty_string, log_create, log_304, get_query_params\n\n\nclass BioHazardLevelResource(BaseResource):\n    fields = {\n        'code': fields.String,\n        'name': fields.String,\n        'description': fields.String\n    }\n\n    def get(self):\n        BioHazardLevel.reindex()\n        query_strings = get_query_params()\n        if query_strings is not None:\n            for query_string in query_strings:\n                query, total = BioHazardLevel.search(query_string, 1, 15)\n                biohazard_levels = query.all()\n\n                data = marshal(biohazard_levels, self.fields)\n                return BaseResource.send_json_message(data, 200)\n\n        elif request.headers.get('code') is not None:\n            code = format_and_lower_str(request.headers['code'])\n            bio_hazard_level = BioHazardLevelResource.get_bio_hazard_level(code)\n            if bio_hazard_level is None:\n                return BaseResource.send_json_message(\"Bio Hazard level not found\", 404)\n            data = marshal(bio_hazard_level, self.fields)\n            return BaseResource.send_json_message(data, 200)\n\n        else:\n            bio_hazard_level = BioHazardLevel.query.all()\n            if bio_hazard_level is None:\n                return BaseResource.send_json_message(\"Bio Hazard levels not found\", 404)\n            data = marshal(bio_hazard_level, self.fields)\n            return BaseResource.send_json_message(data, 200)\n\n    @jwt_required\n    @is_lab_admin\n    def post(self):\n        args = BioHazardLevelResource.bio_hazard_level_parser()\n        code = args['code']\n        name = args['name']\n        description = args['description']\n\n        if not BioHazardLevel.bio_hazard_level_exists(code):\n            try:\n                bio_hazard_level = BioHazardLevel(code=code, name=name, description=description)\n                BaseModel.db.session.add(bio_hazard_level)\n                BaseModel.db.session.commit()\n                log_create(bio_hazard_level)\n                return BaseResource.send_json_message(\"Bio Hazard level successfully created\", 201)\n\n            except Exception as e:\n                current_app.logger.error(e)\n                BaseModel.db.session.rollback()\n                return BaseResource.send_json_message(\"Error while adding bio hazard level\", 500)\n        current_app.logger.error(\"Error while adding bio hazard level :> Duplicate records\")\n        return BaseResource.send_json_message(\"Bio Hazard level already exists\", 409)\n\n    @jwt_required\n    @is_lab_admin\n    @has_required_request_params\n    def put(self):\n        code = format_and_lower_str(request.headers['code'])\n        bio_hazard_level = BioHazardLevelResource.get_bio_hazard_level(code)\n\n        if bio_hazard_level is None:\n            current_app.logger.error(\"Error while updating bio hazard level. Record does not exist.\")\n            return BaseResource.send_json_message(\"Bio Hazard level not found\", 404)\n\n        else:\n            args = BioHazardLevelResource.bio_hazard_level_parser()\n            code = args['code']\n            name = args['name']\n            description = args['description']\n\n            if code != bio_hazard_level.code or name != bio_hazard_level.name or \\\n                    code != bio_hazard_level.code or description != bio_hazard_level.description:\n                old_info = str(bio_hazard_level)\n                try:\n                    bio_hazard_level.name = name\n                    bio_hazard_level.code = code\n                    bio_hazard_level.description = description\n\n                    BaseModel.db.session.commit()\n                    log_update(old_info, bio_hazard_level)\n                    return BaseResource.send_json_message(\"Bio Hazard level successfully updated\", 202)\n                except Exception as e:\n                    current_app.logger.error(e)\n                    BaseModel.db.session.rollback()\n                    return BaseResource.send_json_message(\"Error while updating bio hazard level\", 500)\n            log_304(bio_hazard_level)\n            return BaseResource.send_json_message(\"No changes made\", 304)\n\n    @jwt_required\n    @is_lab_admin\n    @has_required_request_params\n    def delete(self):\n        code = format_and_lower_str(request.headers['code'])\n        bio_hazard_level = BioHazardLevelResource.get_bio_hazard_level(code)\n\n        if bio_hazard_level is None:\n            return BaseResource.send_json_message(\"Bio Hazard level not found\", 404)\n        BaseModel.db.session.delete(bio_hazard_level)\n        BaseModel.db.session.commit()\n        log_delete(bio_hazard_level)\n        return BaseResource.send_json_message(\"Bio Hazard level deleted\", 200)\n\n    @staticmethod\n    def bio_hazard_level_parser():\n        parser = reqparse.RequestParser()\n        parser.add_argument('code', required=True, type=standard_non_empty_string)\n        parser.add_argument('name', required=True)\n        parser.add_argument('description')\n\n        args = parser.parse_args()\n        return args\n\n    @staticmethod\n    def get_bio_hazard_level(code):\n        return BaseModel.db.session.query(BioHazardLevel).filter_by(code=code).first()\n","sub_path":"api/resources/bio_hazard_level_resource.py","file_name":"bio_hazard_level_resource.py","file_ext":"py","file_size_in_byte":5619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"611671876","text":"n = int(input())\n\nfield1 = []\nfield2 = []\n\nmax_r = 0\nmax_l = 0\n\nfor i in range(n):\n    s = list(map(int, list(input().rstrip().split())))\n    if(max_r < s[0]):\n        max_r = s[0]\n    if(s[1] < max_l):\n        max_l = s[1]\n    field1.append(s)\n    field2.append(s)\n\nif(max_r >= -max_l):  # 最初の進行方向右\n    target = 0\n    field1.sort(key=lambda x: -x[0])\n    field2.sort(key=lambda x: x[1])\nelse:\n    target = 1\n    field1.sort(key=lambda x: x[1])\n    field2.sort(key=lambda x: -x[0])\n\nmove = 0\nnow = 0\nfield = 1\nm = int(n / 2)\n\nfor i in range(m + 1):\n    if(field1[i][0] <= now and now <= field1[i][1]):\n        break\n    last = field1[i][target]\n    if(now > 0 and last > 0)or(now < 0 and last < 0):\n        move += abs(now - last)\n    else:\n        move += abs(now) + abs(last)\n    now = last\n    if(i == m):\n        break\n    else:\n        if(field2[i][0] <= now and now <= field2[i][1]):\n            break\n        last = field2[i][1 - target]\n    if(now > 0 and last > 0)or(now < 0 and last < 0):\n        move += abs(now - last)\n    else:\n        move += abs(now) + abs(last)\n    now = last\n\nmove += abs(now)\n\nprint(move)\n","sub_path":"agc/agc025/c_2.py","file_name":"c_2.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"194382958","text":"from django.conf import settings\nfrom django.contrib.sessions.middleware import SessionMiddleware\nfrom django.test import RequestFactory\n\nimport pytest\nfrom dj_rest_auth.utils import jwt_encode\nfrom rest_framework.test import APIClient\n\nfrom test.factories import UserFactory\n\npytest_plugins = [\"test.schema\", \"test.factories\"]\npytestmark = pytest.mark.django_db\n\nUser = settings.AUTH_USER_MODEL\n\n\n@pytest.fixture\ndef user() -> User:\n    user = UserFactory(\n        username=\"test-user\",\n        email=\"test-user@test.com\",\n        password=\"1234\",\n    )\n    return user\n\n\n@pytest.fixture\ndef client(user):\n    access, __ = jwt_encode(user)  # access, refresh\n    headers = {\"HTTP_AUTHORIZATION\": f\"Bearer {access}\"}\n    client = APIClient()\n    client.credentials(**headers)\n    return client\n\n\n@pytest.fixture\ndef no_auth_client():\n    client = APIClient()\n    return client\n\n\n@pytest.fixture\ndef fake_request(rf: RequestFactory, user):\n    # build \"request\" for the test: needed to populate HiddenField(default=serializers.CreateUserDefault())\n    request = rf.get(\"/fake-url/\")\n    # Django Test Case Error 'WSGIRequest' object has no attribute 'session'\n    # https://stackoverflow.com/questions/35659676/django-test-case-error-wsgirequest-object-has-no-attribute-session\n    middleware = SessionMiddleware()\n    middleware.process_request(request)\n    request.session.save()\n    request.user = user\n    return request\n","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"441285049","text":"from django.contrib.auth.models import User\nfrom django.core.mail import send_mail\nfrom django.template import loader\n\nfrom distributor_node import DistributorNode\nfrom distributor_node_list import DistributorNodeList\nfrom ...models import DistributedNode\n\nEMAIL_SUBJECT = 'You\\'ve been assigned a donee'\nEMAIL_TEMPLATE = 'distribute/email.html'\nEMAIL_SENDER = 'from@example.com'\n\n\nclass Distributor:\n\n    def __init__(self):\n        self.distributed_users = None\n\n    def process(self):\n        nodes = self.get_undistributed_users()\n\n        if len(nodes) > 1:\n            distributed_nodes = self.distribute_users(nodes)\n            for node in distributed_nodes:\n                self.send_email(node)\n                node.save_node()\n            return True\n        else:\n            return False\n\n    @staticmethod\n    def get_undistributed_users():\n        users = User.objects.filter(is_superuser__exact=0).filter(santa__isnull=True)\n        nodes = []\n\n        for user in users:\n            nodes.append(DistributorNode(user))\n\n        return nodes\n\n    @staticmethod\n    def distribute_users(nodes):\n        node_list = DistributorNodeList(nodes)\n        current_node = node_list.get_random_element()\n        number_of_connections = 0\n\n        while True:\n            def condition(n): return n.gifted is None\n\n            if number_of_connections + 2 == len(nodes):\n                def condition(n): return n.gifted is None and n.santa is None\n\n            next_node = node_list.get_random_or_next_element(\n                condition,\n                not_equal=current_node\n            )\n\n            current_node.santa = next_node\n            next_node.gifted = current_node\n            number_of_connections += 1\n\n            if number_of_connections == len(nodes):\n                break\n\n            current_node = next_node if next_node.santa is None else node_list.get_random_or_next_element(\n                lambda n: n.santa is None\n            )\n\n        return node_list.get_array()\n\n    @staticmethod\n    def reset():\n        DistributedNode.objects.all().delete()\n        return True\n\n    @staticmethod\n    def send_email(node):\n        content = loader.render_to_string(\n            EMAIL_TEMPLATE,\n            {'username': node.user.username, 'recipient': node.gifted.user.username})\n\n        send_mail(\n            EMAIL_SUBJECT,\n            content,\n            EMAIL_SENDER,\n            [node.user.email],\n            fail_silently=True)\n","sub_path":"test_project/secretsanta/classes/distributor/distributor.py","file_name":"distributor.py","file_ext":"py","file_size_in_byte":2464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"636541939","text":"from django.conf import settings\nfrom django import forms\nfrom django.utils.translation import ugettext as _\nfrom taggit.utils import parse_tags, edit_string_for_tags\n\n\nclass TagWidget(forms.TextInput):\n    def render(self, name, value, attrs=None):\n        if value is not None and not isinstance(value, basestring):\n            value = edit_string_for_tags([o.tag for o in value.select_related(\"tag\")])\n        return super(TagWidget, self).render(name, value, attrs)\n\nclass TagField(forms.CharField):\n    widget = TagWidget\n\n    def clean(self, value):\n        value = super(TagField, self).clean(value)\n        try:\n            parsed_values =  parse_tags(value)\n            TAGGIT_ALLOWED_TAGS =  getattr(settings, \"TAGGIT_ALLOWED_TAGS\", None)\n            if TAGGIT_ALLOWED_TAGS:\n                LOWER_TAGS = [x.lower() for x in TAGGIT_ALLOWED_TAGS]\n                for value in parsed_values:\n                    if value.lower() not in LOWER_TAGS:\n                        raise forms.ValidationError('Tag %s not valid (should be one of: %s)' %(value, \",\".join(TAGGIT_ALLOWED_TAGS)))\n            TAGGIT_FORCE_LOWERCASE = getattr(settings, 'TAGGIT_FORCE_LOWERCASE', False)\n            if TAGGIT_FORCE_LOWERCASE:\n                parsed_values = [x.lower() for x in parsed_values]\n            return parsed_values\n        except ValueError:\n            raise forms.ValidationError(_(\"Please provide a comma-separated list of tags.\"))\n","sub_path":"taggit/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}
+{"seq_id":"308066025","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2017/8/31 9:19\n# @Author  : Aries\n# @Site    : \n# @File    : 线程池.py\n# @Software: PyCharm\nfrom concurrent.futures import ProcessPoolExecutor,ThreadPoolExecutor\nimport threading,os,time,random\ndef task(n):\n    print(\"%s:%s is running\"%(threading.current_thread().getName(),os.getpid()))\n    time.sleep(2)\n    return n**2\n\nif __name__ == '__main__':\n    p=ThreadPoolExecutor()\n    l=[]\n    start=time.time()\n    for i in range(10):\n        obj=p.submit(task,i)\n        l.append(obj)\n    p.shutdown()\n    print(\"=\"*30)\n    print([obj.result() for obj in l])\n    print(time.time()-start)","sub_path":"python/练习/线程池.py","file_name":"线程池.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"68"}